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The Vertebrate Retina, Photoreceptors, and Color Vision

Part 1: Photoreceptors and Image Processing I

00:00:05.22 I'm Jeremy Nathans, I'm a professor at the John Hopkins Medical School,
00:00:09.22 an investigator of the Howard Hughes Medical Institute.
00:00:12.22 This is the first of three lectures on the vertebrate retina,
00:00:15.20 its structure, function, and evolution.
00:00:19.01 In this first part, we're going to look at photoreceptors and image processing.
00:00:22.27 Let's start with a look at the visual system in its entirety.
00:00:26.14 It consists, of course, of the eye and a significant fraction of the brain,
00:00:32.15 as indicated by these arrows.
00:00:34.11 That's really too much for three lectures, and so we're going to concentrate
00:00:37.29 just on the eye and the inner workings of the retina within it.
00:00:41.16 Let's have a closer look at the vertebrate eye.
00:00:44.10 Light enters the vertebrate eye by refraction at the cornea-air interface,
00:00:50.11 and again by the lens, and then the image is formed on the back wall of the eye
00:00:54.11 lined by a thin layer of neural tissue, called the retina.
00:00:57.24 Now, a comparison between the eye and a camera is irresistible.
00:01:03.01 You can see that many of the components are the same.
00:01:05.22 They both have an aperture, they have a lens, the image is formed on the back wall
00:01:11.10 of both the camera at the film, and on the back wall of the eye, at the retina,
00:01:17.16 It's inverted, of course, by the action of refraction at the cornea and the lens,
00:01:24.01 but there's a critical difference and that is that the retina
00:01:27.21 doesn't just capture the image, the way the film does.
00:01:29.29 It processes it and it extracts from it those aspects of the visual world
00:01:35.22 that are most important to the organism.
00:01:37.13 Now, this remarkable piece of engineering has captured
00:01:43.18 the imagination of scientists for centuries.
00:01:45.29 The performance of the retina is really quite remarkable.
00:01:49.01 For example, the retina performs...the whole visual system performs
00:01:53.01 extremely well on a moonless night and also extremely well on a sunny afternoon
00:01:59.04 when the ambient light levels differ by a million fold.
00:02:02.08 The retina is also capable of discriminating objects that differ
00:02:06.12 in their angular position by only one percent of one degree...
00:02:10.29 an amazing performance.
00:02:13.08 In fact, that performance gave Charles Darwin a bit of a nightmare.
00:02:18.21 He had written in his Origin of Species a critical chapter
00:02:24.23 which he called "Problems with the Theory"
00:02:27.01 in which he examined what he called organs of extreme perfection.
00:02:31.23 And by this he meant organs which had achieved such remarkable abilities
00:02:37.06 that they seemed to fly in the face of his simple idea of evolution by stepwise change.
00:02:43.13 The star witness for this collection of organs of extreme perfection was the eye.
00:02:49.26 At the end, Darwin concluded that perhaps the eye could have evolved stepwise,
00:02:54.15 but I think it gave him a bit of a headache.
00:02:56.28 Now, we're going to talk mostly about the vertebrate eye,
00:03:00.14 but I want, just for completeness to note
00:03:02.06 this is not the only structure that evolution has come up with
00:03:05.27 to capture light and to analyze it.
00:03:09.06 Insects have an anatomically quite different arrangement.
00:03:12.26 Here's a Drosophila. It has a characteristic pair of eyes on either size of the head,
00:03:20.26 but they're really a collection of hundreds of little eyes called ommatidia.
00:03:26.07 And that anatomic difference, for many years, was thought to reflect
00:03:32.11 a really rather different evolutionary pathway to the production of an eye.
00:03:39.22 But, interestingly, in recent years, it's become apparent
00:03:43.11 that a number of the molecular players are similar...
00:03:46.21 virtually identical between insects and vertebrates,
00:03:50.27 although the overall anatomy is quite different.
00:03:53.12 So, for example, the light-sensing proteins
00:03:55.15 which we'll talk about in detail in a few minutes
00:03:58.05 are nearly identical between insects and vertebrates.
00:04:01.26 And, perhaps more surprising, the genes that control
00:04:06.15 the development of the vertebrate eye... one in particular called Pax6,
00:04:10.12 a master regulator gene of vertebrate eye development,
00:04:13.11 and eyeless (and its close cousin, twin of eyeless) in insects
00:04:18.24 function in largely the same ways, and these genes have dramatic effects
00:04:23.17 on the controlled development of the eye.
00:04:27.03 Let me just show you one example of how dramatic that effect is.
00:04:31.04 This is an experiment in which either eyeless or the twin of eyeless gene
00:04:36.20 has been overexpressed in incorrect locations within a fruit fly.
00:04:41.21 On the left, here, we have its overexpression on the legs,
00:04:46.09 and here this red patch on both legs
00:04:50.13 left and right, is an eye which has developed on the leg,
00:04:53.29 simply because that gene was overexpressed.
00:04:56.09 On the right, we see a fly, where, on the antenna, where there shouldn't be an eye,
00:05:00.13 now an eye has appeared.
00:05:02.16 Now, we're not going to say anything more about invertebrate vision.
00:05:07.09 At this point, we'll return to the vertebrate retina
00:05:09.29 which is really the object of our lectures today
00:05:12.20 Let's just look at its overall structure.
00:05:16.11 It's a layered structure, it's rather thin --
00:05:19.07 substantially less than a millimeter in thickness,
00:05:21.28 and in the outermost layer are the photoreceptor cells, the rods and cones.
00:05:27.07 These are up here.
00:05:28.22 The rods are thin cells... these vertical cells... the more numerous cells,
00:05:34.01 and the cones are the thicker ones.
00:05:35.21 And then there's a second layer of intervening neurons:
00:05:39.25 the bipolar, horizontal, and amacrine cells,
00:05:41.16 and finally, at the bottom, there's an output layer of cells,
00:05:46.13 which send their axons to the brain. These are the retinal ganglion cells,
00:05:49.28 the final arbiters of information that will flow to the brain.
00:05:54.22 Let's have a slightly closer look at the photoreceptor cells.
00:05:58.27 Here's a scanning electron micrograph showing
00:06:01.19 the rods and cones in a salamander retina,
00:06:03.21 and you can see that they're quite unusual looking cells.
00:06:07.13 The cones are the little ones with these cone-shaped tips,
00:06:11.03 and the rods are the big ones with these barrel, so-called rod-shaped outer parts.
00:06:17.05 Each cell has one of these specialized appendages, and they're called outer segments,
00:06:23.11 which really are modified cilia and hold all of the light-sensitive machinery
00:06:29.26 and the enzymes that transduce the light information following capture.
00:06:35.07 Let's look again, at even higher power at the internal structure
00:06:39.19 of one of these outer segments.
00:06:41.27 Here is a transmission electron micrograph.
00:06:45.07 What we see is that the outer segment is filled... just chock full of membranes
00:06:50.25 and they're layered like stacks of pancakes.
00:06:53.18 There's about a thousand of these flattened membrane sacks
00:06:58.25 within a single outer segment.
00:07:01.19 Why is it built this way? Why are all of these membranes present,
00:07:05.14 and why are they stacked in this configuration?
00:07:09.23 The answer is that the light-sensing protein, generically called visual pigment,
00:07:15.24 (rhodopsin, in the case of rods - the specific name)
00:07:18.23 is an integral membrane protein, and it sits in this membrane.
00:07:24.06 In fact, the membrane is virtually a liquid crystal of the visual pigment.
00:07:29.02 Here is the 3D structure of the pigment... this is bovine rhodopsin.
00:07:33.29 You can see it has seven transmembrane domains.
00:07:36.17 Those are these cylindrical objects, passing through the membrane,
00:07:42.27 which is in the horizontal plane here.
00:07:44.29 It is a G-protein-coupled receptor. All visual pigments are G-protein-coupled receptors,
00:07:49.14 and they act by catalyzing the activation of a G-protein which then
00:07:55.26 transmits the signal further within the cell. We'll discuss that in a minute.
00:08:00.01 So, one can ask, though, what part of the protein actually absorbs light?
00:08:05.18 Now, protein side chains in general do not absorb visible light.
00:08:09.19 They absorb in some cases in the ultraviolet.
00:08:11.21 The thing that absorbs visual light, which is shown here in red
00:08:15.28 is the chromophore, 11-cis-retinal. It's a different compound
00:08:19.20 that's joined covalently to the receptor protein.
00:08:23.10 And this is what it looks like in schematic form.
00:08:25.27 It's a vitamin A derivative. We don't make it ourselves. We have to eat it.
00:08:30.10 It comes from a variety of plant sources... carrots are a good source.
00:08:34.25 It's bound covalently to the terminal amino group of a lysine that is part of the apo protein.
00:08:43.01 Now, light does only one thing in all of vision.
00:08:48.03 It isomerizes retinal from cis to trans.
00:08:51.27 That is, in the dark state, in the unexcited state, there's one double bond
00:08:56.07 in the cis configuration... this 11-12 double bond.
00:08:59.24 And photoisomerization, as shown by this transition from the upper to the lower
00:09:05.29 structure converts that cis bond to a trans bond.
00:09:10.08 That's it. Light does nothing else in all of vision.
00:09:13.00 And that happens in about 10 picoseconds from the time of photo-capture.
00:09:18.13 Everything else that happens, in terms of receiving and amplifying the signal,
00:09:23.15 happens as a consequence of this isomerization.
00:09:26.26 And what does this do to the protein?
00:09:29.12 Well, if you think like a pharmacologist, you can conceptualize the dark form...
00:09:36.08 the 11-cis form of retinal as an antagonist that sits in the binding pocket
00:09:42.14 and it keeps the protein in the off configuration... keeps it silent.
00:09:46.18 And this isomerization, which converts the 11-cis to all-trans form of the chromophore
00:09:53.24 then activates a series of conformational changes in the attached apo protein
00:09:58.25 and turns it into the on state.
00:10:01.22 Now there's one new player in the photo-sensing world that has just emerged in the last decade,
00:10:09.24 and that is yet an additional light-sensing pigment that is in the vertebrate eye
00:10:17.13 but not in the rods or cones. This came as quite a surprise.
00:10:20.22 This is a pigment which we call melanopsin.
00:10:25.16 It resides in intrinsically photosensitive retinal ganglion cells...
00:10:29.14 that third and output layer of the retina where one might not
00:10:33.28 have expected direct photosensitivity.
00:10:36.14 But, it turns out that a tiny subset of those cells have a response
00:10:40.22 that's shown as exemplified here by this very slow time course of response,
00:10:46.22 and down below we see the pulse of light (the square wave is the pulse of light)...
00:10:50.22 And this very slow response is typical for these intrinsically photoreceptive ganglion cells.
00:10:56.21 Now that's way too slow a response to be useful for image-forming vision
00:11:01.20 of the sort that allows us to play tennis or walk around.
00:11:04.29 It is, however, just the sort that is useful for rather slower responses.
00:11:11.02 For example, constriction of the pupil. When the ambient light increases or decreases,
00:11:16.15 your pupil constricts or dilates accordingly.
00:11:18.24 Or, for example, in training neural rhythms to the light and dark variation
00:11:26.08 that occurs over 24 hours... the so-called circadian rhythm.
00:11:29.29 And, in fact, these intrinsically photoreceptive ganglion cells mediate exactly those two functions.
00:11:36.09 Now let's ask a question which has been asked by many vision scientists for a very long time.
00:11:44.03 And that is, what is the absolute threshold of human vision?
00:11:47.25 That is, what is the dimmest light that we can see?
00:11:51.11 This is a question which has been of interest, not only to vision scientists,
00:11:55.07 but, for example, to astronomers for a long time.
00:11:58.03 In the days before electronic detectors and film, an astronomer like Keppler or Galileo or Newton
00:12:05.18 would look through the telescope with his naked eye,
00:12:08.21 and so the question actually arose, How good is the eye?
00:12:13.11 Are there, for example, stars out there that are so dim that we can't see them
00:12:17.23 because the eye isn't sufficiently sensitive?
00:12:19.21 Of course, we know the answer is yes, there are stars that are far dimmer
00:12:23.13 than can be seen with the naked eye.
00:12:25.19 And so, in addressing this question, you might think that it's not that difficult experimentally
00:12:33.24 to obtain an answer. We could simply start with a calibrated light source.
00:12:38.18 We know how many photons it's delivering per unit time.
00:12:41.24 It delivers some stimulus to the eye, and we'll just simply calculate the number of photons.
00:12:48.04 To first approximation, that is the way the experiment is done,
00:12:51.24 and if you do it with all due preparations...
00:12:58.02 That is, you have the subject fully dark-adapted
00:13:00.02 (the person sits in a completely pitch black room for half an hour or so)
00:13:04.14 Then if you deliver a very brief and very tiny flash of light, that's the best stimulus.
00:13:12.02 And you deliver it to the most rod-rich region of the retina,
00:13:15.17 because the rods are more sensitive to dim light than are cones...
00:13:19.02 You can get a calibrated answer, and the answer turns out to be
00:13:24.25 a rather small number of photons.
00:13:26.28 It's about 100 that must be delivered to the cornea
00:13:29.22 for the person to say, "Yes, I saw that flash of light."
00:13:33.07 Now that's an impressively small number... it's an impressive answer.
00:13:39.21 But it doesn't get fully at the question because what we'd really like to know
00:13:43.29 is how many photons must be captured by the photoreceptor cells
00:13:50.25 to initiate the visual act. Not how many hit the eye from the outside world
00:13:56.07 because many of those might not be used at all.
00:13:58.17 They may be thrown away. They may, for instance, pass through the retina unabsorbed.
00:14:01.20 And, in fact, that is the case. The eye is not 100% efficient
00:14:06.10 in its ability to capture and utilize light information.
00:14:10.05 And so, in the quest to figure out exactly how many photons need to be absorbed
00:14:18.07 to initiate a visual act, Selig Hecht, one of the great visual scientists,
00:14:22.26 thought of an extremely clever approach to this question
00:14:28.05 which would use the statistical variation in the number of photons delivered to the eye
00:14:35.13 in one of those flashes from trial to trial
00:14:38.14 as a way of determining that number of photons that are required to see.
00:14:44.18 Let's just follow this experiment.
00:14:47.05 So, imagine I'm going to give you two numerical examples which will illustrate the method.
00:14:51.22 Imagine that we're delivering, in the upper example here,
00:14:57.26 a certain number of photons per flash, and we'll say that the average number is 3.
00:15:04.28 So, every time a shutter opens, on average, 3 photons strike the eye
00:15:11.02 as a result of that 1 millisecond flash.
00:15:14.23 And let's also imagine, for the sake of this argument, that 5 or more photons
00:15:18.22 are required to allow the person to actually see the flash of light.
00:15:24.14 That is to say, 5/3 as many as are delivered on average per flash.
00:15:29.05 Now, if one is delivering on average 3 photons per flash...
00:15:32.26 And recall that, photons emerge from a filament of a light bulb independently and at random,
00:15:38.22 so, this number will vary in a Poisson manner (that is, a random manner).
00:15:43.25 And so, the variation in number of photons delivered on different stimulus occasions
00:15:50.20 will have this bell-shaped curve, as shown here,
00:15:54.27 which in some instances deliver the average, 3,
00:15:58.29 some deliver more (say 4 or 5), some deliver fewer (2 or 1).
00:16:02.22 And because of this variation, it will only be the occasional, let's say,
00:16:07.22 1 in 20 flashes which happens to deliver 5 or more photons per flash.
00:16:13.18 So, the individual will say perhaps 1 out of 20 times, "Yes, I saw something."
00:16:17.28 Now, let's look at this lower example.
00:16:21.20 Suppose the experimental setup is essentially the same,
00:16:24.26 but the only difference is that one requires 50 or more photons to see the flash.
00:16:30.08 And that on average, 30 are delivered.
00:16:32.24 How will that change the outcome of the experiment?
00:16:36.00 It will change it in the following manner...
00:16:38.03 Because photons emerge from the filament at random,
00:16:41.28 and because that random process has this sort of bell-shaped curve,
00:16:48.11 if the number is larger, that curve will necessarily be narrower...
00:16:54.07 That is, the relative fluctuation about the mean for larger numbers of events
00:17:01.24 will be less than it will for smaller numbers.
00:17:05.03 So, even though the number of photons required
00:17:08.04 for the person to say, "Yes, I saw something"
00:17:10.21 is still 5/3 the average number that were delivered,
00:17:15.24 the fraction of the time that large number is delivered to the eye...
00:17:22.12 that is, 50 or more photons delivered to the eye
00:17:24.23 will be far less. It might be 1% or less than 1% of the time
00:17:30.21 And it's that difference which then Hecht set out to measure.
00:17:35.06 Now let's look at two related curves.
00:17:38.20 These show the fraction of trials in which the subject reported seeing a flash of light.
00:17:45.05 as a function of the average number of photons captured per trial.
00:17:49.16 And I think we can see in this pair of curves (and these really are
00:17:54.00 the experimental curves that would come from doing the experiment that Hecht did)
00:17:58.24 that if a small number is required, say 5 or more,
00:18:04.12 that curve is a rather shallow curve. It's a broad S-shaped curve because
00:18:09.14 if we deliver somewhat fewer than five, or somewhat more than five,
00:18:16.05 there will be a gradual increase in the probability that 5 or more photons are acquired
00:18:21.01 because the spread in the number of photons that are issued per trial
00:18:26.14 has this rather large variation.
00:18:29.00 On the other hand, if a larger number, say 50 or more photons is required per trial,
00:18:35.03 the curve is somewhat steeper.
00:18:38.12 So, I think you can see that there's a sharper cutoff, where below the average of 50,
00:18:44.15 it's rather unlikely that a sufficient number will be delivered to the retina
00:18:49.22 and above 50, say 60 or 70 or 80, it's very, very likely
00:18:54.20 that above that number will be delivered to the retina...
00:18:57.29 Hence, the steepness of the curve.
00:18:59.22 And therefore, based simply on how shallow or steep the response curve is,
00:19:06.06 Hecht was able to determine how many photons are required to see a flash of light.
00:19:12.25 The answer, remarkably is only 5-7 photons, a very small number.
00:19:18.23 So, even though 100 photons strike the cornea, only 5-7 of them
00:19:24.23 are actually used to initiate the visual sense at absolute threshold.
00:19:29.22 5, 6, or 7 photons corresponds to an extremely small amount of energy.
00:19:36.02 Let's get a sense of how small that energy is, and we'll compare it to the energy
00:19:41.02 that's lost by dropping a dime 1mm in the Earth's gravitational field.
00:19:46.06 So, I have a dime here, and I'm just going to drop it 1mm onto the palm of my hand,
00:19:50.25 and it makes a little thud.
00:19:52.10 I can certainly feel it, but it's not a big stimulus.
00:19:55.17 And, let's ask, suppose we convert that amount of energy into photons...
00:20:01.23 We can ask how many photons would that give us?
00:20:04.27 That's a very straightforward calculation.
00:20:07.14 Here, we've just calculated the weight of the dime: 10^-3 kg,
00:20:11.29 the acceleration in the Earth's gravitational field: 9.8m/s^2,
00:20:17.17 and the distance dropped: 10^-3 meters,
00:20:21.20 The product of those gives us 9.8*10^-6 joules.
00:20:25.22 That's how much energy was lost when we dropped the dime 1 mm,
00:20:29.19 and that, of course, was the energy that hit the palm of my hand.
00:20:34.00 Now, let's figure out the amount of energy per 500nm photon.
00:20:38.27 This would be a wavelength right in the middle of the visual spectrum.
00:20:43.17 And, of course, energy for photons = h * nu.
00:20:48.01 h is Planck's constant, nu, the frequency is the speed of light over wavelength.
00:20:53.20 We can plug in the numbers as shown down here.
00:20:56.04 We end up with 4*10^-19 joules per photon.
00:21:02.17 Now, comparing this number to the number that we calculated for dropping that dime 1 mm,
00:21:08.20 we see that the energy equivalent to dropping a dime 1 mm in the Earth's gravitational field
00:21:14.28 corresponds to 2*10^13 photons.
00:21:20.11 That is, that energy, if divided into flashes of light and delivered to every person on the planet,
00:21:27.29 would give a very bright and easily detectible flash.
00:21:31.25 That means that the visual system is extremely sensitive at absolute threshold.
00:21:37.24 A remarkable property.
00:21:40.05 Now let's ask if it's possible to look at the single-cell level
00:21:45.00 and see the single-cell correlate of this incredible sensitivity.
00:21:49.04 At this point, I should mention that the flash of light that's used
00:21:53.22 for an experiment of the sort that Hecht did
00:21:55.23 lands on the retina over an area that encompasses roughly 500 photoreceptor cells.
00:22:01.15 Why 500? Because that's the region that is summed
00:22:05.20 by a single ganglion cell and affects its output.
00:22:09.03 If it's spread over a much larger region of retina,
00:22:12.08 the sensitivity will go down because the information will be carried
00:22:16.04 by different channels that are not as effectively integrated.
00:22:20.14 But, if we look at a region of just 500 photoreceptor cells,
00:22:25.09 and we deliver the flash of light to just that region,
00:22:28.20 then those signals will impinge on a single ganglion cell
00:22:32.02 or a small number of ganglion cells and the signal will be optimally delivered to the brain.
00:22:36.25 Now, the fact that we can see as few as 5-7 photons reliably,
00:22:44.12 over an area that is encompassed by 500 photoreceptors must mean that,
00:22:49.24 in general, a single photoreceptor is responding to a single photon.
00:22:55.08 Of course, most of the photoreceptors are capturing no photons.
00:22:57.15 The occasional photoreceptor is capturing only one, and very rarely
00:23:02.28 -- hardly ever -- is a photoreceptor capturing more than one in that circumstance.
00:23:06.18 So, this would predict that photoreceptors can, in fact,
00:23:10.20 capture and amplify the signal from a single photon.
00:23:14.29 The predicted single photon response has, in fact, been measured in photoreceptor cells,
00:23:21.24 and it's done by recording the current flowing into a single rod outer segment
00:23:27.15 using the recording arrangement shown here.
00:23:30.19 This large object is a glass electrode. This is its tip.
00:23:37.19 It has an opening down at the bottom, and within that opening is a single rod outer segment...
00:23:44.15 (You can just barely see it)
00:23:46.01 ...drawn up from this little chunk of retina,
00:23:48.18 where there are many other outer segments sticking out.
00:23:51.16 And, this horizontal yellow line is the exciting beam...
00:23:57.14 the beam of light which is going to stimulate that photoreceptor outer segment.
00:24:01.17 Using this apparatus, where the seal between the outer segment
00:24:07.12 and the recording pipette is extremely snug at the bottom allows one
00:24:12.12 to measure the current that flows in the top, into the cell, and then out the bottom.
00:24:18.16 And what one observes with this sort of recording setup
00:24:25.22 is that a response to a brief flash of light,
00:24:29.05 and that's shown here in this middle trace here, this little blip is the light stimulus.
00:24:36.16 The response to that brief flash has a characteristic shape and duration.
00:24:42.21 If the flash is very bright, as we see with the highest of these response curves,
00:24:47.27 the response rises quite rapidly to a final saturating level.
00:24:53.25 It's maintained at that level and then it returns back to baseline.
00:24:58.01 As the light gets dimmer and dimmer and dimmer, as we see in this family of response curves,
00:25:03.09 the response correspondingly goes down.
00:25:05.22 At some point, the response is quite small.
00:25:08.13 And as the light gets ever dimmer, we see that the response is either there or not there.
00:25:16.19 It's quantized.
00:25:17.11 That is the response to single photons, where the capture of a photon
00:25:24.07 occurs only rarely per trial, and when it does occur, it's almost always a single one.
00:25:30.07 Occasionally, there are two, and one can see that as a response
00:25:32.29 that's roughly twice the size of the single photon response.
00:25:37.28 Now if that's all there was to it, you might wonder,
00:25:42.02 well why can't the brain just see a single photon? Why do we need 5-7 photons?
00:25:48.28 And the answer is that this is not the entire story.
00:25:53.11 If you look at the response properties of a rod cell,
00:25:58.21 in complete darkness, what you see is that there are electrical events that resemble...
00:26:04.19 in fact, are indistinguishable from single photon events.
00:26:08.11 So, here's a series of traces. The upper three traces show
00:26:12.28 the responses of a single rod outer segment
00:26:16.16 to complete darkness. So, in theory, there should be no activity whatsoever.
00:26:23.14 But, in fact, you see that there's first a little rumbly background.
00:26:27.13 It's not a completely smooth baseline.
00:26:29.08 But, most strikingly, there are a series of occasional responses - little blips -
00:26:34.24 which in fact resemble in every respect the response to single photons.
00:26:39.12 Now this is not just noise in the recording apparatus.
00:26:42.28 How do we know that?
00:26:44.19 Because in this lowest trace, the same rod has been exposed to very bright light
00:26:49.16 and the very bright light saturates the response, and as you can see, it's quite smooth...
00:26:54.27 relatively smooth. It clearly lacks these individual blips up and down.
00:27:01.12 And therefore, this alone says that the noise that we're seeing comes from the rod,
00:27:06.24 and it comes from the dark-adapted rod, and it's completely suppressed
00:27:11.01 in the presence of saturating light.
00:27:12.29 Now, what's the origin of these little responses that look like single-photon responses?
00:27:18.18 Well these are thermal isomerizations of the visual pigment, rhodopsin.
00:27:25.02 That is, occasionally, the 11-cis-retinal, even in the absence of light
00:27:30.24 will spontaneously isomerize to all-trans.
00:27:33.18 On a per-molecule basis, it only happens about once every 100 years.
00:27:38.01 But, there are a very large number of these molecules per photoreceptor cell,
00:27:42.19 so in a given cell, one of those events happens roughly every minute or so
00:27:48.22 or every half-minute.
00:27:51.05 So that makes the problem from the point of view of the ganglion cell,
00:27:55.02 more complicated. What does this thermal isomerization mean
00:28:00.20 from the point of view of the signal that's being sent from the retina to the brain?
00:28:04.22 Recall that the ganglion cells that are sending this signal
00:28:09.23 are in charge of roughly 500 photoreceptors each.
00:28:12.25 Now that ganglion cell has the task of distinguishing signal - true signal
00:28:18.21 (that is, a light-induced response) from the thermal noise.
00:28:23.16 Now, we saw that the thermal noise consists of spontaneous events
00:28:27.21 at a frequency of roughly 1 or 2 per minute per photoreceptor cell.
00:28:33.01 And so the ganglion cell is being bombarded with perhaps 5 or 10
00:28:37.27 of these false signals every second.
00:28:40.18 I think you can see immediately why a true signal consisting only of a single photon
00:28:46.01 absorbed could never be seen over that background.
00:28:48.21 What the ganglion cell is looking for, then, is a small number or more
00:28:55.28 of responses over a very small time interval.
00:29:00.01 Say, ten or so milliseconds, that would be a signal that rises above that noise.
00:29:06.24 So, if we see, for example, in this central panel, here just drawn schematically,
00:29:12.12 what the ganglion cell input looks like.
00:29:15.04 It's this noisy signal coming from these many photoreceptor cells.
00:29:20.03 But, if there's a flash of light as shown in this upper trace here
00:29:24.11 at one moment in time, which induces a series of photoreceptor responses
00:29:30.04 which bring that input to the ganglion cell above the noise
00:29:34.03 by some criterion, then the ganglion cell will respond by firing action potentials
00:29:40.19 (a couple of action potentials are shown here)
00:29:42.26 which, upon receipt in the brain, may register in consciousness
00:29:47.11 as a signal above noise. Again, at each step, there is a signal to noise issue,
00:29:52.10 because of course, the ganglion cell occasionally
00:29:54.25 fires an action potential when there is no light, because just in response to the random input
00:30:02.03 the threshold has been reached to initiate an action potential.
00:30:06.12 So, really the system is built to be a coincidence detector.
00:30:11.12 And, the five to seven photons are required to bring that signal
00:30:17.03 above the noise to an extent that it is statistically significant.
00:30:21.10 And that's really the very best it can do in the context of photoreceptors
00:30:28.02 that are not completely noiseless.
00:30:29.22 Now let's ask, looking inside the photoreceptor,
00:30:35.16 how it can detect a stimulus as weak as a single photon.
00:30:42.29 This, of course, is by virtue of the quantum nature of light,
00:30:47.15 the smallest possible stimulus that the photoreceptor could detect.
00:30:51.28 And, if we take an engineer's view of signal amplification in sensory receptors
00:30:57.24 we could imagine ways in which the photoreceptor might be built to respond to this signal.
00:31:05.14 And then we can ask, how do those different potential ways
00:31:10.11 measure up in terms of detecting very weak signals?
00:31:13.21 So, on the left here, I've illustrated just the very simplest possible
00:31:18.05 response that one could have,
00:31:19.13 that is the receptor, R, converted to an activated form, R*,
00:31:24.12 which is the entire response. There's nothing more in this instance,
00:31:29.02 and a little flash of light here, shown by this blip,
00:31:31.10 would elicit an essentially immediate response:
00:31:33.10 this step function of receptor being active.
00:31:36.19 Now that is certainly a possible way to build a receptor.
00:31:40.17 But, it doesn't have an amplification built in, in the sense that a single initiating particle,
00:31:48.03 say a photon, doesn't lead to multiple particles within the responding cell.
00:31:53.02 And so, we just have a 1:1 correspondence between input and output.
00:31:57.14 So, that's an unamplified system. It's rapid, but not particularly sensitive.
00:32:02.23 We could make it more sensitive in this central panel, by imagining
00:32:08.05 that the activated receptor, R*, is a catalyst.
00:32:12.20 This is the classic way that biological systems amplify signals,
00:32:16.18 by initiating the formation or unveiling the formation of a catalyst.
00:32:22.01 And that this catalyzes a reaction, indicated here as A being converted to A*.
00:32:28.09 Now that has a virtue of amplification, because for every catalyst which appears--
00:32:37.11 the R* which appears in response to the stimulus--
00:32:41.21 there will be many A to A* conversions.
00:32:46.05 And, I think if we see the time course here, of A* appearing as a function of time
00:32:52.09 (that's this ramp function)
00:32:53.12 after a flash of light, we can appreciate that, although there's a bit of a delay,
00:32:57.07 it takes a while for many of these final activated compounds, A*s, whatever they are,
00:33:04.18 to accumulate, their numbers could accumulate to rather large levels.
00:33:08.28 It could be hundreds, perhaps, or even thousands.
00:33:11.01 And so that would be a way of amplifying the signal on a per-particle basis.
00:33:14.25 Let's just take that logic one step further.
00:33:17.00 Suppose, as shown on the right, A* is itself a catalyst.
00:33:23.16 So, there's an initial catalytic event, converting A to A*,
00:33:26.21 but then A* converts B to B*.
00:33:30.15 Now, we have a second stage of amplification, and if B* is the final signal
00:33:38.17 that activates the cell's response, that would have a time course
00:33:43.04 as shown on the top curve.
00:33:44.16 There would be an even greater delay,
00:33:47.02 because it takes a while for the A*s to accumulate
00:33:49.25 and then for the B*s to accumulate, in turn.
00:33:52.11 But the reward for that delay is an enormous amplification of B*s,
00:33:59.02 perhaps in the thousands, tens of thousands, or hundreds of thousands.
00:34:02.10 So, how does a photoreceptor actually do it?
00:34:05.03 Well, it turns out that this rightmost path is, in fact,
00:34:08.08 the path used in the vertebrate eye.
00:34:10.29 In this case, rhodopsin is the initial catalyst, the G protein-coupled receptor,
00:34:15.13 catalyzes the activation of multiple G-proteins called transducin, shown in green, here.
00:34:23.07 And then the transducins activate a cyclic GMP phosphodiesterase,
00:34:28.09 shown in blue. So, in the dark, the phosphodiesterase is inactive,
00:34:34.28 the transducin is also inactive, the rhodopsin is inactive,
00:34:37.16 and cyclic GMP is present at quite high concentrations.
00:34:42.12 And cyclic GMP, the internal messenger, acts directly on an ion channel in the membrane,
00:34:49.13 a sodium and calcium permeable channel, and maintains it in the open state.
00:34:54.17 The effective light activation of this cell is to activate a rhodopsin,
00:35:00.16 this red ball here, which then activates a series of transducins --
00:35:05.10 a large number, although only one is drawn here, it's in the hundreds,
00:35:08.14 and each of those transducins then activates a phosphodiesterase,
00:35:13.04 which in turn hydrolyzes hundreds to thousands of cyclic GMPs.
00:35:16.16 So, the cyclic GMP level drops precipitously,
00:35:20.28 and that drop then leads to the closure of the ion channels.
00:35:25.15 In fact, you can even think of the ion channel as, essentially, a catalyst.
00:35:28.16 It's catalyzing the transfer of ions from one side of the membrane to another.
00:35:33.01 A single ion channel, of course, on a per-particle basis, allows the passage of many ions,
00:35:38.15 so really, in a sense, there are three stages of amplification in the system.
00:35:43.05 And this is what has allowed a rod photoreceptor to respond to a single photon--
00:35:49.23 this enormous per-particle amplification.

Part 2: Photoreceptors and Image Processing II

00:00:09.14 Let's look at the arrangement of photoreceptors within the retina and explore the
00:00:14.20 significance of that arrangement for the functioning of the system.
00:00:18.09 Here's an en face view of a primate retina.
00:00:22.03 We see each of these white spots is a single rod photoreceptor cell
00:00:26.25 These are the closely packed ones, and the clear spaces with a single white spot
00:00:32.27 within it are the cone photoreceptor cells.
00:00:35.06 You can see that it's essentially a sea of rods with cones interspersed like little pebbles.
00:00:40.06 Now this is what the peripheral retina looks like - the region that's off to the side.
00:00:45.22 The retina overall is not a homogeneous structure
00:00:50.16 with respect to photoreceptor topography.
00:00:53.23 In the primate retina, including the human retina,
00:00:56.09 if we were to map across the width of the retina from one side to the other
00:01:03.00 the density of the different photoreceptor cell types,
00:01:06.16 you would see a very distinctive distribution.
00:01:10.00 So, for example, the rod photoreceptors are very abundant (that's these dots here)
00:01:16.12 are very abundant in the peripheral retina.
00:01:18.12 The cone photoreceptors down at the bottom are relatively rare in the peripheral retina,
00:01:23.20 as we saw on the previous slide.
00:01:25.24 But, towards the central retina, and especially at the
00:01:29.20 very center of the retina, called the fovea,
00:01:31.29 the cone density rises to a very high peak,
00:01:37.09 and the rod density correspondingly goes down
00:01:40.20 to essentially zero.
00:01:42.10 That's a very striking arrangement. Why is that?
00:01:46.21 It turns out that the retina of primates like ourselves
00:01:53.06 is really a two-stage structure, in the sense that
00:01:57.03 we have a high acuity zone of the retina, this very central zone,
00:02:02.25 which encompasses just a tiny fraction of the retinal surface area... less than 1%.
00:02:08.06 And the rest of the retina really exists simply to alert the central retina
00:02:14.09 that something interesting has happened.
00:02:15.22 Whenever we look at something that we're interested in,
00:02:19.20 what we do is we turn our eyes towards that object.
00:02:22.22 And, we do that because we want the object to be imaged
00:02:26.08 on the central region of the retina.
00:02:28.18 Let's look at one simple consequence of this specialization within the retina.
00:02:33.29 And that is the eye movements that allow different regions of the world in which
00:02:40.21 we're interested in to be sequentially imaged on the central retina.
00:02:43.26 And there's no better example than reading text.
00:02:46.00 So, if you're reading the page of, for example, a novel,
00:02:49.26 and you look at the eye movements that correspond to that process,
00:02:55.29 you'll see that as we look here on the horizontal axis at eye position
00:03:00.13 and on the vertical axis at time,
00:03:02.16 if we look at the way the eye moves, we'll see that it's a series of stops and starts
00:03:08.12 where the eye is initially focused on one location,
00:03:12.18 and then there's a rapid movement to a new location,
00:03:15.01 a brief pause at that place and then a rapid movement to yet another location
00:03:19.06 and so on as we read from word to word across the page.
00:03:23.20 And, of course, when we get to the end of the line, then we reset all the way back
00:03:27.18 to the beginning of the next line.
00:03:31.03 Why are we doing that? Why can't we just stare straight ahead and read?
00:03:34.29 And the answer is that, when you're reading,
00:03:37.01 you need to see the words... the letters of interest
00:03:41.02 at high acuity. And the only way to do that
00:03:43.14 is to have them fall on the very central retina.
00:03:45.24 So, your eye must continually be moving to allow
00:03:49.20 the object to interest to fall in that most central region, the fovea.
00:03:54.22 Really, the rest of the retina exists simply to get our attention
00:04:00.09 and tell us about an object that we might want to image on the fovea.
00:04:04.21 For example, if you see a fly in your peripheral vision
00:04:08.01 buzzing around the room, that will grab your attention,
00:04:10.20 and you'll perhaps turn your eye towards the fly,
00:04:13.14 but you won't really get a good look at it until you turn your eye towards it.
00:04:17.20 Now let's look at eye movements in a somewhat more natural context.
00:04:22.12 And this tells us also something not only about the eye, but about the brain...
00:04:25.23 that is, about which regions of a given scene
00:04:28.16 (perhaps a more natural scene than reading of text)
00:04:31.01 grabs our attention.
00:04:32.13 And so here, for example, we're looking at eye movements
00:04:36.00 that correspond to looking at faces.
00:04:40.16 So, the top two photographs are the images that were observed,
00:04:46.04 and the bottom data points indicate where the eyes were pointed
00:04:52.16 during viewing of that image.
00:04:54.00 So, when looking at a face... for example, this young lady's face.
00:04:58.07 You can see that the eyes of the observer tend to look at her eyes,
00:05:04.11 and they also look at her mouth, they look at her nose a little bit,
00:05:06.26 maybe around the face.
00:05:07.28 If we look at a face in profile, again, you can see from where the eyes were pointing,
00:05:12.14 that they tend to look at the edges of the image--
00:05:18.02 the nose, the eyes, the chin, the mouth, and so on,
00:05:20.24 and largely neglect the central regions --
00:05:24.04 the cheeks, the neck, and so on that are perhaps of lower interest
00:05:28.11 and where less is going on.
00:05:30.09 This is a highly selective viewing strategy,
00:05:34.03 and so it really means that we're sampling the world in a way that is
00:05:38.26 far from random and far from complete.
00:05:42.00 We're sampling just subsets of any given scene.
00:05:45.17 Now, the fact that the central retina is the only place
00:05:52.08 where the image is seen with high acuity
00:05:55.21 means that, if you're going to examine, say, a non-moving object,
00:06:00.18 and you're doing it in the context of a head and a body that are moving,
00:06:05.17 perhaps, because you're walking or running or just trying to stay still,
00:06:09.11 but you're not entirely still,
00:06:10.18 there must be a mechanism that allows the eye to stabilize itself
00:06:15.11 in the context of this moving holder that is the rest of the body.
00:06:19.21 And, in fact, that mechanism exists, and it's a very powerful one.
00:06:23.22 Here's an example just showing how that sort of movement compensates
00:06:29.05 for head and body movements during walking.
00:06:31.14 Again, on the horizontal axis, we have the angular degree,
00:06:36.26 and we have plotted here, in red, the head movements associated with walking.
00:06:42.13 Of course, there's some swaying back and forth with each step.
00:06:45.00 And, as time goes on, moving up this chart,
00:06:48.28 we see that the head movements in red
00:06:53.05 and the eye movements within the head in green
00:06:56.01 are nearly mirror images of each other.
00:07:00.16 That is, the eye is moving within the socket to almost perfectly compensate
00:07:07.10 for the back and forth movement of the head.
00:07:09.26 It's completely unconscious - we do it without thinking about it.
00:07:12.29 But, the result is that the eye is pointing nearly perfectly straight ahead
00:07:18.29 during the entire walk
00:07:21.23 by virtue of this moment to moment sense of where it is deflected.
00:07:27.05 If it's deflected a little to the left, it will move a little to the right.
00:07:29.24 If it's deflected a little to the right, it will move to the left,
00:07:32.06 and this, of course, keeps the world looking relatively still
00:07:36.05 despite the fact that we are moving around.
00:07:39.27 Now let me say one final thing about the arrangement of photoreceptors
00:07:45.15 within the retina... this non-random arrangement.
00:07:49.02 And that now relates to the distribution of the different cone photoreceptors.
00:07:53.26 We haven't talked about the different kinds of cones.
00:07:56.07 We're going to talk in detail about that in the second and third lectures.
00:07:59.16 But, in the human retina, let's just for the moment note
00:08:03.19 that there are three different cone classes.
00:08:05.29 One most sensitive to longer wavelengths,
00:08:08.23 one most sensitive to medium wavelengths of light,
00:08:10.24 and one most sensitive to shorter wavelengths of light.
00:08:12.24 The medium and longer wave receptors (and I'll call these M and L),
00:08:17.06 are, in fact, rather similar to one another, with largely overlapping absorbances,
00:08:23.08 whereas the shorter wave (which I'll call S),
00:08:26.08 is really quite different. It absorbs at wavelengths quite down
00:08:30.18 towards the short end of the visible spectrum.
00:08:33.13 And, as a consequence, any given image which contains
00:08:38.16 wavelengths throughout the visible spectrum,
00:08:41.12 and which therefore would be exciting all the different photoreceptor cells,
00:08:45.26 will, when it arrives at the retina, not be perfectly focused.
00:08:50.13 Let's think about why that is.
00:08:51.24 It's because any lens, including our own lens,
00:08:55.17 has some degree of what's called chromatic aberration.
00:08:58.16 That is, the image being focused for a given wavelength,
00:09:03.05 is not going to be focused perfectly for some other wavelength.
00:09:07.24 And so, for example, as shown here, in this upper image,
00:09:12.25 if this word red, in red letters, is focused perfectly on the retina,
00:09:18.02 so that it's sharply focused and a point in the outer world
00:09:21.19 comes to a point on the retina,
00:09:23.01 then blue light, coming from the same region of space,
00:09:28.07 would be focused actually ahead of the retina,
00:09:30.18 so that the word blue here, as shown on the right,
00:09:33.09 would be a bit blurry - a bit out of focus.
00:09:35.23 How does the retina deal with this problem?
00:09:38.11 It turns out that in the very central fovea, the S cones have been excluded.
00:09:43.19 Only M and L cones populate that region.
00:09:47.02 That's the region with the very highest acuity.
00:09:50.00 And, what the retina has done... what the eye has done,
00:09:52.24 is simply eliminate the short wave end of the spectrum
00:09:59.00 for that most high acuity zone of the retina.
00:10:03.12 So, by taking a narrow cut of wavelengths,
00:10:07.05 we're effectively getting a sharper image,
00:10:09.01 of course at the price of having a less colorful image.
00:10:12.25 Now let's talk about how the image is processed in the retina.
00:10:19.07 We mentioned at the very beginning of the lecture,
00:10:20.27 that the retina is really different from a film in a camera,
00:10:24.16 in the sense that it is not just detecting the image,
00:10:29.03 and sending that detected image to the brain,
00:10:32.10 but it's processing it and extracting from it those aspects
00:10:35.28 that are of greatest interest to the organism.
00:10:37.24 Let's look at one of those feature extractors here.
00:10:41.19 This can be illustrated with this classic illusion, the so-called Hering illusion.
00:10:45.10 I hope you can see that at the intersections, where these white streets come together,
00:10:50.21 there are illusory black dots.
00:10:53.21 Actually, not on the one that you might focus on,
00:10:56.10 but on the ones that strike your peripheral retina.
00:10:59.01 I'll leave it as a homework exercise to figure out why that should be so.
00:11:03.22 But, if you can't see these as clearly as I can,
00:11:07.29 I invite you to produce an illusion of just this sort on a home computer.
00:11:12.04 You can do it either with black squares and white streets in between
00:11:15.20 or the reverse, if you do it with white squares and black streets,
00:11:19.02 you'll see little white dots at the intersections of the streets.
00:11:22.04 And this illusion has been known for well over a century,
00:11:26.08 and it turns out to be fully explained by a peculiar type of spatial organization
00:11:34.06 of the receptive fields of ganglion cells.
00:11:37.11 Now, let me define receptive field.
00:11:38.27 That refers to that zone of primary photoreceptor input
00:11:45.12 which influences the output of the retinal ganglion cells.
00:11:48.07 The classic retinal ganglion cell receptive field is as shown here.
00:11:53.07 There's a little zone of retina (So, we're looking now en face at the retina...)
00:11:56.22 There's a little zone of retina where illumination excites the ganglion cell
00:12:01.12 and would increase, for instance, the firing of action potentials.
00:12:04.15 And then there is an annulus around it (a donut)
00:12:07.29 where light activation of photoreceptors actually inhibits the ganglion cell.
00:12:15.08 This is essentially a contrast detector.
00:12:18.18 The ganglion cell is looking for spatial differences in illumination in the retina,
00:12:23.26 and let's see how this explains that illusion.
00:12:28.17 Here I've shown, superimposed on a little region of that Hering illusion
00:12:34.28 one of the receptive fields of a ganglion cell,
00:12:39.00 where, in the center, the ganglion cell is looking at the light
00:12:44.15 that falls at one of these intersections
00:12:46.05 between white streets, and I think we can see that for this particular cell,
00:12:50.17 not only is the center being illuminated, which would tend to excite the cell,
00:12:53.16 but a rather substantial chunk of the surround on the right and left sides
00:12:59.02 above and below is also being illuminated, so the result would be
00:13:02.25 that this cell would be substantially suppressed
00:13:06.00 by that illumination of the inhibitory surround.
00:13:09.20 What happens if we look at a ganglion cell that’s little off to the right?
00:13:14.15 Its center is also fully illuminated, but now I think you can appreciate
00:13:18.16 a bit less of the surround is illuminated than was the case for the first cell.
00:13:23.18 But now, if we look at a cell that is even further off to the right,
00:13:28.15 where we see the center is illuminated,
00:13:31.01 but now the surround is only minimally illuminated...
00:13:34.07 just this horizontal region here is receiving light,
00:13:38.10 I think we can appreciate that this cell would be substantially more active,
00:13:41.18 because the central region being fully illuminated is countered by
00:13:46.14 only a modest amount of inhibitory illumination just from the left and right sides.
00:13:51.04 So, the brain would perceive, or the retina would perceive,
00:13:56.28 this zone of white street as brighter, relative to the zone over here,
00:14:04.08 because the ganglion cells, seeing the street that's off to the right
00:14:08.09 or off to the left side,
00:14:10.10 would have greater overall activity than would the ganglion cell
00:14:15.11 that was centered over this intersection here.
00:14:20.12 And that is the source of that illusion of blackness...
00:14:24.02 that sort of fuzzy blackness at the intersections between the streets.
00:14:27.16 I should just say that this idea of center-surround ganglion cells was really predicted
00:14:34.09 by this illusion many years before those cells
00:14:36.28 were actually identified electrophysiologically.
00:14:39.21 Now this leads us to a larger question related to image processing in the visual system,
00:14:52.06 and here we're going to move a little bit beyond the retina, into the brain.
00:14:55.00 But, if what the retina and also the brain are looking for,
00:15:00.01 among other things, are differences
00:15:02.11 between one region of the image and another region,
00:15:06.07 as illustrated by those center-surround ganglion cells,
00:15:09.01 then it stands to reason that schematics of the sort that bring out those differences
00:15:17.22 might resonate in some way - might have a special meaning
00:15:21.02 in terms of their information content for our visual system.
00:15:25.03 And that is exactly the case.
00:15:26.12 So, for example, we see here on the left, a photograph of a hand
00:15:30.29 with fingers about to snap, and on the right,
00:15:34.02 we see a schematic here, just a line drawing of the same kind of hand
00:15:40.08 with the fingers about to do like this (snap).
00:15:43.11 And, we immediately recognize in this simple line drawing what's going on.
00:15:48.06 But that's actually kind of surprising that we should recognize
00:15:52.21 this image of a hand so readily,
00:15:55.19 after all, on a pixel by pixel basis, this little schematic
00:15:59.14 bears very little resemblance to the photograph.
00:16:02.03 The schematic shows black lines that outline each object -
00:16:08.04 each finger and the palm of the hand,
00:16:09.27 but the real world isn't like that at all.
00:16:12.09 In fact, the real world is made of shapes with shadows
00:16:15.22 and complex changes in illumination--
00:16:18.13 as shown by that photograph--very different on a pixel by pixel basis
00:16:22.04 from this schematic.
00:16:23.17 Why is it that the schematic is so immediately recognizable?
00:16:27.28 I think almost certainly it's because the schematic is essentially the processed version
00:16:33.17 that our retina and our brain has created from the real image.
00:16:37.29 We've fed the visual system by giving it this schematic
00:16:41.20 a version which is already part way up the visual chain of command.
00:16:46.03 Let's see another example of this.
00:16:48.04 Here, in this self-portrait by Pablo Picasso,
00:16:51.27 we see the remarkable effectiveness of simple portrait sketches.
00:16:56.21 Why is this so immediately recognizable as a face?
00:16:59.15 And, in fact, it's recognizable as Picasso's face.
00:17:01.21 Again, even though, on a pixel-by-pixel basis, it's very different
00:17:06.01 from a real human face, it has abstracted the information
00:17:10.16 in just the way that our brain abstracts it as well.
00:17:13.10 Now there are other things that the visual system is interested in,
00:17:17.17 besides changes in intensity over space.
00:17:22.08 It's also interested in changes over time and the combination of time and space.
00:17:28.11 Let's look at an example of time changes.
00:17:31.16 Here are the responses of a retinal ganglion cell in the rabbit retina,
00:17:37.06 to an object moving in various different directions.
00:17:40.17 The directions of motion across the receptive field
00:17:42.28 are indicated by the arrows in the center here,
00:17:45.23 and the responses that go with each of those directions of motion
00:17:49.14 are indicated by the cluster of action potentials
00:17:54.04 recorded from that cell that are indicated adjacent to the corresponding arrow.
00:17:59.07 Now, I think you can appreciate that motion in the upward direction
00:18:05.00 and directions that are close to it are especially effective,
00:18:08.04 but motion in the downward direction gives essentially no response.
00:18:12.13 So, this is a cell that is interested in movement,
00:18:16.05 and in particular, in movement in a given direction.
00:18:19.14 Having cells of this kind makes perfect sense in a visual system.
00:18:24.29 Because of course, things that are moving in the world are of interest to the organism.
00:18:29.02 If you're a rabbit, you're interested in things like hawks that might be moving,
00:18:33.14 and so having cells that respond selectively to motion
00:18:37.21 as opposed to simply the world that is unchanging,
00:18:41.11 has obvious selective value.
00:18:43.04 And I think we can all appreciate that in our own, everyday experience,
00:18:46.24 For example, if a fly or some other object is moving in the peripheral visual field,
00:18:52.06 that immediately catches our attention and it does so in a way
00:18:56.21 that non-moving objects do not.
00:18:58.17 Now this brings us to a potential paradox.
00:19:02.07 As we saw a few minutes ago, the head and body are generally moving
00:19:07.15 around in space as we walk or go around our daily business,
00:19:12.09 and even though there are compensatory eye movements
00:19:14.13 which work quite well to keep the eye largely oriented in space,
00:19:20.00 despite those movements, the compensatory eye movements are not
00:19:23.19 perfect, and so the eye is constantly wiggling around back and forth in space,
00:19:28.19 and therefore the image which falls upon the retina
00:19:31.20 is constantly wiggling back and forth in the reverse direction.
00:19:36.06 So, if our retinas were composed of direction-selective cells
00:19:43.10 of the sort we've seen here,
00:19:44.21 and these cells are the ones that are supposed to tell us
00:19:48.06 what's moving out there in the world,
00:19:49.28 we have a problem that these cells would be constantly stimulated simply by
00:19:54.23 the motion that's referable to head, body, and eye motion, that is self motion.
00:20:00.18 What's the solution to that?
00:20:02.16 It turns out that the solution is a yet cleverer set of cells.
00:20:07.12 Again, motion-selective cells, but they respond only
00:20:11.28 to local motion and not to global motion.
00:20:14.26 And so, for example, if we deliver to one of these cells, within its receptive field,
00:20:22.11 a grating of alternating black and white stripes which are moving from left to right,
00:20:28.09 so they are just flowing across the visual field,
00:20:32.20 the cell gives a very brisk response if that's all that is being presented to the retina.
00:20:39.14 That is, the surrounding region of retina is being presented simply
00:20:43.03 with a non-moving grey background.
00:20:45.16 The cell loves this stimulus and responds vigorously.
00:20:49.09 But what if we simply unmasked the rest of the retina
00:20:54.29 and show it the same set of bars moving from left to right.
00:20:58.20 Now, the receptive field of the cell--this central circular region--
00:21:04.10 hasn't changed at all in terms of the stimulus being delivered to it.
00:21:07.26 Completely unchanged.
00:21:10.14 The only thing that's different is that now the surround is doing exactly the same thing.
00:21:14.12 What happens? The cell immediately falls silent.
00:21:18.03 That is, this is a cell which wants to see a little region of the world moving
00:21:26.07 against a background that is not moving.
00:21:28.25 And if everything moves at the same time, that's not an interesting stimulus.
00:21:33.07 This is perfect because this is just the kind of cell that would tell a rabbit,
00:21:38.13 for example, if a hawk were flying across the sky,
00:21:42.10 but not if the rabbit had turned its eye a little bit,
00:21:46.01 and all the trees and bushes and everything else whose images
00:21:50.06 are impinging on the retina
00:21:51.08 had moved across the retinal surface.
00:21:54.11 And, of course, that's just what the rabbit wants to know.
00:21:56.21 There's a very beautiful illusion -- the so-called Ouchi illusion,
00:22:00.26 named after the Japanese artist who initially derived this
00:22:06.02 which nicely illustrates this kind of motion-sensitive ganglion cells effect
00:22:11.21 on our visual system.
00:22:12.25 In the Ouchi illusion, and I've shown them here at two different magnifications,
00:22:18.01 because depending on how you're viewing this lecture,
00:22:20.21 one or the other might be more effective.
00:22:22.27 In the Ouchi illusion, there are a series of horizontal black and white bricks
00:22:28.21 which encompass the surrounding zone and are most of the image,
00:22:33.10 and then in the circular center is essentially the same thing,
00:22:37.01 except it's been flipped vertically.
00:22:38.27 And I think you can appreciate that the central region seems to float around
00:22:42.28 and bounce back and forth in a way somewhat different from the surround,
00:22:46.19 as if it's almost autonomous from the surround.
00:22:49.08 The origin of that motion is the spontaneous eye movements which we have all the time.
00:22:56.17 Our eyes are constantly jiggling around in their sockets.
00:22:59.03 And, because of that jiggling, the direction-selective ganglion cells
00:23:06.01 are differentially activated by the center versus the surround of this illusion.
00:23:12.06 So, for example, if the eye were to make a horizontal excursion,
00:23:18.16 these horizontal brick stimuli would be less provocative to those cells on average.
00:23:24.12 Why? Because a small horizontal movement leaves much of the visual field
00:23:29.27 with the same stimulus - the same little black zone that was stimulated
00:23:33.16 when the eye was at one location will now, if the eye moves a tiny bit horizontally,
00:23:38.10 still be stimulating in a black region of the new location.
00:23:44.08 But the vertically arranged bricks will have a relatively larger effect
00:23:48.29 for a given horizontal eye movement
00:23:50.18 because that little movement will be more likely to shift the gaze
00:23:54.27 from, say a black zone to a white zone or a white zone to a black zone.
00:23:58.10 Now, I think you can appreciate that the reverse is true for vertical eye movements.
00:24:01.27 So, that causes the visual system to make this segregation of the central circular zone
00:24:10.18 from the surround and it gives it this sort of bouncing back and forth...
00:24:14.07 this sort of autonomy that grabs our attention and makes this illusion so effective.
00:24:19.01 To continue with our theme of looking at how the visual system
00:24:24.07 extracts information from the scene,
00:24:27.06 we can ask whether having two eyes gives us some special advantage
00:24:31.00 over an organism that might have only one.
00:24:34.23 And the answer is yes, having two eyes does confer a special advantage.
00:24:37.27 And, in particular, it allows us to determine the depth of an object.
00:24:42.11 That is, its distance away from ourselves,
00:24:45.07 using the mechanism called stereoscopic depth perception.
00:24:49.05 Let's see how that works.
00:24:50.24 So, if we have two points in the visual world,
00:24:55.10 one here on the right, and one here on the left,
00:24:58.12 I think you can see by simple geometric optics
00:25:00.25 would be imaged on the retinas of the two eyes
00:25:04.04 shown down at the bottom.
00:25:06.11 And, of course, the left point is imaged to the right on the eye,
00:25:10.26 because the image is reversed when it's imaged on the retina,
00:25:14.09 and the right point is imaged on the left side.
00:25:16.12 And those two images fall on what would be called corresponding points.
00:25:21.07 There are corresponding points for the right spot,
00:25:24.19 and there are corresponding points for the left spot.
00:25:26.23 So, there's nothing surprising here.
00:25:29.08 But, now let's ask, what if there is a pair of points that differ
00:25:35.21 not by virtue of their position left and right,
00:25:38.29 but by virtue of the fact that one is further and one is nearer to the viewer.
00:25:43.02 Now, if you think about it, what the geometric optics tells us
00:25:48.17 is that the innermost point is imaged further to the periphery
00:25:56.07 on the left retina and also further to the periphery on the right retina.
00:26:01.00 That is, the corresponding points for this closer object
00:26:08.07 lie on opposite sides of the corresponding points for the further object.
00:26:13.19 That's this more distant one here, which is now imaged on points
00:26:19.03 that are closer in - more nasal - on the two retinae.
00:26:23.00 Now that is a bit more confusing, perhaps, for the brain to figure out.
00:26:30.10 What the brain has to do is figure out that the image that it sees for that nearer point
00:26:37.29 on the left retina and on the right retina - those two images
00:26:41.25 are really representing the same object. This is the so-called correspondence problem.
00:26:46.02 And because it falls on these different sides of that further object's image,
00:26:51.29 therefore, it must be nearer.
00:26:56.09 The nearer one can be detected as nearer by virtue of the fact
00:27:01.13 that its corresponding points are lateral,
00:27:04.06 and the further one can be detected as being further
00:27:07.11 by virtue of the fact that its corresponding points are more nasal.
00:27:10.21 Now that's a very difficult computational problem, as it turns out.
00:27:15.00 It's not fully understood how it's done.
00:27:17.03 But, it's a very powerful system, and it gives us very accurate depth perception.
00:27:22.16 As we consider further how the brain analyzes the visual scene,
00:27:29.00 we eventually reach a point where that information is so complex,
00:27:33.26 that the analysis requires not just an assumption-less series of steps,
00:27:40.22 but a series of steps that involve imposing some order
00:27:43.19 related to our previous experience of visual information.
00:27:47.25 Let me give you an example of this.
00:27:49.01 Here we see a way in which we analyze depth from shading,
00:27:53.26 and we have included in that analysis a hidden assumption.
00:27:57.26 And the hidden assumption is that this three dimensional object
00:28:01.20 has been illuminated from above.
00:28:03.18 That's a natural assumption because ,in general, the sun, the classic illuminant,
00:28:09.11 (the classic source of illumination) is above.
00:28:11.20 And so, when we see an object, generally, shadows are below,
00:28:15.22 and the rounded object can be analyzed in part based on the pattern of its shadows,
00:28:22.15 with that correct assumption that the sun is sitting above it.
00:28:27.05 So here we have, just to illustrate this type of analysis,
00:28:31.12 two images of cuneiform writing.
00:28:34.18 In fact, these two images are the same image. The one on the right
00:28:40.08 differs from the one on the left only by virtue of having been rotated 180 degrees.
00:28:45.22 And yet, I think, we can appreciate that the one on the left looks
00:28:50.12 as if the cuneiform is coming out at us from the plane,
00:28:54.15 and the one on the right looks like the cuneiform is receding into the plane.
00:28:58.20 And, of course, the reason is that when we see the shadows on the left,
00:29:03.02 we immediately assume that the sunlight is from above, and therefore,
00:29:07.04 since the shadows are below, the objects are coming out,
00:29:10.08 and on the right side, the reverse -- the shadows,
00:29:14.15 if we assume the illumination is from the top,
00:29:17.11 would tell us that the objects have receded into the plane.
00:29:20.22 OK, that's obviously a learned assumption...
00:29:23.29 it's really an unconsciously learned assumption.
00:29:25.20 And, it's generally a correct one.
00:29:28.21 Let's look at another way in which learned responses
00:29:34.02 affect the way we analyze a scene.
00:29:36.28 Of course, we've moved around in a world of three dimensions,
00:29:40.02 and we're very tuned into the depth of 3D scenes.
00:29:44.18 We see some objects as closer, some as further away,
00:29:47.15 In this little cartoon, we certainly get a strong sense
00:29:51.13 that the walls form a zigzag pattern.
00:29:54.12 coming out and receding. Of course, the gentlemen who are pictured here
00:29:59.01 by virtue of their size reinforce that sense.
00:30:03.20 And now, if we look at these heavy black bars -
00:30:07.00 the one that's here and the one over here to the left,
00:30:10.10 we get a strong sense, because we have assumed their different depths,
00:30:16.29 that the bar that is to the right is a smaller object than the bar that is to the left.
00:30:22.23 And, of course, in the context of this three-dimensional scene, that is perfectly right.
00:30:27.22 But, in fact, if you measure the size of these two bars in this particular image...
00:30:33.16 you simply put a ruler next to this bar and a ruler next to that bar,
00:30:37.28 they're exactly the same size.
00:30:39.19 Now, what's most striking is that once one knows that,
00:30:43.12 even if you hold two rulers next to them,
00:30:46.05 and you prove to yourself that that is the case,
00:30:49.18 you have a very difficult time convincing your visual system that that's true.
00:30:54.05 That differential in size is a very strong and persistent sense,
00:31:00.25 and again, it's a learned sense,
00:31:04.09 and it is learned in the context of depth perception in the real world.
00:31:08.20 Now, let's look at one particularly famous example of this sort
00:31:14.00 of imposing of order upon a scene.
00:31:16.28 This is a commemorative vase that we're seeing here.
00:31:20.17 It was made to honor Prince Philip and Queen Elizabeth,
00:31:23.22 and I think you can appreciate that, not only is it a beautiful white vase,
00:31:28.11 but on one side is the profile of Queen Elizabeth -
00:31:31.29 that is the shape of the vase makes her profile,
00:31:34.23 and on the other side is the profile of Prince Philip.
00:31:37.23 That's the nose right here, here's a nose right here.
00:31:40.16 And, the striking thing about the vase, as you view it and look for those profiles,
00:31:48.22 is that the sense that it is a white vase or the sense
00:31:54.00 that it is two faces looking at each other,
00:31:57.01 seem to be mutually exclusive. That is, you can see one or you can see the other,
00:32:02.18 but it's hard to see them both or see them fully
00:32:06.06 and see them in a dominant way at the same time.
00:32:09.01 And this is just an example, again, of the visual system
00:32:11.29 imposing some order on the scene.
00:32:14.10 It's trying to choose which of those two somewhat exclusive ways
00:32:19.01 of looking at the scene is the correct one.
00:32:20.27 Let's look at one final example.
00:32:23.23 And this is a series of four sketches that Pablo Picasso drew of a bull
00:32:29.02 (one of his favorite subjects).
00:32:30.16 I think you can appreciate that going from the upper left to the lower right,
00:32:35.11 we're seeing an increasingly abstract version of a bull.
00:32:39.00 Yet the striking aspect of this set of drawings is that, despite the abstraction,
00:32:45.02 we immediately recognize it for what it is.
00:32:47.27 Now, like those simple line drawings, I think this begs the question why that should be so...
00:32:53.26 why the visual brain is so good at these sets of abstract images as representing a bull.
00:33:02.18 And, like those line drawings, I think our conclusion would be that
00:33:06.08 what Picasso has hit upon is that these almost child-like drawings
00:33:10.27 are really the internal representation
00:33:13.12 of the bull. And that we have created a simplified, abstracted version in our heads,
00:33:18.11 our memory trace of what a bull is that really is this kind of drawing
00:33:24.13 and therefore resonates immediately with it.

Part 3: Human Color Vision and its Variations

00:00:02.02 I'm Jeremy Nathans. I'm a professor at the Johns Hopkins Medical School,
00:00:05.15 and an investigator of the Howard Hughes Medical Institute.
00:00:08.05 This is the second of three lectures on the vertebrate retina,
00:00:11.23 its structure, function, and evolution.
00:00:14.05 In this lecture, we're going to look at human color vision and its variations.
00:00:18.19 Now, why should we study color vision?
00:00:22.21 Well, one reason - the reason I like most - is because it's beautiful.
00:00:26.09 We see a world of color out there, as seen in this upper photograph,
00:00:31.17 and if we were deprived of that color, the world would be a far duller place.
00:00:36.16 And the story I want to tell you is really a centuries-long story,
00:00:42.05 It begins in 1665, in the dormitory room of Isaac Newton,
00:00:47.10 who was an undergraduate at Cambridge University at that time.
00:00:50.05 And Newton did a very simple, yet profound experiment.
00:00:54.02 He drilled a hole in his wooden shutter to admit a beam of light,
00:01:00.12 and he passed that beam of light through a prism and asked what happened to it.
00:01:06.03 And the answer was that the beam was split up into its component colors...
00:01:10.19 the colors of the rainbow, from red at the least refrangible to violet at the most refrangible,
00:01:19.11 and in between, orange, yellow, green, and blue.
00:01:22.09 Now that had been known for some time, but Newton appreciated
00:01:27.04 the physical significance of that phenomenon.
00:01:29.16 That is, that white light is a summation of different lights,
00:01:33.21 that those lights could be separated physically in an objective fashion,
00:01:37.17 and that their properties could be objectified, in the sense that each kind of ray
00:01:43.05 had a particular angle of refraction when passed from air to glass and then glass back to air again.
00:01:49.24 And furthermore, Newton realized that this method could be used
00:01:53.17 to purify the individual rays of light to obtain spectrally pure samples with which to work.
00:02:01.01 So, for example, by blocking off all but the orange ray of light as shown here,
00:02:05.26 Newton was able to obtain a beam of spectrally-pure orange.
00:02:10.03 Nowadays, perhaps, we would do this with a tuneable laser to obtain a spectrally-pure light.
00:02:15.23 But, Newton appreciated the power of this purification method, and he used it to good effect,
00:02:21.20 and in particular, he used it to recombine lights of different colors.
00:02:27.28 Now, I can't resist showing you Newton's original sketch.
00:02:32.06 This is his quick pen sketch of the experiment.
00:02:36.10 It's reversed left to right from the image that I showed you earlier,
00:02:39.05 but here's the beam of light coming in the window,
00:02:42.11 it's refracted by the prism, and then various beams are obtained from it
00:02:48.00 by using their specific angles of refraction.
00:02:50.26 And in particular, Newton performed the following very simple but profound experiment
00:02:57.19 diagrammed here, essentially with projectors, as we might do it today.
00:03:01.27 But Newton did it with mirrors and those beams of spectrally purified light.
00:03:07.19 Newton asks, what if you combine together a spectrally pure red and a spectrally pure green light?
00:03:17.27 What would it look like?
00:03:19.22 The answer is it looks like neither. It looks yellow.
00:03:22.14 That is, the superposition of red and green looks just like a spectrally pure yellow.
00:03:28.26 But, it is not physically identical to a spectrally pure yellow, and Newton could prove this
00:03:36.14 by passing this yellow, such as we see here, through another prism
00:03:41.28 and a spectrally pure yellow, identical to it in all appearances,
00:03:45.28 also through a prism. And what he saw was that they behaved quite differently.
00:03:49.20 A spectrally pure yellow retains its same initial angle of refraction,
00:03:54.15 and remains a single homogeneous beam when passed through a prism,
00:03:58.07 but a synthetic yellow, such as we see here,
00:04:00.21 one obtained by superposition of red and green lights,
00:04:04.05 in fact splits up back into its component red and green lights,
00:04:07.10 each with their own original angles of refraction when passed through a prism.
00:04:12.11 And Newton realized that this meant that human color vision was imperfect
00:04:17.19 in the sense that it could not distinguish certainly physically distinct combinations of lights.
00:04:23.06 Now, as far as we know, Newton never examined the physiologic basis for that phenomenon,
00:04:32.25 although he worked on the physical rules that govern
00:04:37.10 the recombining of lights of different colors.
00:04:40.18 Now we would refer to them as different wavelengths
00:04:43.13 instead of the angles of refraction that he used.
00:04:47.11 But he had remarkable physical insights into the color vision system,
00:04:53.20 and let me just give you one of these.
00:04:55.27 This appeared in his Opticks, his wonderful book describing his early experiments.
00:05:02.23 And here, Newton asks, "May not the harmony and discord of Colours"
00:05:08.11 (that is, the similarities and differences of the colors)
00:05:11.04 "arise from the proportions of the Vibrations propogated through the"
00:05:15.28 "fibres of the optick Nerves into the Brain as the harmony and discord of Sounds"
00:05:20.29 "arise from the proportions of the Vibrations of the Air?"
00:05:24.24 Now this is a remarkable insight at a time when it was
00:05:27.28 not at all clear what the physical nature of light was.
00:05:31.06 Newton had a sense, really by analogy, that light might consist of some sort of wave,
00:05:36.29 but I should say that Newton also in the Opticks discussed
00:05:41.22 the evidence that light has a particle-like characteristic.
00:05:46.24 He comes down somewhat in the middle and says that it is both particle-like and wave-like
00:05:52.20 a remarkable judgment in light of the fact that this is 2+ centuries before the quantum theory
00:06:00.07 emerged to tell us that in fact that light does have both particle and wave properties.
00:06:05.07 The physiologic underpinnings of the color mixing of the kind that Newton saw
00:06:11.08 was not really laid out until the 19th century.
00:06:14.25 Its first clear description we owe to Thomas Young,
00:06:19.24 the universal genius who worked on many things, including the Rosetta stone and vision.
00:06:25.28 And Young, in the Bakerian Lecture of 1802, before the Royal Society of London,
00:06:32.23 suggested the following as a model for how color vision might work.
00:06:38.27 And he says, as follows: "As it is almost impossible to conceive"
00:06:44.26 "each sensitive point of the retina to contain an infinite number of particles"
00:06:49.28 (that is, an infinite variety of receptors)
00:06:52.18 "each capable of vibrating in perfect unison with every possible undulation,"
00:06:58.08 (that is, every possible wavelength of light, because at this point,
00:07:01.25 it was clear that light had wave properties)
00:07:04.10 "it becomes necessary to suppose the number limited,"
00:07:07.20 "for instance, to the three principle colours, red, yellow, and blue,"
00:07:11.24 "and that each particle is capable of being put in motion more or less forcibly"
00:07:17.10 "by undulations differing less or more from perfect unison."
00:07:22.04 Now this very prescient statement in fact hit the nail exactly on the head.
00:07:27.11 Because what Young is saying here is that each of the three receptors
00:07:30.24 would have a broad, bell-shaped curve.
00:07:34.03 There would be a place where it is maximally sensitive,
00:07:37.03 here it is matching the so-called undulation of the stimulus,
00:07:42.29 and then there are other parts of the spectrum
00:07:45.15 where it would be less sensitive because the match was less perfect.
00:07:48.00 And so, by having that sort of broad sensitivity, the limited number of receptors,
00:07:55.19 the three receptors that he's postulating, would be able to cover the spectrum,
00:07:58.28 and between them, and by comparing their degrees of excitation,
00:08:03.03 one could determine the color appearance of any given light.
00:08:07.13 That was exactly right.
00:08:09.17 Now, the physiologic evidence - the behavioral evidence, really, from testing humans -
00:08:16.02 that would support this model was not forthcoming until another 50 years or so.
00:08:21.14 And it came from two equally remarkable scientists,
00:08:25.12 Hermann von Helmholtz, shown at the left,
00:08:27.21 and James Clerk Maxwell, shown at the right.
00:08:29.23 They're both shown here in their 20s. Helmholtz, at the time of this photograph,
00:08:34.06 had just invented the ophthaloscope and revolutionized ophthalmology.
00:08:38.16 Maxwell, shown to the right, is holding his famous color top.
00:08:42.11 He's an undergraduate in this photograph. He had been interested in color for many years,
00:08:48.16 starting as a young man, and had devised a series of tops with different colored patterns
00:08:54.08 that he could spin at high speed to effect a uniform mixing of the colors.
00:08:59.05 We now know, if we can fast forward another 150 years or so,
00:09:07.23 that the Young model is exactly right, and there are, in fact, three light sensors,
00:09:15.29 and they have broad, bell-shaped curves, and these curves
00:09:19.02 have now been identified by a number of methods,
00:09:21.27 and here's just one of them.
00:09:23.25 In this case, the individual proteins that constitute these receptors and their chromophores
00:09:31.20 have been joined together and the absorbance spectra determined in a spectrophotometer,
00:09:35.22 and we see that Rho, which stands for rhodopsin,
00:09:40.10 the one in rods, is this curve peaking at about 500nm.
00:09:46.10 This one is not involved in color vision. I've put it here just for completeness.
00:09:49.20 It's involved in dim light vision only, but it has the same overall shape
00:09:54.07 because these different pigments work, in terms of their photochemistry,
00:09:57.23 really in the same way.
00:09:59.14 But the other pigment curves - the longer wave, the so-called L curve,
00:10:03.28 the medium wave (the M curve), and the short wave (the S curve)
00:10:08.09 are offset by substantial numbers of nanometers
00:10:12.24 from one another and from the rhodopsin curves.
00:10:15.07 And together, they span, essentially the full range of the visible spectrum.
00:10:20.03 Now what does this get us, in terms of color vision ability?
00:10:26.00 We can ask that in a number of ways.
00:10:28.00 One way we could ask that question is by simply asking, at any wavelength
00:10:34.25 along the entire visible spectrum, how different do two lights have to be to tell them apart?
00:10:41.19 So that's shown here, for example.
00:10:45.00 Here, on the horizontal axis is wavelength, and on the vertical axis
00:10:48.13 is the differential - the delta lambda - the differential between two lights
00:10:52.21 that one needs to discriminate them.
00:10:55.24 I think you can appreciate that the curve is not a simple flat curve.
00:10:59.11 It goes up and down - there are hills and valleys.
00:11:01.11 There are some places, for example right here, where a difference of perhaps half a nanometer
00:11:06.00 is sufficient to distinguish two lights, and other places
00:11:09.12 where a difference of say 6 nanometers or so is required.
00:11:13.01 This curve of hills and valleys is completely understood
00:11:19.06 by reference to the original absorbance curves of the pigments.
00:11:23.26 So, for example, if we ask how we can distinguish, say,
00:11:29.17 a light that is at say, 600 or so nanometers,
00:11:32.22 we can distinguish it because it differentially excites the long and medium wave pigments,
00:11:39.15 and a light of say 595 nm - a little bit blue-shifted relative to it
00:11:45.26 has a different ratio of excitation of those two pigments.
00:11:49.15 And that's a sufficiently great difference that we can tell them apart.
00:11:52.26 I should add that this set of curves also explains Newton's color mixing data
00:11:57.29 in a very simple way. For example, if we have a sum of red and green lights
00:12:03.17 those give a certain degree of excitation of those three different sensors
00:12:10.15 that is the same, as it turns out, as a spectrally pure yellow light.
00:12:14.09 So, of course, the system cannot tell them apart.
00:12:16.19 Because, in the end, a detector is really just a photon counter.
00:12:20.19 Once a given number of photons is captured and that number is then sent to the brain,
00:12:26.29 there's no way to tell what the source of those photons were.
00:12:29.26 Was it a long wave light that was captured inefficiently, but was very bright, for example,
00:12:34.28 or was it a shorter wave light which was captured more efficiently, but was dimmer.
00:12:38.10 That's completely lost within the system, and any pair of lights
00:12:42.19 which give the same total number of photons captured per unit time
00:12:47.27 for each of the three receptors, will, of course, then be indistinguishable.
00:12:51.29 As part of Maxwell's experimental approach to analyzing human color vision,
00:12:57.18 he brought to us a conceptualization of color perception,
00:13:02.14 which is extremely useful and is shown here.
00:13:04.29 Maxwell thought that we could conceptualize color space as a 3D space,
00:13:12.10 in which each of the three axes, as shown here (the L, the M, and the S axes),
00:13:19.24 correspond to the degrees of excitation of each of the three classes of cones.
00:13:24.19 So, for example, a white light, which equally excites all three cone classes,
00:13:30.25 as shown by this corner of this square here,
00:13:34.14 and this vector coming from the origin,
00:13:37.00 would be represented by a vector in this direction, and lights of other wavelengths
00:13:44.12 would be represented by distinctive vectors in their own unique directions.
00:13:48.16 So, in Maxwell's conceptualization, our color vision is a 3D color vision.
00:13:54.16 We have three degrees of freedom. If an individual were to have only two receptors,
00:13:59.23 that individual would have a 2D color space.
00:14:01.20 So, for example, if one is missing the L receptor,
00:14:05.17 the color space would consist of the M and S axes, and the plane defined by them,
00:14:11.12 but not the 3D S, M, and L space.
00:14:16.02 Now, let's talk a little bit about the chemistry of color reception,
00:14:24.10 and in particular, the ways in which different wavelengths of light might be selectively absorbed
00:14:31.16 by the different visual pigments.
00:14:33.08 We saw, in the first lecture, the overall structure of visual pigment.
00:14:37.10 It's a G-protein-coupled receptor. It has 7 transmembrane segments
00:14:40.24 and sits in the membrane of the outer segments.
00:14:44.06 And, as you recall from that first lecture, the actual light absorbing part
00:14:48.27 is a small molecular weight vitamin A derivative, called 11-cis-retinal.
00:14:54.27 That's true for all of the human pigments.
00:14:57.11 They're all built on the same basic architecture
00:15:00.01 and they all use exactly the same 11-cis retinal chromophore.
00:15:04.06 So, the difference between the pigments is a difference in the proteins,
00:15:09.15 and what the proteins do is they impose a somewhat different electronic environment
00:15:14.12 around that retinal chromophore which has the effect of tuning its absorbance spectrum
00:15:19.27 to either longer or shorter wavelengths.
00:15:21.26 So, one can think about this spectral tuning challenge as really
00:15:26.14 a question of how to modify the pi-electron system of the retinal chromophore
00:15:34.16 by virtue of the surrounding amino acid side chains.
00:15:38.13 And we can learn something that at least constrains the possible mechanisms
00:15:44.23 by examining the basic photochemistry of retinal itself.
00:15:47.19 The critical point, with respect to spectral tuning, is that the degree
00:15:55.00 of pi electron delocalization within retinal determines its absorbance spectrum.
00:16:00.20 Now, retinal being a conjugated polyene, as shown here,
00:16:03.18 with 6 double bonds, has a substantial amount of resonance and therefore
00:16:09.12 delocalization of electrons.
00:16:10.23 I've shown below just one resonance structure that one could draw.
00:16:14.22 Not a particularly favorable one because it places a positive charge on this carbon here,
00:16:19.06 as a result of moving the pi electrons over towards the nitrogen,
00:16:23.03 which now has a free electron pair,
00:16:25.06 but this, of course, sort of resonance structure, even though it is relatively unfavorable,
00:16:30.15 will contribute to some extent in that equilibrium ensemble of all resonance structures.
00:16:35.13 Just to get a sense of how substantial this resonance effect is,
00:16:41.24 in the absorbance spectra of these kinds of compounds,
00:16:44.27 let's look at two model compounds which are shown here.
00:16:48.18 A polyene at the top, and a cyanin at the bottom.
00:16:51.17 Now, these are drawn with the same number of double bonds as retinal has,
00:16:55.16 that is 6, and I think that you can see, if we look at this polyene at the top,
00:17:00.11 where the chain ends in carbons, here, that a resonance structure of
00:17:06.29 the sort that I've drawn just below it,
00:17:10.07 in which one carbon now carries a positive charge and the other carries a negative charge,
00:17:15.22 would be quite unfavorable.
00:17:18.04 That involves, of course, a charge separation,
00:17:21.18 in addition to the general unfavorability of placing charges on carbons,
00:17:25.26 and so, in general, polyenes have very little pi electron delocalization.
00:17:31.26 We know this for a fact because when one looks at the crystal structure of a polyene,
00:17:36.27 one sees that the lengths of the bonds are highly alternating.
00:17:39.28 There are lengths that are very long - the canonical single bond lengths,
00:17:43.28 and there are others in between that are very short - the double bond lengths,
00:17:47.07 and so this back and forth - long, short, long, short, long, short -
00:17:50.27 tells us that, really, the single bonds have virtually exclusive single bond character,
00:17:56.20 the double bonds have exclusive double bond character,
00:17:58.14 and to go along with that, the absorbance spectra of the polyenes
00:18:03.17 is at quite short wavelengths. For example, here,
00:18:06.26 we're down in the UV at about 360 nm for the absorbance maximum.
00:18:14.27 At the other end of the spectrum, so to speak, for pi electron delocalization,
00:18:23.25 would be a model compound of the sort shown down here.
00:18:26.15 This is a cyanin. Again, six double bonds, but now we have an NH2 at both ends,
00:18:33.29 in one case, there's a positive charge, a free electron pair,
00:18:38.09 but I think you can appreciate that these sort of resonance structures that are drawn below
00:18:42.26 in which each single bond has now been switched to a double bond,
00:18:45.28 each double bond now switched to a single bond,
00:18:48.04 simply is a mirror image of the one up above.
00:18:51.21 Now the free electron pair is at the right, the positive charge is at the left,
00:18:55.27 but everything else is the same in chemical terms.
00:19:00.18 And therefore, these two must be equal in their energies, and therefore equally likely to occur.
00:19:05.27 And therefore, the pi electrons are maximally delocalized in a cyanin-type compound.
00:19:13.02 And again, we know this is true because, from crystal structures of cyanins,
00:19:17.08 we see that all of the bond lengths are the same,
00:19:19.28 and they're mid-way between the lengths of single and double bonds.
00:19:23.16 Going along with that, we see that the absorbance maximum of a cyanin is quite long.
00:19:30.02 In this case, it's 750nm - far to the red. This would be a very deep red compound.
00:19:35.25 So, with these two model compounds, we see a difference
00:19:44.07 in absorbance that spans essentially the entire visible range.
00:19:47.19 All visual pigments that are known in nature are somewhere
00:19:52.04 within the extremes defined by these two model compounds
00:19:56.00 and, in essence, what visual pigments are doing is they're creating with the retinal chromophore
00:20:02.00 some sort of hybrid between a polyene and a cyanin.
00:20:06.00 A hybrid in the sense that the electrons are delocalized to a degree that is intermediate
00:20:10.22 between the delocalization seen with a polyene and the delocalization seen with a cyanin.
00:20:16.17 And, correspondingly, the absorbance spectra is intermediate
00:20:22.08 between the extremes shown here - 360nm and 750nm.
00:20:28.00 So, now that focuses our thinking a little bit, because really what we can imagine
00:20:32.14 the protein must do is make the chromophore look more polyene-like or more cyanin-like.
00:20:39.05 How it does that we don't know in complete detail at this point,
00:20:46.09 but we have a few clues.
00:20:47.11 First, we have the amino acid sequences of the proteins.
00:20:50.00 And when we look at those sequences, and here they're shown with the proteins splayed out in 2D,
00:20:55.02 with each dot in each of these schematics indicating an amino acid difference.
00:20:59.21 So, here, for example, we're comparing the M cone pigment
00:21:02.26 (the middle wave sensitive pigment) with rhodopsin.
00:21:05.18 Here we're comparing the S or short-wave pigment with rhodopsin.
00:21:11.06 Here we're comparing the medium pigment (the M pigment) with the short-wave pigment
00:21:15.08 and here, in the lower-right, we're comparing the medium pigment
00:21:19.03 with the longer-wave pigment (M vs. L).
00:21:22.17 So, I think you can appreciate that, although they have the same overall structure,
00:21:28.00 they are 7-transmembrane-pass G-protein coupled receptors,
00:21:30.29 there are obviously many amino acids that are identical - the open circles indicate identities
00:21:36.27 there are also, for at least three of these pairwise comparisons,
00:21:41.12 many amino acid differences.
00:21:44.14 I think you can also appreciate that there's an asymmetry here.
00:21:46.25 The M vs. L comparison shows that these two pigments are extremely similar - one to another.
00:21:53.14 There are only about 15 amino acid differences, depending which variants
00:21:58.01 (there are a few allelic variations that can make that number a little bigger or smaller)
00:22:02.17 but roughly 15 amino acid substitutions that distinguish the M and L pigments.
00:22:08.27 out of a total of 364 amino acids.
00:22:13.02 That asymmetry is going to be a focus of much of the remainder of this lecture
00:22:18.24 and the next lecture.
00:22:20.14 Now let's just look at what that sequence difference looks like in a dendrogram form.
00:22:28.19 Here we're looking at sequence divergence among human and mouse rod and cone pigments.
00:22:33.26 We've picked the mouse as a representative non-primate mammal.
00:22:37.26 And here, let me just say that the non-primate mammals have only two types of cones
00:22:42.20 and rods - very much like our rods - whereas a subset of primates, like ourselves,
00:22:49.09 have three cone pigments.
00:22:51.03 Now, if we look at these amino acid sequences, and here we're plotting on the horizontal axis,
00:22:56.14 the degree of sequence divergence - the typical dendrogram-type plot -
00:23:02.02 and on the vertical axis, we're plotting the
00:23:04.20 wavelength of maximal absorbance of the corresponding pigment.
00:23:07.26 So, let's just walk through this, starting with the mouse example.
00:23:11.01 The mouse rhodopsin, like typical mammalian rhodopsins,
00:23:14.17 absorbs at about 500 nm. And its sequence is rather different.
00:23:19.06 That is, this dendrogram line is rather long,
00:23:21.07 (this horizontal line) compared to the mouse medium wave pigment,
00:23:26.18 the only example that the mouse has of a longer-wave sensitive pigment.
00:23:30.21 And it's also quite different from the shorter wave pigment that the mouse has,
00:23:34.05 which actually absorbs in the near UV - it's absorbing at wavelengths
00:23:39.05 somewhat shorter than the one that we have.
00:23:40.28 In the case of the human pigment set, we see that again,
00:23:46.29 we have a rhodopsin typical of mammalian rhodopsins,
00:23:49.08 and we have a shorter-wave pigment, and these differ substantially in sequence.
00:23:54.09 And then in what must have been a very recent evolutionary event,
00:23:58.09 there has been a split on the arm of the tree to give a long and a medium wave pigment,
00:24:06.09 whereas our immediate mammalian ancestors did not enjoy that pair of pigments.
00:24:14.21 When the genes for the long and medium pigments were first identified,
00:24:19.12 not only were their sequences found to be very similar,
00:24:23.16 (and that, of course, is the source of the sequence comparison that we saw
00:24:26.16 just a few slides ago.)
00:24:28.13 But also the arrangement of genes was such that they were found
00:24:33.14 to be adjacent to one another on the DNA.
00:24:36.11 So here is the human arrangement of a long-wave pigment gene, shown with a red arrow,
00:24:41.08 and a medium-wave (an M pigment gene) shown with a green arrow,
00:24:43.23 and that's not a surprising arrangement, given their recent evolutionary history.
00:24:50.06 Genes that duplicate are not infrequently found to be adjacent to one another.
00:24:54.08 And we imagine that, at some point in evolution, there was an erroneous
00:24:58.22 recombination event which paired a segment downstream of one gene
00:25:03.27 and upstream of another, and as a result, we got a chromosome...
00:25:08.03 or our ancestors got a chromosome with both genes adjacent on the DNA.
00:25:13.29 Now, I've drawn here one aspect of that ancient arrangement
00:25:20.09 which I'll justify in the third lecture,
00:25:22.04 and that is that the original pair of pigments which underwent this recombination
00:25:27.09 were actually not identical - one was already an L pigment gene,
00:25:32.18 and the other was already an M pigment gene.
00:25:35.05 That is, they were already different from each other at the time of the duplication.
00:25:38.19 And, let me reserve that, as I said, that story for the third lecture.
00:25:43.08 Now, if we look in modern-day humans, although we see
00:25:48.13 this arrangement of a single L and a single M pigment gene,
00:25:51.09 on some chromosomes... in fact, only about 20% of human X-chromosomes
00:25:57.19 (it turns out, these are on the X chromosome.)
00:25:59.18 Only about 20% of human X-chromosomes actually have that arrangement.
00:26:03.17 When DNA from a large number of different humans was examined
00:26:09.03 to look at the arrangement of L and M genes,
00:26:12.14 it became apparent that the basic arrangement of one L gene and one M gene,
00:26:17.29 as shown at the top here, is actually not the most common of arrangements in the population.
00:26:24.22 In fact, most people have one L and two M genes,
00:26:29.14 some have one L and three M genes,
00:26:31.07 and there are a few even who have four or five M pigment genes.
00:26:35.01 All of these people, as far as we can tell, have perfectly normal color vision.
00:26:39.11 Now this arrangement almost certainly arises because
00:26:44.24 the genes are homologous to one another,
00:26:47.05 and are capable of recombination, and we'll discuss that in just a minute.
00:26:52.03 This recombination, in fact, gives rise to a number of variations in gene structure,
00:26:58.12 and the sort of variation which appeared first in terms of scientific attention
00:27:04.17 was the kind that affected color vision and has been called, perhaps incorrectly,
00:27:09.06 color blindness. Now, color blindness doesn't mean a complete inability to see colors.
00:27:14.11 As generally used, it means simply a reduction of color vision from trichromacy,
00:27:20.09 (from having three receptors) down to having only two.
00:27:23.21 Perhaps the most famous dichromat is shown here, John Dalton,
00:27:27.04 the great British chemist who, in his first scientific paper, in 1794,
00:27:33.05 described his own color vision in meticulous detail.
00:27:37.02 Dalton was a dichromat and he reported that what other people
00:27:42.29 called red or orange or yellow or green
00:27:46.04 appeared to him to be just a single kind of color, differing only in intensity.
00:27:50.25 Let's see what the world looks like to people who are missing one of the three receptors.
00:27:57.22 And, I'm showing here, photographs of a fruit stand, taken and processed so that we see
00:28:07.22 what it would look like to a normal trichromat, here on the upper left,
00:28:10.13 or to any of the three types of dichromats.
00:28:14.17 On the upper right, is a so-called protanope's view - someone missing the long-wave sensor.
00:28:21.29 On the lower right is the deuteranope's view - that's someone missing the medium-wave sensor.
00:28:27.00 And over here on the lower left is a tritanope's view -
00:28:29.28 that's someone missing the short-wave sensor.
00:28:31.23 Now, I think you can appreciate that, as Dalton reported,
00:28:35.23 without the long or medium wave sensor, the fruit looks very much washed out in its colors.
00:28:42.26 That is, the longer wavelength colors - the ones that
00:28:45.11 particularly characterize the ripeness of fruits,
00:28:49.08 the green, yellow, orange, and red colors,
00:28:52.15 really are not distinguishable readily by an individual
00:28:56.25 who is missing either the long or medium wave pigment.
00:29:01.12 What's the origin of these not-uncommon color vision anomalies?
00:29:06.25 It turns out that they arise from unequal recombination between the members of the
00:29:14.09 L and M tandem array, and that this recombination occurs quite readily
00:29:20.10 because the genes not only are adjacent to one another,
00:29:24.00 not only are they in a head-to-tail tandem array,
00:29:26.08 but they also have retained a very high degree of sequence identity
00:29:30.14 (approximately 98% identity at the DNA level).
00:29:33.21 And so, as a result, mispairing can occur at meiosis, of the kind shown here
00:29:38.19 For example, a region distal to this M pigment gene,
00:29:42.22 is nearly identical in sequence just distal to this L pigment gene.
00:29:48.02 And if those two regions were to pair at meiosis
00:29:51.12 and a recombination event were to occur there,
00:29:53.21 we would get the two meiotic products shown at the bottom.
00:29:57.04 One, in which only a single L pigment gene remains,
00:30:01.03 and the other in which an additional M pigment gene has been acquired.
00:30:05.00 The one at the top, if inherited by a man, since this is on the X chromosome,
00:30:12.15 (for whom this is, of course, the only X chromosome)
00:30:15.22 would cause dichromacy - this gentleman would be missing
00:30:20.07 M pigment and would have only the S and L pigments.
00:30:24.08 As far as we know, as I mentioned earlier, people with extra
00:30:28.01 pigment genes have perfectly normal trichromatic color vision.
00:30:32.21 In fact, current evidence would suggest that only the first gene in the array,
00:30:36.25 when there are multiple M pigment genes, only the first M pigment gene
00:30:40.19 is expressed, and more distal M pigment genes are not expressed.
00:30:44.23 Now, what if recombination occurs at an intragenic location, rather than between the genes?
00:30:54.06 That is, within the genes.
00:30:56.07 Well, if this region of the M pigment gene pairs with
00:31:00.07 the corresponding region of the L pigment gene,
00:31:01.29 and again these regions are almost identical in sequence,
00:31:04.19 it would create meiotic products in which there would be hybrid genes.
00:31:10.13 So, for example, one product in which the M pigment gene was lost,
00:31:15.09 would retain, not a normal L pigment gene,
00:31:17.25 but an L/M hybrid. And, the reciprocal product
00:31:22.08 would be present on the other chromosomal product of this recombination.
00:31:26.24 with an M pigment gene picked up as a more distal gene of that recombination event.
00:31:32.28 Now again, the color vision of these individuals would be affected.
00:31:38.10 Of course, the person who inherits this chromosome at the top,
00:31:42.05 (the male who inherits that chromosome) would be missing one of the longer wave genes,
00:31:47.22 and in this case, would have the L/M hybrid instead of either the normal L or normal M gene.
00:31:53.11 In the case of the individual who gets the chromosome shown at the bottom,
00:31:57.13 recall that I just mentioned that only the first two genes in the array are expressed,
00:32:05.01 so this person would express the L pigment gene (that would be a normal gene),
00:32:08.23 but the M/L hybrid would now be expressed instead of the normal M pigment gene.
00:32:13.27 And that hybrid, we've come to realize, would in general have an absorbance spectrum
00:32:20.14 different from either of the two parental pigments.
00:32:23.19 So, this hybrid (and this is also true of the hybrid diagrammed here)
00:32:27.10 would be shifted in its absorbance spectrum, and the individual down here
00:32:31.01 would be an anomolous trichromat - one who had three pigments, but not the normal set of 3.
00:32:37.16 And this person would make different judgments
00:32:41.05 when asked to quantitatively mix different colored lights.
00:32:44.09 Now, more complex events are also possible
00:32:51.19 because many of the starting arrangements involve arrays that have multiple M pigment genes.
00:32:57.27 So, for example, if we start with an array that has two M pigment genes,
00:33:02.22 and a second array that has only one and envision the kind of recombination events
00:33:09.04 - in this case intragenic recombination events - that one might encounter,
00:33:13.17 we can, for example, see a meiotic product that has a hybrid gene,
00:33:18.18 an L/M hybrid instead of the L pigment gene, paired with an M pigment gene.
00:33:23.02 Now this person would also be an anomalous trichromat,
00:33:25.29 in this case, having replaced the normal L pigment gene with a spectrally-shifted hybrid.
00:33:32.01 And the person down below would be the reciprocal kind of anomalous trichromat,
00:33:36.12 now having a hybrid M/L and an unexpressed M paired with a normal L pigment.
00:33:43.07 These are quite common in the population - roughly 8 or 10% of human X chromosomes
00:33:50.19 have some sort of rearrangement that affects trichromatic color vision.
00:33:54.25 And, therefore, roughly 8-10% of males who carry those chromosomes
00:33:59.12 show that color vision anomaly.
00:34:02.18 There are also more subtle variations in color vision,
00:34:07.29 and let me show you the one that is the most common
00:34:12.02 and perhaps the most fascinating.
00:34:13.28 And that is a single nucleotide variation which divides the apparently color normal population
00:34:22.13 into two distinct perceptual groups.
00:34:25.25 This is in males, and among females, into 3 perceptual groups.
00:34:28.26 This is a single nucleotide difference in the L pigment gene,
00:34:35.20 which results in a single amino acid substitution,
00:34:38.13 it's a serine vs. alanine difference at position 180.
00:34:42.25 And, if you look at, in this psychophysical test, a mixing of red and green lights...
00:34:50.11 and here the result is plotted on the horizontal axis
00:34:52.25 as a ratio of red to red+green lights, and on the vertical axis
00:34:57.13 the number of individuals who had a given test result.
00:35:02.14 And here the mixture of red and green lights is being compared
00:35:06.04 to a standard, spectrally pure yellow light.
00:35:08.12 in just the way that Newton did this color mixing experiment.
00:35:11.27 What we see is that some people - let's look at the male curve at the top.
00:35:15.06 (males are at the top, females are at the bottom)
00:35:17.13 Some males require more red in the mixture to match the standard yellow,
00:35:23.29 others require less, and the distribution is bimodal.
00:35:27.18 And in the female set, there are some that test like the males who require more red,
00:35:33.24 some who test like the males who require less red,
00:35:36.22 and others who are in the middle - these are the heterozygotes in the middle.
00:35:40.01 And what we're seeing is the effect of this polymorphic variation
00:35:47.13 and its effect on the absorbance spectrum of the L pigment
00:35:53.04 which is now shown here, determined analytically by producing
00:35:57.09 the recombinant proteins and reconstituting them with the retinal chromophore.
00:36:00.24 So, the serine version - the one that has serine as position 180 -
00:36:04.08 is a little bit red-shifted (that's this upper curve) compared to the absorbance spectrum
00:36:08.28 of the alanine at position 180.
00:36:10.26 This is a 4 or 5 nm shift, and it precisely accounts for this variation in spectral sensitivity
00:36:18.19 between individuals who have either the alanine version or the serine version
00:36:26.11 of the L pigment.
00:36:28.00 So, even though we think, perhaps, simplistically as the world of trichromats
00:36:35.18 as a single unitary group of individuals with respect to their color vision,
00:36:41.07 that's not so - individuals are seeing the world somewhat differently
00:36:45.20 based on this genotypic different, and although each of them is internally consistent,
00:36:50.17 if you gather them together in a laboratory and ask them to quantify color mixing,
00:36:57.23 you'll see that they divide up into these distinct perceptual groups.
00:37:01.17 We saw at the beginning of this lecture, and also in the last slide,
00:37:05.11 how human visual pigments can be produced in the laboratory,
00:37:09.19 and their absorbance spectra measured directly after they are joined to 11-cis-retinal.
00:37:13.25 That's a wonderfully direct technique, and it gives us a very precise measure
00:37:18.28 of the absorbance spectra of those pigments.
00:37:21.03 Now, we'd like to ask, if we go back to the living human eye,
00:37:24.21 can we analyze the pigments' spectral sensitivities in the same precise way?
00:37:28.27 And, in this example, I will show how males who have only a single gene on the X chromosome
00:37:36.10 provide a perfect group for this sort of study.
00:37:39.04 So, these gentleman have lost, by homologous recombination,
00:37:43.26 all but one of the genes on the X chromosome.
00:37:46.14 What remains behind is either a normal L pigment gene
00:37:50.14 because they've lost the M pigment genes by homologous intergenic recombination,
00:37:55.04 or what remains behind is a single, hybrid gene,
00:37:58.21 containing either more or less of the parental contributions,
00:38:02.07 depending on the exact point of recombination.
00:38:04.16 But again, the other genes in the array have been lost by this recombination mechanism,
00:38:10.07 and these subjects are, in a sense, the perfect ones to study, because
00:38:14.03 they have only two cone pigments that one needs to examine,
00:38:19.25 the S pigment, and the remaining longer-wave pigment.
00:38:24.20 And, for simplification, one can design the study such that the S pigment
00:38:29.10 is really not in any way interfering with the sensitivity
00:38:33.00 measurements for the longer wave pigments.
00:38:35.08 So, one can use longer wave lights and selectively desensitize the shorter pigments.
00:38:39.09 So, in a sense, we have, in at least the longer wave end of the spectrum,
00:38:44.17 we have the very simplest arrangement one could possibly have.
00:38:47.06 We have someone who has just a single X chromosome, and on that X chromosome
00:38:50.29 is the single visual pigment gene, and we're going to ask, what is its spectral sensitivity?
00:38:56.07 And that is shown on this next slide.
00:38:59.08 This is determined by the method called flicker photometry.
00:39:03.22 It's a completely safe and non-invasive method.
00:39:06.27 The individual views a screen, on which is alternating about 20 times per second,
00:39:12.15 a standard light - a light of a standard wavelength (say 600 nm),
00:39:16.29 and with it, in alternation, is a test light whose wavelength is variable.
00:39:22.27 It could be 610 nm, 620, 630, 640, 650, and so on,
00:39:29.15 in 10 nm increments up and down the wavelength axis.
00:39:33.01 And what the subject has to do is to adjust a dial which controls the intensities
00:39:40.09 (the relative intensities) of these two lights
00:39:42.19 so that the sense of flicker is completely eliminated.
00:39:45.22 Now, let's ask how that could possibly happen.
00:39:48.21 After all, these are two differently colored lights - lights of different wavelengths
00:39:51.21 that are being presented alternating one with another.
00:39:55.02 How could the sense that they're flickering back and forth not be seen?
00:39:59.11 Well, remember that these subjects have only a single visual pigment
00:40:03.16 in the long wavelength end of the spectrum.
00:40:05.12 And, we've suppressed the S-cone pigment, so that it's really irrelevant to this measure.
00:40:10.20 And I should say, the rods have also been suppressed
00:40:14.07 by the high intensity of the lights being used here,
00:40:17.03 so for that one remaining pigment, if the number of photons captured per
00:40:22.01 second by these two different stimuli is the same,
00:40:26.03 then at that point, for the individual, there's no sense of flicker.
00:40:30.28 just like a continuous illuminant.
00:40:32.24 And that's the sense that is obtained when dial is set to just the right location.
00:40:38.15 I think you can appreciate that that gives us the relative efficiency of photon capture
00:40:44.03 for those two different stimuli.
00:40:46.12 And that, of course, is done for each of the different test wavelengths,
00:40:50.11 along the whole wavelength axis, and then we can plot out the full sensitivity curve.
00:40:55.20 And here they are. In green, we see the curve of sensitivity for the M pigment.
00:41:01.16 That is, these are individuals who have an L/M hybrid, but the L/M hybrid
00:41:07.05 consists almost completely of M sequences.
00:41:10.13 And, in particular, it consists of M sequences in those regions
00:41:13.14 which are critical for the spectral difference
00:41:16.09 between L and M pigments - so, de facto, these are individuals
00:41:19.12 who have only a M pigment and no L pigment.
00:41:21.21 And here is their sensitivity spectrum in green.
00:41:24.14 And then, in yellow and red are the two different variants of the L pigment:
00:41:30.22 one that has alanine at position 180, or serine at position 180.
00:41:34.20 I think you can appreciate that the serine at position 180,
00:41:37.08 just as it was in the in vitro spectrum,
00:41:40.12 is offset to the right, compared to the alanine at 180 version.
00:41:45.05 So, here, this is a very satisfying result. In the living human eye, we can see
00:41:49.07 the full spectral sensitivity of the pigments that underlie normal color vision
00:41:55.00 in the human population.
00:42:00.08 And, my only regret in describing this experiment is that most interesting of dichromats,
00:42:05.25 John Dalton, didn't live long enough to participate.
00:42:09.06 Dalton was born in 1766. I think if he had been born two centuries later,
00:42:15.11 he would have been an eager participant in this experiment.
00:42:18.12 Dalton maintained a lifelong interest in color vision and color blindness
00:42:23.18 and, even though he didn't participate in this experiment,
00:42:26.06 you may be interested to know that he made substantial contributions to
00:42:29.29 the science of color vision, and in particular, to understanding color blindness
00:42:34.02 even after his death, which seems surprising.
00:42:37.28 But, Dalton suggested, during his lifetime that perhaps the source of his color anomaly
00:42:44.24 was a selective filtering out of longer wavelengths of light
00:42:49.29 by some color medium, even in his vitreous - the fluid within the eyeball -
00:42:54.29 or perhaps in the lens, and so he stipulated in his will,
00:42:58.12 that when he died, his eyes should be cut out,
00:43:02.04 and a little hole cut in the back of the eyeball, and
00:43:06.26 by viewing the world through that hole, it would possible to tell whether the world was
00:43:11.05 being filtered by some sort of color filter within his eyes.
00:43:14.10 And so that was duly undertaken, upon his death,
00:43:18.09 and the result was that, except for a bit of yellowing of the lens,
00:43:21.17 which was to be expected in someone who is somewhat older,
00:43:24.29 the path was completely clear.
00:43:27.01 There was no filtering at all by the hypothesized color filter.
00:43:32.23 And, therefore, Dalton's color blindness must have been due
00:43:37.21 to some difference within the retina,
00:43:39.17 or possibly the brain. And, this was a major conceptual leap
00:43:46.01 in understanding the origins of color blindness,
00:43:47.29 and it would have been a substantial contribution if nothing more had been done,
00:43:54.22 but in fact, Dalton continued to make contributions to the world of color blindness research,
00:44:00.18 and 150 years later, his eyes, which had been preserved
00:44:04.25 by the Manchester Literary and Philosophical Society,
00:44:08.15 were examined by DNA analysis, and a small tissue sample was taken for that analysis,
00:44:14.25 and it was shown that Dalton was, in fact, missing the M pigment gene.
00:44:20.27 And, he was a dichromat of just the sort we've seen before
00:44:24.17 who has only an L pigment gene and an S pigment gene.
00:44:28.05 I think Dalton would have been very pleased with that result.

Part 4: The Evolution of Trichromatic Color Vision

00:00:02.06 I'm Jeremy Nathans. I'm a professor at the Johns Hopkins Medical School
00:00:05.28 and an investigator of the Howard Hughes Medical Institute.
00:00:09.09 This is the third of three lectures on the vertebrate retina
00:00:12.29 on its structure, function, and evolution.
00:00:16.12 And in this third part, we're going to focus on the evolution of trichromatic color vision.
00:00:21.02 Now, if we just consider the requirements that an organism needs to fulfill,
00:00:28.07 to evolve a new dimension of color vision...
00:00:31.21 And here, I'll use the word dimension in the way Maxwell used it.
00:00:35.22 We saw in the last lecture that Maxwell envisioned a 3D space - a color space...
00:00:41.22 a perceptual space, where each dimension corresponded to the degree of excitation
00:00:48.09 of a single type of photoreceptor cell.
00:00:51.06 If we ask what one needs to do to add an additional dimension to that color space...
00:00:57.27 And that's exactly the evolutionary step that we're going to consider in this lecture,
00:01:02.17 this transition from a simple or simpler dichromatic color vision
00:01:08.25 to the trichromatic color vision that we humans enjoy today.
00:01:12.01 If we ask what's essential to make that evolutionary step,
00:01:16.24 I think we can divide it into three parts.
00:01:20.12 First, the organism needs to evolve a new light-sensing visual pigment.
00:01:26.23 And this would be presumably encoded by a distinct gene.
00:01:30.12 The gene would encode a protein whose sequence differs from those of the pre-existing visual pigment proteins.
00:01:36.10 And then, that gene needs to be expressed in a second requirement.
00:01:43.29 It needs to be expressed in a distinctive set of photoreceptor cells.
00:01:47.17 Distinct from the visual pigment expression that was pre-existing in that organism.
00:01:53.14 And finally, if those two steps are fulfilled, the organism still needs one thing more,
00:01:58.27 and that is it needs the neural circuitry to extract the information from that new receptor
00:02:04.09 and compare it to the information that it's receiving from the pre-existing receptors.
00:02:08.28 As we proceed in this lecture, we'll consider each of these three steps,
00:02:13.13 and what we know about them, and how we think they might work.
00:02:16.15 Now, how do we know that an organism has color vision?
00:02:22.01 How do we know that an animal has color vision?
00:02:24.04 At some level, you have to ask the animal.
00:02:28.03 Of course, if the animal is a human, that's simple.
00:02:31.23 You can set up a color vision test that Newton set up, as we saw in the last lecture,
00:02:36.10 in which, for example, two spectrally pure lights are superimposed,
00:02:41.15 and they would match in appearance a third light.
00:02:44.26 And we can ask the person, do they match or do they not match?
00:02:48.02 And if they don't match, we can, for instance, give the person a dial and let them adjust the intensity
00:02:53.10 of one or another of those lights until a match is made.
00:02:56.21 That's the classical human color vision testing.
00:02:59.14 For a non-human animal, where one doesn't have the advantage of verbal communication,
00:03:04.24 we have to set up a test that will determine whether the animal can make those kinds of discriminations...
00:03:10.29 chromatic discriminations. And the test is of the sort shown here.
00:03:16.00 Now, this is a little monkey who has the task of deciding which one of these three images
00:03:23.00 looks different from the other two.
00:03:24.19 I should say this particular slide is a lab joke
00:03:27.23 We don't usually show them pictures of people... we show them just a blank panel of one or another color
00:03:34.06 or one or another intensity, and the animal decides which of the three panels looks different.
00:03:39.02 But here, this little lady is going to decide that this set of three panels has one that stands out,
00:03:47.23 (it's the one on the right, obviously - it's in color and the other two are in black and white),
00:03:51.20 and she'll press a little lever underneath the bowl that corresponds to that panel,
00:03:57.06 and she'll get a little candy reward in that bowl for figuring out the right answer.
00:04:00.26 And this sort of test will be repeated on many trials -
00:04:05.04 the picture that is different would be changed in its location
00:04:09.28 among the three different locations, and in particular, one would vary the intensity.
00:04:16.22 Now, remember, in the real world we don't use photos like this - portraits like this.
00:04:22.00 We're just using blank panels of a solid color.
00:04:25.11 But, we would vary the intensities of the colors at random.
00:04:28.29 And that's a very important point to make, because we want to make sure
00:04:32.27 that the animal is not cueing on intensity,
00:04:34.27 but is cueing strictly on color.
00:04:37.22 And the animal has to be smart enough and also have the color vision ability
00:04:43.01 to realize that the test can be figured out by determining which color looks different
00:04:50.08 in dependent of intensity differences.
00:04:52.12 Now, we're going to focus on evolution in the primate lineage,
00:04:58.17 and I want to just remind you that primates are quite diverse, although we think of them
00:05:02.19 as just one little subset of the mammals.
00:05:06.03 Here we have just three representative primates - gibbons on the left, a gorilla in the middle,
00:05:10.20 and a bushbaby on the right.
00:05:11.24 I want to just remind you that this diversity is really enormous.
00:05:17.23 The bushbaby on the right could easily fit into the palm of the hand of the gorilla in the middle.
00:05:22.18 And, within the primate lineage, color vision is also diverse.
00:05:27.24 The diversity correlates in an interesting way with the habitats of the primates.
00:05:34.26 Now, recall from plate tectonics that the continents were not separated for all time, as they are now.
00:05:44.05 But, South America and Africa used to be contiguous, and they've been moving steadily apart.
00:05:50.11 That's been going on for about 150 million years.
00:05:54.07 About 50 or 75 million years ago, there was a complete separation of all of the animal species,
00:06:02.03 in the sense that there was no communication whatsoever,
00:06:04.23 and the new and old world primates in particular were free to go their separate evolutionary ways.
00:06:10.09 And, as you'll see in a minute, they have, with respect to their color vision.
00:06:13.26 Now, we could ask, in the case of the evolution of trichromacy within the primate lineage,
00:06:22.17 what advantage is there? Why not stick with the dichromacy
00:06:26.16 which characterizes all of the non-primate mammals... all of the lower mammals.
00:06:30.28 And, although I don't think we really know the answer to this question,
00:06:34.24 there are some ideas which are quite plausible.
00:06:37.06 One of the most plausible is that it is easier to detect fruit among foliage,
00:06:44.02 and in particular, the ripeness of fruit, if one has trichromatic color vision.
00:06:47.22 Now, we've got a sense of that in the preceding lecture, when we looked at an image
00:06:52.22 of a fruit stand as seen by a normal trichromat, and various of the types of dichromats
00:06:59.02 in the human population.
00:07:00.09 Dichromats clearly have a more difficult time judging the ripeness of fruit,
00:07:04.26 and for animals that get a substantial fraction of their food from fruit, as many primates do,
00:07:11.10 this could be a significant selective pressure.
00:07:14.19 It's also possible that there are other visual tasks
00:07:19.01 which would be better performed with trichromatic color vision.
00:07:21.28 Some animals - some primates - are brightly colored, and it's a general pattern
00:07:28.07 that those animals which have bright coloration, be they birds or
00:07:31.16 fish - tropical fish, for example - also have excellent color vision to see that coloration.
00:07:37.26 So, the answer is not in, in terms of what the real selective pressures are for trichromatic color vision,
00:07:45.06 but it is widespread among primates, and we presume there is some positive selective value to it.
00:07:51.00 Now let's revisit a slide that we saw on the previous lecture, just briefly,
00:07:54.27 and this is just to remind ourselves that the basic mammalian arrangement,
00:07:59.07 as shown by this dendrogram in the center, for the mouse,
00:08:02.15 is one in which there are just two cone pigment genes.
00:08:06.20 There's a longer wave one (an M pigment in the mouse)
00:08:09.17 and a shorter wave one (it happens to be UV-sensitive in the mouse).
00:08:12.22 And, in the case of humans and other old-world primates, like gorillas and macaques, and so on,
00:08:19.03 we have seen a split in the longer wave pigment lineage - a very recent split -
00:08:24.27 so that now we have both L and M pigments in place of just a single pigment.
00:08:29.27 But those pigments are still very similar in their sequences.
00:08:33.05 Let's examine one of the earliest clues to the origins of primate trichromacy,
00:08:41.09 and that was gleaned by looking at the way in which color signals are analyzed in the retina.
00:08:48.21 If we look at the basic dichromat arrangement,
00:08:52.08 (this is the arrangement typical of non-primate mammals)
00:08:55.10 it's quite simple. They have a shorter wave cone (an S cone)
00:08:59.05 and they have a longer wave one, here I just call it M/L - the precursor of our M and L pigments.
00:09:04.27 And, these two cone types feed in an antagonistic way into a circuit which ultimately
00:09:15.20 is essentially a blue vs. yellow circuit - a shorter wave vs. a longer wave light circuit
00:09:23.06 which then goes to the brain. And, this might be, for example, an excitatory input
00:09:29.19 from the longer wave side and an inhibitory input from the shorter wave side,
00:09:33.11 and the output (the ganglion cell that is the output of this little circuit)
00:09:39.00 is essentially a differential analyzer for long vs. shorter wave lights.
00:09:43.07 That's a basic circuit that is present in all mammals.
00:09:46.04 We have essentially that same circuit in our retinas still,
00:09:49.05 and, as I'll show you in a minute, it has not changed very much.
00:09:52.05 But, if we look at the primate retina, at the old-world primate retina,
00:09:58.23 a retina like ours, we see that the circuitry has changed remarkably little.
00:10:06.18 So, for example, that circuit that I showed you on the previous slide has now just been
00:10:10.26 enhanced by adding the M and L cone inputs to the limb
00:10:16.24 that previously received the ancestor of those cones.
00:10:20.07 But, it makes no distinction between these two new cone types.
00:10:24.00 M and L cones are treated equivalently, as regards this short vs. longer wave circuit.
00:10:31.14 Ultimately, after summing them at the first stage,
00:10:34.27 they are compared in an antagonistic fashion (+ vs. -) in a second stage
00:10:40.09 and one gets essentially the same sort of output.
00:10:43.07 (A blue vs. yellow type output.)
00:10:46.22 There is a second circuit, though, which is involved in comparing M vs. L signals.
00:10:55.06 This circuit is again an antagonistic one - one of the cone signals is for example excitatory,
00:11:04.12 that's shown here as the L signal, the other inhibitory (the M signal).
00:11:07.13 But, there's a curious feature of this circuit which would appear to be a new circuit added on
00:11:14.11 and not present in the more primitive mammals.
00:11:18.03 And that is actually... it's not a new circuit... this circuit pre-exists
00:11:21.28 as simply a circuit involved in spatial vision, and it uses, in our ancestors,
00:11:30.09 a single cone type - the precursor of the M and L cones
00:11:33.25 and simply compares a signal, for example, in the center of a receptive field
00:11:39.10 to the signal in the surround - a signal that's completely achromatic
00:11:43.19 (a non-color signal) that tells the organism something about intensity differences
00:11:48.03 in one place vs. another place.
00:11:50.00 It appears that the same circuitry, essentially unchanged as far as we can tell,
00:11:55.08 has now been coopted to tell us something about color, as well as spatial intensity.
00:12:01.20 In fact, the two pieces of information - the two kinds of information - are now convoluted
00:12:08.05 within the signal. And, it's not entirely clear exactly where and exactly how
00:12:13.25 those signals would be deconvoluted.
00:12:15.20 But this observation - this finding that the circuitry within the retina appears to have changed
00:12:25.00 little if at all, in the transition from dichromat to trichromat vision is a striking one.
00:12:32.23 And it argues that the principal evolutionary event, and perhaps the only evolutionary event,
00:12:38.25 in making this transition was the acquisition of a new kind of cone.
00:12:43.12 Now, I want to just mention one thing about this peculiar circuitry.
00:12:50.15 I think you can appreciate there's an asymmetry here, in the sense that the
00:12:54.14 circuit does not compare S vs. M and S vs. L and L vs. M in a symmetric way
00:13:03.03 the way it could have.
00:13:04.21 And, because of this sort of peculiar pairwise comparison of S vs. M+L
00:13:11.23 and M vs. L, we have essentially a pair of axes for color comparisons.
00:13:18.22 One can think of them as sort of an X and Y axis space,
00:13:23.12 which has resulted in a peculiar psychophysical phenomenon, in which, when we view
00:13:31.15 the wavelengths of light which one can splay out in order of their wavelengths,
00:13:37.28 from short wavelengths at the ultraviolet end through the blues, the greens,
00:13:41.25 the yellows, the oranges, the reds, to the longer wavelengths,
00:13:45.10 essentially a linear physical stimulus where we go from short wave to long wave...
00:13:53.02 The visual system - our visual system - actually converts this linear scale into a circular scale.
00:14:02.00 And, I think we've all appreciated this from kindergarten, when we first started playing with crayons,
00:14:06.29 that if you draw the colors in what seems to be a natural order - natural in the sense
00:14:13.06 that the gradations between colors is minimal,
00:14:15.04 we naturally arrange them in the order red, orange, yellow, green, blue, violet, but then
00:14:25.29 we think violet and red are actually rather similar - there's sort of a purple transition between them,
00:14:31.23 but we naturally connect it into a circular pattern. Why do we do that?
00:14:35.19 Why do we make a circle out of what is physically a linear input?
00:14:40.11 And the answer is, because we're using this pair of axes to do our color comparisons
00:14:46.05 in the visual system, we are basically looking at red vs. green - that is the L vs. M dimension
00:14:56.20 in one comparison, and we're looking at the short wave (the blue) vs. the sum of red and green
00:15:02.28 (blue vs. yellow) in the other dimension.
00:15:06.02 And simply by dividing it in these two dimensions - red/green and yellow/blue,
00:15:11.08 we naturally create a two-dimensional system in which we psychologically
00:15:17.16 complete the circle. That's sort of an aside, but it's a natural outcome
00:15:21.27 of that way in which we analyze color.
00:15:24.16 Now let's talk a bit about the details of visual pigment sequence
00:15:29.29 and the implications of those sequences for the evolution of primate trichromacy.
00:15:35.20 In the previous lecture, we looked a little bit at the sequence differences that distinguish
00:15:42.05 the different human pigments, one from the next,
00:15:44.22 and we remarked on the striking similarity between
00:15:48.18 long and middle wave pigments (the L and M pigments).
00:15:51.20 And if we look at those comparisons in a little greater detail,
00:15:56.10 and then ask functionally, what's the significance of the various amino acids
00:16:00.19 that differ between L and M pigments,
00:16:02.14 what we see is that there are really only three major players in terms of the spectral sensitivity -
00:16:09.14 the spectral tuning, especially the differential tuning of L and M pigments.
00:16:14.04 That is, there's an amino acid at 277, which can either be phenylalanine or tyrosine,
00:16:19.15 at 285, which can be either alanine or threonine,
00:16:22.27 or at 180 which can be either alanine or serine - in fact, this is the one that's polymorphic
00:16:28.00 in the gene pool in the L pigment.
00:16:30.10 And those three together are responsible for virtually all of the spectral difference
00:16:38.12 between the L and M pigments.
00:16:40.21 If phenylalanine is present at 277, if alanine is present at 285, and alanine at 380,
00:16:47.23 the pigment will absorb at 530nm maximally.
00:16:51.24 If tyrosine is present at 277, threonine at 285, and serine at 180,
00:16:57.12 the pigment will absorb at maximally at 560nm. It'll be an L pigment.
00:17:01.17 And those three players are of interest
00:17:07.06 because they have been seen in both new and old world primates,
00:17:11.07 as we'll discuss in just a minute.
00:17:14.05 But at this point, I need to take a slight interlude and say a few words about the X chromosome
00:17:21.07 and the significance of X-linkage, because X-linkage turns out to loom very large in this story.
00:17:27.17 In all mammals, males differ from females in their chromosomal constitution.
00:17:33.07 Males have an X chromosome and a Y chromosome and females have two X chromosomes.
00:17:40.02 The genes on the X chromosome, of which there are many thousands,
00:17:45.12 must be expressed in both males and females at the appropriate level.
00:17:49.07 Yet, females are endowed with twice the number of copies of those genes as males have.
00:17:56.00 So, how does one avoid expressing twice as many copies of the corresponding mRNAs
00:18:03.19 in a female, compared to a male? If one did, that would be disastrous.
00:18:08.12 It would wreak havoc with development.
00:18:10.07 Mammals have hit upon a peculiar solution to this problem.
00:18:15.13 There is a selective inactivation of one of the two X-chromosomes
00:18:20.18 in each cell in the female body. Now, just for completeness, let me just say
00:18:25.22 it's not every gene on the X chromosome that obeys this rule, but most of them do.
00:18:29.16 And, what we see is that, at an early point in development,
00:18:35.12 when the embryo consists of on the order of a few thousand cells,
00:18:38.14 each of those cells decides with essentially a molecular coin toss,
00:18:42.18 which of the two X chromosomes will be inactive and which will remain active.
00:18:47.00 And the result is that all female mammals are mosaics,
00:18:50.01 as shown here for this cat - this female cat has an X-linked coat color variation.
00:18:58.04 She's a heterozygote. And, I think you can appreciate that there are patches of her coat
00:19:03.02 that are one color and other patches that are another color
00:19:05.16 that are sort of haphazardly mixed to some extent.
00:19:07.20 But, there are other parts where there are little chunks that are solidly one or the other.
00:19:13.14 And, this sort of mosaicism then can play itself out in the context of any number of X-linked genes,
00:19:22.09 and it turns out that it plays itself out in the context of the visual pigment genes
00:19:27.04 in new world primates in an especially interesting way.
00:19:30.16 New world primates, as it turns out, have trichromatic color vision
00:19:37.09 only for females. Males are dichromats among the new world monkeys.
00:19:42.27 And among the females, in those populations, only a fraction are trichromats.
00:19:48.17 (Roughly two-thirds.)
00:19:50.06 And, the way this works is that those new world monkeys
00:19:56.24 have a single X-linked visual pigment gene.
00:20:00.22 They don't have the gene duplication that we have.
00:20:03.17 But that gene is polymorphic in the population.
00:20:06.19 And, the different alleles encode different spectrally-sensitive variants of that pigment.
00:20:12.27 So, for example, for one typical new world monkey species,
00:20:18.17 we see that there are three alleles - one maximally sensitive at 535nm
00:20:24.10 (rather like our M pigment), another at 550nm (sort of midway between the M and L pigments),
00:20:31.03 and a third at 563nm (quite close to our L pigment).
00:20:36.01 By comparison, our old world arrangement involves two genes (two or more in some cases),
00:20:43.19 in tandem on the X chromosome, and they encode pigments of 530nm at peak absorption
00:20:50.26 and 560nm.
00:20:52.08 Now the striking finding, when one looks at the sequences
00:20:55.23 of new and old world monkey pigment genes,
00:20:59.20 is that the new world monkeys are using exactly the same three amino acid differences
00:21:05.29 to differentially tune the spectral sensitivities as we use
00:21:10.23 (we, old world primates use) to differentially tune the spectral sensitivities of our pigments.
00:21:15.19 So, just to recap, we use at these three critical sites, alanine, phenylalanine, and alanine
00:21:23.17 in our M pigment and serine, tyrosine, and threonine in our L pigment,
00:21:30.12 and the new world monkeys do exactly the same thing.
00:21:34.01 And the pigment that's halfway in between has an alanine and a phenylalanine and a threonine.
00:21:40.14 That is, it's sort of a hybrid between M and L, just as you might have expected.
00:21:45.20 And, this is quite a striking finding, because we know from work
00:21:50.03 both by site-directed mutagenesis and by sequencing the genes from different mammals,
00:21:55.00 that this is not the only way to tune these pigments.
00:21:58.05 There are other ways (there are other amino acids, other positions) that can vary
00:22:02.29 that can give rise to spectral tuning to either shorter or longer wavelengths.
00:22:08.07 And the fact that new world and old world primates use exactly the same amino acid variations
00:22:15.27 to tune their spectral sensitivities argues that this is not likely a coincidence -
00:22:21.19 not likely a convergence on the same solution, but rather that they arose from a common ancestor.
00:22:26.27 And we suspect that the common ancestor is basically the new world arrangement.
00:22:33.19 That is, this allelic variation pre-existed, we imagine,
00:22:38.16 in the earliest primates that pre-dated the separation of new and old worlds.
00:22:44.27 And, as shown schematically on this next slide,
00:22:47.25 we can envision, therefore, a timeline (time now going from left to right)
00:22:52.03 in which non-primate mammals, shown at the very top,
00:22:56.12 have gone their separate route and remain dichromatic,
00:22:59.22 and along the primate lineage (or the proto-primate lineage),
00:23:04.25 we see, at some point before the split of new and old world,
00:23:09.15 this polymorphism appearing in the X-linked pigment genes.
00:23:13.03 And then, with the split of new world (who have maintained that system)
00:23:17.23 and old world primates, there has been the acquisition at some point
00:23:24.24 in the old world primate lineage, of this gene duplication that we now enjoy.
00:23:30.01 This harks back to the previous lecture when I mentioned that we believe that the duplication
00:23:36.15 occurred between variant sequences that were already different.
00:23:42.08 That is, between a pre-existing L-like pigment and a pre-existing M-like pigment.
00:23:47.14 And, that's in a sense a bit of an exception to the way that people think
00:23:54.08 that gene evolution usually occurs.
00:23:55.12 The classic mode of gene evolution involves the duplication of a gene
00:24:00.15 to make an identical copy and then, over time, the acquisition of mutations
00:24:05.13 within that copy or perhaps as well the parental copy, to change their sequence and their function.
00:24:11.16 So that's essentially duplication followed by divergence -
00:24:14.26 that's certainly the classic mode of gene evolution,
00:24:19.01 and it's been documented in many, many instances.
00:24:22.12 This is really the reverse. This is divergence followed by duplication.
00:24:27.20 And, it begs the question of whether there might be other examples of this sort as well.
00:24:33.14 Now, this really, I think addresses the first of the three challenges
00:24:42.23 for evolving a new dimension of color vision,
00:24:44.27 that is, the production of a pigment that has a novel absorbance spectrum.
00:24:49.21 So, this has happened initially by allelic variation,
00:24:54.07 and now it's been cemented in the old world lineage by gene duplication,
00:24:57.04 and now, let's consider the second of the three requirements.
00:25:01.23 And that is a way in which the novel pigment gene could be expressed in a class
00:25:07.29 of photoreceptor cells distinctive from the class that is expressing the pre-existing pigments.
00:25:12.23 The first clue to how this might work for the L and M pigments
00:25:19.19 came from a study of those rare humans
00:25:23.15 who are missing the expression of both M and L sensitivities.
00:25:29.28 These individuals have what's called blue cone monochromacy.
00:25:33.07 They have normal blue cones with which they can see the world, and they have normal rods.
00:25:37.28 But, I think you can appreciate, since the rod system doesn't contribute to color vision,
00:25:43.13 and the blue cones are the only cone system that they possess,
00:25:46.14 there is no ability to compare the blue cone output to any other cone type.
00:25:51.11 And therefore, these individuals are monochromats - they have a fully monochromatic
00:25:57.05 color world - that is, just a one-dimensional world, in which there is essentially only intensity
00:26:02.00 and they have no sense of color at all.
00:26:05.01 So, this is true color blindness.
00:26:06.21 Now, it turns out that blue cone monochromacy is X-linked,
00:26:09.19 as are the common dichromacies and anomolous trichromacies.
00:26:13.09 And, in fact, blue cone monochromacy arises from sequence variation at the L and M gene locus.
00:26:20.18 And there are two basic mechanisms for blue cone monochromacy.
00:26:24.28 The first, which I'll describe here just for completeness,
00:26:27.04 which is really not that relevant to our story,
00:26:29.22 is the one that you might have guessed, knowing what you know thus far
00:26:33.24 about how this came about.
00:26:35.25 And that is that the wild type array (just one example is shown here at the top -
00:26:41.12 one L and two M pigment genes), can by the sort of homologous recombination
00:26:45.13 that we considered in the previous lecture, be reduced to a single gene within the array.
00:26:50.21 And that can either be an L pigment gene or an L/M hybrid gene.
00:26:55.07 This is no news at this point. The individual who carries those genes would be a dichromat.
00:27:01.24 But then, that gene could suffer an additional mutational event.
00:27:06.12 It could, like any gene, suffer some sort of mutation which disrupts protein function or structure,
00:27:12.08 and as a result, this individual would now, by virtue of these two mutational steps,
00:27:18.11 be missing all functional long and medium wave pigment genes.
00:27:24.05 And this is, in fact, seen in about half of blue cone monochromats.
00:27:27.28 Blue cone monochromacy occurs at a frequency of about 1 person in 100,000,
00:27:33.01 and about half of blue monochromats have the sort of mutational event I've illustrated here.
00:27:38.23 The other half, though, are more germane to the lecture today.
00:27:44.19 And that is, these individuals have, in one step, suffered a mutational event
00:27:51.15 which has eliminated the expression of the L and M pigment gene array.
00:27:56.00 And interestingly, in many of them, that mutational event, in fact,
00:28:00.28 has not affected the coding regions of those genes at all.
00:28:04.05 So, here's an example of a normal array - one L and 2 M pigment genes (this is a typical array).
00:28:10.09 And here, below, is the array with a deletion of sequences upstream.
00:28:18.02 That deletion, as far as we can tell from analyzing those individuals,
00:28:24.22 has completely eliminated the expression of all the genes in the array.
00:28:28.14 So there's some sequence - there's some critical sequence upstream -
00:28:31.05 which is involved in their expression.
00:28:33.06 This is not completely surprising - in the mammalian gene expression business,
00:28:38.13 this would be called an enhancer (a sequence at some distance from the genes that it controls)
00:28:45.29 and presumably it is involved in some sort of chromatin assembly
00:28:51.09 of promoter and enhancer sequences,
00:28:54.02 and their associated proteins, which control transcription.
00:28:57.12 Now, we know that this sequence is important in the control of gene expression
00:29:05.11 because if we produce a transgenic mouse which carries the human upstream sequences -
00:29:11.02 the promoter region of the L pigment gene, for example plus this upstream region,
00:29:16.22 which this enhancer-like region which we infer is important
00:29:20.18 from the analysis of blue cone monochromats,
00:29:22.23 we can see that combination of sequences can drive the expression of a reporter,
00:29:28.02 (in this case, the lacZ... the beta-galactosidase gene from E. coli)
00:29:32.05 in cone photoreceptor cells in the mouse retina - that's what these blue cells are.
00:29:37.18 These are now cells that have been stained with the beta galactosidase substrate,
00:29:43.19 X-gal, and in this transgenic mouse retina, that substrate is then converted to a blue precipitate,
00:29:51.02 indicating the location of cells expressing the lacZ or beta galactosidase transgene.
00:29:57.17 If exactly the same sort of mouse is produced with sequences
00:30:02.12 that are missing that upstream segment,
00:30:04.16 just the kind of deletion that a blue cone monochromat might have,
00:30:08.16 there's no expression whatsoever in the retina.
00:30:12.22 So, this sequence is involved in transcriptional control,
00:30:16.07 and the mouse experiment as well as the sequences of many mammals,
00:30:20.01 reveal that, in fact, this upstream region (which we're now going to call a locus control region
00:30:26.00 because it's controlling the locus of L and M pigment gene)
00:30:30.25 that this upstream sequence, in fact, is highly conserved among mammals.
00:30:34.16 It's present in dogs, cows, cats, whatever...
00:30:39.23 And, it's also present in us.
00:30:43.17 So, here's this sequence, just shown schematically as a little purple ball,
00:30:46.14 and, although we don't know the details, we presume that in the ancestral mammals,
00:30:51.23 the ones with just a single visual pigment gene,
00:30:55.04 or in new world primates, where they have a single gene, but the gene is polymorphic,
00:31:00.07 somehow it acts on or with the adjacent promoter to affect transcription in cone photoreceptors.
00:31:09.01 Now, this locus control region turns out to be outside
00:31:16.11 of the region which was duplicated in evolution to create the L and M pigment genes.
00:31:21.17 We have only (we, that is, old world primates)
00:31:27.27 have only a single copy, as do the lower, non-primate mammals.
00:31:31.29 And therefore, one presumes that this locus control region acts
00:31:37.15 on the L pigment promoter in L cones and then on the M pigment promoter in M cones.
00:31:45.06 That's a reasonable assumption, although I should say it's really more of a model
00:31:50.15 than a summary of data.
00:31:52.09 This model makes a simple prediction.
00:31:55.27 And that is, that the pairing of the locus control region either with the L pigment gene
00:32:02.00 in L cones or the M pigment gene in M cones
00:32:04.29 is a critical event in the development of those cones -
00:32:08.00 in the decision to become either an L or an M cone.
00:32:11.28 And, it's also the case that there's no other molecular difference
00:32:18.07 that we know about between those two cell types.
00:32:20.18 So that the possibility exists that that's the only decision that distinguishes them from each other.
00:32:27.11 And so, with that sort of idea in mind, let's just consider for a moment,
00:32:32.24 how that decision might be made and now we're really considering models
00:32:38.24 for which we don't have firm data, but I think they constrain the way
00:32:43.19 in which we would think about the process.
00:32:45.15 We can imagine that, in an ancestral dichromatic mammal,
00:32:53.08 for example, in the new world primates of today,
00:32:55.25 there's a single transcription factor, shown by this black ball - this ancestral transcription factor,
00:33:03.00 which mediates the pairing of the locus control region with the
00:33:07.27 single nearby promoter. There's really no decision to be made here...
00:33:11.20 There's only one gene that's available for expression in any given cell.
00:33:16.14 And, that pairing then would facilitate the expression of that one gene
00:33:23.09 in the one kind of longer wave cone cells that that organism had.
00:33:26.22 Now, in primates like us - old-world primate - where there are two different genes,
00:33:33.20 an L and an M pigment gene, we could, in the context of this... what I will call the standard model,
00:33:39.02 envision that there are transcription factors specific for either the L or the M cones,
00:33:44.25 and these are shown as either red or green balls, and that those orchestrate the pairing
00:33:49.25 of the locus control region with the appropriate promoter.
00:33:54.07 And that would be, I think, the sort of standard model that one thinks about
00:33:59.01 in the expression of genes in any particular set of distinctive cells,
00:34:04.06 say kidney vs. liver... that would be orchestrated
00:34:07.01 by cell-type specific transcription factors in those cases.
00:34:11.05 But, let's just consider a, perhaps simpler model,
00:34:15.04 and one that has a different set of predictions -
00:34:20.07 that's a stochastic model of cell type-specific gene expression.
00:34:23.17 And in this model, we can imagine that, in fact,
00:34:28.07 the ancient transcription factor - the ancestral transcription factor...
00:34:31.16 this black ball, which mediates the pairing in dichromat mammals,
00:34:36.24 and presumably in new world monkeys, in fact is the same transcription factor that we have.
00:34:41.09 Then, the pairing in L cones or M cones is simply determined by a molecular coin toss.
00:34:46.20 It can either involve pairing to the L pigment promoter or the M pigment promoter.
00:34:52.11 Once the pairing has occurred, presumably it's stable.
00:34:55.16 There are various molecular mechanisms one can envision for stabilizing it...
00:34:59.16 DNA methylation, for example, and other modifications.
00:35:05.09 But, that that initial choice would be made without reference to any other decision-making process,
00:35:12.02 and that once the choice is made, that is what has set the destiny of that photoreceptor
00:35:16.25 as being either an L or an M cone...
00:35:19.03 Now that's a very simple model. It makes a number of predictions.
00:35:26.14 And one prediction it makes is that the decision to express a gene in one cone or another -
00:35:34.29 either L or M - is actually a decision process that any mammal can effect.
00:35:40.25 Any mammal can do this decision-making. It doesn't need a special transcription factor
00:35:48.02 family that's specific to L or M cone types.
00:35:52.18 And so that has been tested - that idea has been tested
00:35:55.02 by placing a version of the human L and M pigment gene array, with the locus control region
00:36:03.01 into the mouse genome. So, here's construct that was inserted into the mouse genome.
00:36:08.07 There's a locus control region at the left end,
00:36:11.29 followed by an L pigment promoter, shown in red,
00:36:14.07 followed by one enzymatic reporter, alkaline phosphatase,
00:36:18.03 which produces a brown histochemical product in those cells in which it's expressed,
00:36:23.26 followed by an M pigment promoter and the beta galactosidase, or lacZ
00:36:29.23 reporter that we've seen before which gives a blue product in those cells in which it's expressed.
00:36:35.03 And this was inserted into the mouse genome at exactly one copy.
00:36:38.20 And then the retina was examined, and lo and behold,
00:36:43.11 it was observed that, not only was the expression not only exclusively in cone photoreceptors,
00:36:48.19 (that was expected), but that cones were either brown or blue,
00:36:54.12 and only very rarely were they both.
00:36:56.26 That is, the mouse appears to be quite capable of doing this molecular coin toss
00:37:03.16 and deciding which of the two genes to express
00:37:06.20 and expressing them in a mutually exclusive fashion.
00:37:10.08 The fact that new world monkeys have, what we know is a stochastic mechanism for doing this,
00:37:16.12 argues that stochastic mechanism in general are plausible mechanisms
00:37:21.15 for generating an L/M mosaic,
00:37:24.02 and extracting information, again in ways we don't fully understand,
00:37:29.04 but extracting information to generate trichromacy.
00:37:32.14 Now, let's take this one step further.
00:37:38.02 If it's possible for a mammal which has never experienced trichromacy to produce a mosaic
00:37:46.05 of L and M type cones in its retina simply by being presented with those genes in its genome,
00:37:52.11 let's ask, could that mammal, if given a new visual pigment gene, immediately use it for color vision.
00:38:01.02 And that experiment was done also in the mouse.
00:38:04.28 So, here is a mouse who has had in its genome,
00:38:11.20 a human visual pigment gene inserted - a long wave pigment gene inserted.
00:38:15.27 She's a female and she has on one of her X chromosomes a normal mouse pigment gene,
00:38:22.10 an M pigment gene which absorbs maximally at about 510 nm,
00:38:25.20 and she has on her other X chromosome a human L pigment gene which encodes a pigment
00:38:31.20 that absorbs maximally at about 560 nm.
00:38:33.29 And she is being given the mouse version of that task that we saw earlier on
00:38:38.29 for our little monkey friend. In this case, she has to decide which of these three panels
00:38:45.21 shown here has a light that is different from the other two.
00:38:49.16 And if she decides correctly, she gets a little drop of soymilk,
00:38:53.11 which she's drinking right now from above the panel which is different.
00:38:56.11 So there's a little tube here, which will produce a drop of soymilk
00:39:01.06 when she guesses the correct panel.
00:39:02.22 And, of course, the test is repeated many times over...
00:39:06.11 the locations of the different color lights are varied randomly among the panels,
00:39:12.03 and the mouse is asked in this sense, by measuring how many times it gets a drop of soymilk,
00:39:19.19 (which is measured electronically)... The mouse is asked, can you see the difference between
00:39:26.08 the colors even if we randomize intensity?
00:39:28.19 Well, when this test was done, in an initial test, the mouse was simply asked
00:39:34.20 whether it could see longer wavelength lights more efficiently
00:39:39.06 now that it had a human L pigment gene, than could a normal mouse.
00:39:43.15 And the answer is yes.
00:39:45.10 A mouse - the heterozygous mouse, with both M and L pigments, required less long wave light
00:39:52.25 compared to a control mouse with only M pigment. Not surprising.
00:39:56.22 But it just shows that the mouse L pigment works and can transduce signals
00:40:01.01 and tell the brain that it has captured light.
00:40:03.16 But the more telling experiment is here, where we're looking at
00:40:07.14 discrimination between lights of different wavelength.
00:40:12.15 So here we have a three-way forced choice task of just the sort we saw the mouse performing
00:40:16.25 and because there are three panels, and one of the three is correct,
00:40:21.08 an animal that cannot pass this test
00:40:24.13 will simply guess on every round and get it right, as shown by these data points down here,
00:40:30.13 33% of the time. So these are mice that cannot pass the test,
00:40:34.20 but they realize quite quickly that it's better to guess than to do nothing
00:40:38.21 because even if they guess, they'll get a drop of soymilk a third of the time.
00:40:42.27 But a mouse that has the engineered human pigment, as well as the normal mouse pigment,
00:40:48.06 (and these, of course, are females because it's on the X chromosome)
00:40:51.14 and which by virtue of X-inactivation has created a mosaic
00:40:55.00 of the different cone types within its retina
00:40:57.00 (a rather fine-grained mosaic, we now know)...
00:41:02.14 That kind of mouse can discriminate a test wavelength (shown here at variable wavelengths
00:41:07.19 from 500-600 nm) from a control, standard wavelength of 600nm
00:41:14.14 quite substantially better than chance.
00:41:17.08 So, roughly 70 or 75% of the time, when that test wavelength is 500 or 520 or 540 or 580 or 560 nm,
00:41:28.24 that mouse can tell the difference between the test wavelength
00:41:32.10 and the standard control wavelength.
00:41:35.14 And again, intensities have been varied so that the animal cannot cue on intensity.
00:41:41.05 It must be seeing the color aspect of the stimulus.
00:41:47.09 Of course, when the test wavelength gets very close to the standard...
00:41:51.06 when it's at say, 590nm, or its exactly the standard, 600nm,
00:41:56.17 then the mouse begins to fail the test.
00:41:59.08 And of course, when it's exactly the same wavelength, it fails completely,
00:42:01.07 and it's just guessing at this point
00:42:03.02 what the right answer might be.
00:42:04.19 But this experiment has a number of interesting implications for color vision evolution
00:42:11.03 and perhaps brain evolution, in general.
00:42:13.12 In the context of the evolution of trichromatic color vision, what it says is that
00:42:18.15 there is a plasticity that is built in naturally, in the mammalian brain,
00:42:23.18 that allows it to take an input - a novel sensory input,
00:42:28.04 and make sense of it... and make sense of it in the context of pre-existing sensory inputs.
00:42:33.05 And from the point of view of brain evolution in general,
00:42:37.15 I think this points to what is probably the winning strategy for brain evolution.
00:42:41.09 That is, to have the system built with a degree of plasticity that allows it to take advantage,
00:42:47.24 perhaps immediately advantage, of genetic changes that affect parts of the system,
00:42:52.25 say the front end system - the receptors themselves.
00:42:57.02 and to extract information that is useful, even though the only genetic change
00:43:03.17 has been in the receptor and not in the neural wiring.
00:43:06.03 And, of course, specifically, from the point of view of the evolution of trichromatic color vision,
00:43:11.12 this would argue that the first primate who acquired an additional genetic change
00:43:17.11 to create a novel pigment immediately saw a world of color that no primate had ever seen before.

Videos in this Talk
  • Part 1: Photoreceptors and Image Processing I
    Part 1: Photoreceptors and Image Processing I
    Audience:
    • Researcher
    • Educators of Adv. Undergrad / Grad
  • Part 2: Photoreceptors and Image Processing II
    Part 2: Photoreceptors and Image Processing II
    Audience:
    • Researcher
    • Educators of Adv. Undergrad / Grad
  • Part 3: Human Color Vision and its Variations
    Part 3: Human Color Vision and its Variations
    Audience:
    • Researcher
    • Educators of Adv. Undergrad / Grad
  • Part 4: The Evolution of Trichromatic Color Vision
    Part 4: The Evolution of Trichromatic Color Vision
    Audience:
    • Researcher
    • Educators of Adv. Undergrad / Grad
Speaker: Jeremy Nathans
Total Duration: 2:38:20
Recorded: August 2008
Educator Resources for this Talk
All Talks in Neuroscience

Talk Overview

In this set of lectures, Jeremy Nathans explores the molecular mechanisms within the retina that mediate the first steps in vision and discusses photoreceptors that mediate human color vision. The first lecture focuses on the structure of the light sensing receptors, the intracellular signals that are triggered by light absorption, and the ways in which the retina extracts information from a complex scene.

The second lecture focuses on the photoreceptors that mediate human color vision and the molecular basis for the common inherited anomalies of color vision.

The third lecture describes recent work on the evolution of trichromatic color vision in humans and our primate relatives.

Speaker Bio

Jeremy Nathans

Jeremy Nathans

Jeremy Nathans is a Professor in the Departments of Molecular Biology and Genetics, Neuroscience, and Ophthalmology at the Johns Hopkins University School of Medicine. He is also an Investigator of the Howard Hughes Medical Institute. He received B.S. degrees in Chemistry and in Life Sciences from the Massachusetts Institute of Technology in 1979, and Ph.D…. Continue Reading

Playlist: Molecular Neuroscience

Related Resources

Jacobs, G.H. (1998) Photopigments and seeing–lessons from natural experiments: the Proctor lecture. Invest. Ophthalmol. Vis. Sci. 39:2204-2216.

Rodieck, R.W. (1998) The First Steps of Seeing. Sunderland, MA: Sinauer Associates. 562 pp.

Nathans, J. (1999) The evolution and physiology of human color vision: insights from molecular genetic studies of visual pigments. Neuron 24:299-312.

Deeb, S.S. (2005) The molecular basis of variation in human color vision. Clin. Genet. 67:369-377.

Luo, D.G, Xue, T., Yau, K.W. (2008) How vision begins: an odyssey. Proc. Natl. Acad. Sci. USA 105:9855-62.

Additional Reading
Hecht, S., Shlaer, S., Perinne, M.H. (1942) Energy, quanta, and vision. J. Gen. Physiol. 25:819-840.

Baylor, D.A., Lamb, T.D., Yau, K.W. (1979) Responses of retinal rods to single photons. J. Physiol. 288:613-634.

Baylor, D.A., Matthews, G., Yau, K.W. (1980) Two components of electrical dark noise in toad retinal rod outer segments. J. Physiol. 309:591-621.

Jacobs, G.H., Neitz, M., Deegan, J.F., Neitz, J. (1996) Trichromatic colour vision in New World monkeys. Nature 382:156-158.

Smallwood, P.M., Wang, Y., Nathans, J. (2002) Role of a locus control region in the mutually exclusive expression of human red and green cone pigment genes. Proc. Natl. Acad. Sci. USA 99:1008-1011.

Smallwood, P.M., Olveczky, B.P., Williams, G.L., Jacobs, G.H., Reese, B.E., Meister, M., Nathans, J. (2003) Genetically engineered mice with an additional class of cone photoreceptors: implications for the evolution of color vision. Proc. Natl. Acad. Sci. USA 100:11706-11711.

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