Neural Summation to Improve Night Vision

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00:00:13.02 It's great to see you.
00:00:14.29 And I mean it.
00:00:17.02 Isn't it amazing that we can see each other
00:00:19.04 and that we can see the world around us?
00:00:21.09 Our sense of vision is crucial
00:00:23.21 to almost every aspect of our daily life,
00:00:26.02 be it helping us grab our cup of coffee in the morning,
00:00:29.07 finding our way to work,
00:00:31.08 or simply admiring the beauty of the world around us.
00:00:34.11 And it's not just us --
00:00:36.09 many animals use vision as their primary sense.
00:00:39.10 And because of the tremendous importance
00:00:41.22 of vision to human and animal life,
00:00:43.19 I've long been fascinated by this sense,
00:00:45.15 and I'm particularly intrigued
00:00:47.26 to understand how neural processing in the brain
00:00:49.26 shapes the visual experience.
00:00:53.05 Today, I'm going to take you
00:00:55.28 to the absolute limits of vision, to the night.
00:00:58.14 Consider this:
00:01:00.16 we depend on a sense that in turn depends
00:01:03.12 on the availability of light,
00:01:05.16 and it's challenged everyday by the setting of the sun
00:01:09.04 and the fading of the light.
00:01:11.09 I will show you how neural processing in the brain
00:01:13.06 can push the boundaries of the visual sense
00:01:15.18 to lower light intensities,
00:01:17.08 and the animal I've conducted my work in
00:01:20.17 is this beautiful hawkmoth,
00:01:22.22 the elephant hawkmoth, Deilephila elpenor.
00:01:25.08 It serves as a model to illustrate
00:01:28.05 how to bring light to the dark
00:01:30.22 using neural adaptations in the brain.
00:01:35.12 But before we dive into the story,
00:01:37.24 I think we have to address the elephant... hawkmoth in the room.
00:01:40.27 I said I'm interested in how neural processing in the brain
00:01:44.28 shapes the visual experience,
00:01:46.19 and now I'm telling you I'm working on an insect.
00:01:48.22 And face it, insects are not really famous
00:01:51.05 for the size of their brains.
00:01:53.19 Indeed, if you would call my hawkmoth pea-brained,
00:01:56.17 she would love you forever.
00:01:58.13 She'd wish she was,
00:02:00.14 because her brain is actually a lot smaller
00:02:02.04 than the size of a pea.
00:02:04.02 But it is this tiny size of their brains
00:02:06.06 that makes my work possible,
00:02:08.16 because it allows me to dissect the contributions
00:02:10.10 of individual neurons in this visual processing.
00:02:14.00 But I don't want you to think that I'm using hawkmoths
00:02:17.08 as an imperfect or reduced model
00:02:19.13 of our superior visual sense;
00:02:21.11 they can achieve impressive feats with their tiny brains.
00:02:24.00 They can actually see really well at night.
00:02:27.08 And I chose this particular hawkmoth
00:02:29.29 because it was the first species -- the first animal species --
00:02:33.26 that has ever been shown to have trichromatic color vision at night.
00:02:37.29 That's the kind of color vision we humans have,
00:02:39.29 and the moths can use it to still find their favorite flowers
00:02:44.05 by their color, even under starlight conditions.
00:02:47.19 If you'd asked me, or if I'd asked you to do that,
00:02:50.21 you'd horribly fail,
00:02:52.19 because we humans only see the world
00:02:54.15 as a shade of grays under starlight.
00:02:57.00 But what do the moths use their impressive visual abilities for?
00:03:00.27 Well, they use them to go about their business,
00:03:02.25 and that's zooming around on the lookout for flowers
00:03:05.02 that they can feed from.
00:03:06.28 And when they find a flower,
00:03:08.15 they hover in front of it to suck the nectar,
00:03:10.06 just like a hummingbird.
00:03:12.03 And in my work,
00:03:14.04 I wanted to understand how these hawkmoths
00:03:16.15 can see so well at night
00:03:18.14 and how their brains help them to achieve this.
00:03:21.14 Now, before we get there,
00:03:23.05 we first have to understand why is it actually
00:03:25.19 so hard to see at night.
00:03:28.25 And it all starts with light.
00:03:30.16 In order to see something, light particles, or photons,
00:03:34.14 have to arrive at the eye,
00:03:36.29 and they're picked up by neurons called photoreceptors.
00:03:39.09 These photoreceptors detect the photons
00:03:41.22 and send information about the light
00:03:43.29 to the brain.
00:03:45.10 In the brain, the information is further processed,
00:03:47.14 and an image of the outer world can be reconstructed.
00:03:51.10 Now, the problem with vision at night
00:03:53.29 is the low abundancy of photons,
00:03:56.14 the fuel of vision.
00:03:58.03 There can be up to 10 million times fewer photons
00:04:00.21 during a starry night than a sunny day.
00:04:03.25 If you want to imagine what that means,
00:04:06.00 imagine photons are raindrops.
00:04:07.26 And during the night, you experience a light drizzle of rain,
00:04:11.24 whereas during the day you're standing right under the Niagara Falls.
00:04:16.05 But there is a second problem with vision at night,
00:04:19.20 and that's the uncertainty of photon arrival.
00:04:22.21 Photons, just like raindrops,
00:04:24.24 arrive in a stochastic manner.
00:04:26.13 That means, at any given point in time,
00:04:28.25 you don't know how many raindrops or photons will arrive.
00:04:32.17 So, if you were to count raindrops in this orange area,
00:04:35.29 every second, let's say,
00:04:38.03 you'll be counting three raindrops, five, six, four...
00:04:42.14 well, you would be counting different numbers
00:04:44.10 at any sample point.
00:04:46.17 And this spread represents the unreliability,
00:04:48.13 or the stochasticity,
00:04:50.16 in raindrop or photon arrival.
00:04:53.08 Now, if you're taking your shower under the Niagara Falls,
00:04:55.25 and you're still counting raindrops,
00:04:58.06 then you would count a much higher number of raindrops
00:05:00.18 at any given sample time.
00:05:02.08 That means your average number of raindrops is higher,
00:05:04.15 but it also means that the uncertainty is lower --
00:05:07.18 the unreliability is decreased
00:05:09.25 when you have a higher average number of raindrops
00:05:11.26 or photons.
00:05:13.18 So, if we bring that back to the visual system,
00:05:15.27 during the day we have a high number of photons
00:05:18.20 and they arrive with a low uncertainty,
00:05:21.10 so that the brain can reconstruct
00:05:24.11 reliable images of the world.
00:05:26.14 Whereas at night, there are only a few photons arriving at the eye
00:05:30.29 with a high uncertainty,
00:05:32.28 which makes it hard for the visual system to reconstruct reliable images,
00:05:35.21 and if there are too few photons, and their arrival is too uncertain,
00:05:39.13 well then, vision cannot produce
00:05:42.04 a reliable information about the world anymore,
00:05:44.21 and it becomes useless.
00:05:46.28 So, to summarize, vision at night is challenged
00:05:49.00 by the low availability of photons
00:05:51.05 and the high uncertainty associated with their arrival
00:05:54.02 at the eye.
00:05:55.29 But how can visual systems overcome these challenges?
00:05:59.13 Well, if there are not many photons
00:06:02.02 available for the visual system,
00:06:03.28 it needs to collect more.
00:06:05.20 And how can you collect more photons?
00:06:07.16 Photographers have figured this one out --
00:06:09.21 you need better optics.
00:06:11.12 Larger lenses and larger apertures
00:06:13.15 collect a larger fraction of the available light.
00:06:16.06 And animals are doing just the same.
00:06:18.00 My elephant hawkmoth
00:06:20.12 has really gigantic eyes that collect a lot of light.
00:06:24.01 It actually has about 12,000 individual facets
00:06:26.28 in each eye
00:06:29.03 to collect as many of available photons as possible.
00:06:31.29 Now, the interesting thing with these hawkmoth is, though,
00:06:35.14 that when looking carefully at the anatomy of their eyes,
00:06:38.12 it has been shown that these eyes
00:06:41.06 should actually just be able to see up to about
00:06:44.12 dim moonlight intensities.
00:06:46.10 But the hawkmoth can still detect colors at starlight.
00:06:49.08 How is that possible?
00:06:51.17 And it's not just moths...
00:06:53.18 we have actually also found this difference in sensitivity
00:06:56.02 between the eyes and the brain
00:06:58.03 in cockroaches and nocturnal sweat bees.
00:07:00.26 So, how do we explain this gap in performance
00:07:03.01 between the eyes,
00:07:04.27 that stopp responding somewhere at dim moonlight,
00:07:07.27 and the animal's behavior,
00:07:09.17 that can still see at starlight?
00:07:11.29 Well, that's the hypothesis going into my work,
00:07:13.25 and it is that this gap is bridged
00:07:16.12 by neural adaptations in the brain
00:07:18.17 that enhance the sensitivity of the hawkmoths.
00:07:22.00 And you can imagine these neural adaptations
00:07:24.03 to be like digital processing of the images
00:07:26.14 that you've taken with your fancy optics,
00:07:28.22 and in post-processing,
00:07:30.25 you can change the brightness or the contrast of the images.
00:07:34.12 So, taken together,
00:07:36.17 visual systems can overcome the challenges of vision at night
00:07:39.20 by catching more photons with their specially equipped eyes or
00:07:43.10 -- and that's my hypothesis going into this work --
00:07:45.18 with neural adaptations that further improve the sensitivity.
00:07:50.00 So, that brings me to the first question in my work:
00:07:53.05 can I find any evidence for these neural adaptations
00:07:55.18 in the brain of hawkmoths?
00:07:59.12 And here, you see a 3D reconstruction of a brain
00:08:02.15 of a hawkmoth,
00:08:04.08 and the question is, where am I going to look for these neural adaptations?
00:08:07.02 What you see on the sides are
00:08:10.13 these two big yellow-red blobs.
00:08:12.09 We call them the optic lobes.
00:08:14.14 They're right under the eyes of the hawkmoths,
00:08:16.08 and they process visual information.
00:08:17.29 So, they're a good place to start looking.
00:08:20.16 And within these optic lobes,
00:08:22.21 there's a red structure that we call the lobular plate.
00:08:25.13 We know that neurons in this lobular plate
00:08:28.26 help insects to control their flight.
00:08:30.21 So, if the hawkmoths are still flying around
00:08:33.03 under starlight conditions,
00:08:35.00 neurons in this lobular plate must be sensitive enough
00:08:37.07 to support this behavior.
00:08:38.28 And they're likely sensitive enough
00:08:40.21 because they're benefiting from neural adaptations.
00:08:43.04 So, I targeted these neurons in the lobular plate,
00:08:47.02 and they're called lobular plate tangential cells.
00:08:50.00 But for the rest of this talk,
00:08:51.22 we can just call them flight control neurons, or flight neurons.
00:08:54.25 And what I did was to insert
00:08:58.01 tiny glass microelectrodes into these neurons
00:08:59.26 so I could pick up their activity.
00:09:01.18 And their activity would tell me about
00:09:03.14 how they're processing visual information.
00:09:06.09 And so, I did these recordings
00:09:08.03 while the moth was looking at a computer screen
00:09:09.26 and looking at a very boring TV program.
00:09:13.15 This consisted simply of black and white stripes
00:09:16.06 that were moving,
00:09:17.27 and this movement was meant to simulate
00:09:20.12 the movement a moth would see as it's flying through the world
00:09:22.14 and the environment is moving by its eyes --
00:09:24.20 just a very simplified version of that.
00:09:27.09 I could also dim my computer screen
00:09:30.07 to step through all the different light intensities,
00:09:32.10 down to starlight levels.
00:09:34.17 And very importantly, I also did all of these recordings
00:09:37.21 in the photoreceptors in the eye.
00:09:40.14 Because I told you before,
00:09:43.02 there are two ways to improve visual sensitivity.
00:09:45.06 One is by adaptations in the eye,
00:09:48.08 and one is by neural adaptations in the brain --
00:09:50.11 or so we hypothesize.
00:09:52.17 Now, I need to be able to separate these two contributions.
00:09:55.18 And by recording the photoreceptors in the eye,
00:09:58.04 I can tell the contribution of the eye.
00:10:00.14 And by recording the flight neurons,
00:10:02.09 I can tell what contributions there are in the brain.
00:10:05.09 So, let's get into it. What did I find?
00:10:08.22 Here, you see the responses of the flight neurons
00:10:11.24 to a range of light intensities.
00:10:14.11 You see their sensitivity responding to these black and white stripes.
00:10:17.10 We start at sunset intensities
00:10:19.15 and we go down to starlight intensities.
00:10:21.26 And what you see is that, from sunset intensities
00:10:24.03 up to about dim moonlight,
00:10:26.03 the flight neurons were very sensitive.
00:10:28.11 And then their sensitivity slowly dropped down to starlight intensities,
00:10:31.19 which is not surprising,
00:10:33.13 because there are fewer and fewer photons available,
00:10:35.11 and it's getting harder and harder for the visual system.
00:10:38.02 The important bit is to see that
00:10:41.00 there were still reliable responses,
00:10:42.21 even though they were weak,
00:10:44.13 at starlight intensities.
00:10:46.10 And that is the physiological basis for the behavior
00:10:48.11 we saw in the animals.
00:10:50.01 Now we have a neuron that is still responding,
00:10:52.16 and could explain to us why these animals
00:10:54.19 can still see and fly around.
00:10:57.03 Now, the interesting comparison
00:11:00.02 comes when we see what the eye is doing.
00:11:02.04 And in green, you see the sensitivity
00:11:04.11 of the eye in all of these light intensities.
00:11:06.15 And you see that already starting at moonlight intensities,
00:11:09.09 the eye is a lot less sensitive than the neurons in the brain.
00:11:13.18 And very importantly, if we look at starlight intensities,
00:11:17.02 the eye is not giving us any reliable responses anymore.
00:11:20.07 But the neurons in the brain are,
00:11:22.17 which is telling us they must get additional sensitivity,
00:11:25.06 and they must be getting it from neural adaptations.
00:11:27.14 They can't be getting it from the eye,
00:11:29.11 because the eye is not responding reliably anymore.
00:11:32.07 And actually, the difference
00:11:34.13 between the sensitivity of the flight neurons
00:11:36.01 and the sensitivity of the eye is about 100-fold.
00:11:38.12 So, the brain is about 100 times more sensitive
00:11:40.13 than the eye in this hawkmoth.
00:11:44.02 So, to summarize, I did find evidence
00:11:46.19 for neural adaptations in the brain of hawkmoths
00:11:48.20 and in the responses of these flight neurons
00:11:51.13 that are 100 times more sensitive
00:11:54.03 than the photoreceptors in the eye.
00:11:56.10 And that brings us to the next question:
00:11:58.10 how is this possible?
00:11:59.29 What neural adaptations are actually improving their sensitivity?
00:12:03.03 And I will walk you through my hypothesis
00:12:05.05 for what is happening in the brain
00:12:07.17 before I show you the data so that you know what to look for.
00:12:10.17 And that brings us back to the raindrops.
00:12:14.25 Remember, I told you one problem with vision at night
00:12:18.08 is the high uncertainty in the arrival of photons,
00:12:20.17 that we can also see in the spread
00:12:22.25 in our raindrop count.
00:12:24.10 Now, if we were to count raindrops in a larger area,
00:12:26.12 we would be counting more raindrops,
00:12:30.00 because we are summing more raindrops over this larger area.
00:12:32.15 So, we get a higher average count,
00:12:34.13 and I told you before,
00:12:36.16 a higher average count is associated with a smaller uncertainty.
00:12:39.23 So, by summing raindrops in space,
00:12:42.04 we're boosting our signal -- we're counting more raindrops --
00:12:44.26 and we're decreasing the uncertainty.
00:12:47.12 And we can achieve the same thing
00:12:50.10 by going back to a small area,
00:12:52.01 but integrating over longer timesteps,
00:12:54.25 so that, at every integration time,
00:12:56.28 we're counting more raindrops because the integration times are longer.
00:13:00.22 That means a higher average count,
00:13:02.29 and a reduced uncertainty.
00:13:06.04 So, using this spatial and temporal summation,
00:13:09.01 we can actually improve the sensitivity
00:13:11.13 of a very unsensitive and unreliable visual input.
00:13:15.29 Just like you can do in a camera,
00:13:18.08 you can increase the shutter time of the camera
00:13:20.07 and collect more photons
00:13:22.12 when taking your picture,
00:13:24.01 or you can make bigger pixels
00:13:26.10 and thus collect more photons per pixel.
00:13:28.09 But you see that that also brings with it a catch.
00:13:30.20 And that is that if we have longer shutter times
00:13:33.08 or larger pixels,
00:13:34.23 we're actually losing resolution.
00:13:36.23 So, if we open the shutter time of a camera,
00:13:39.19 or we're doing temporal integration,
00:13:41.17 then, if we have a fast-moving object like a car,
00:13:43.12 it will just be represented as a blur in the picture,
00:13:45.08 because we don't have enough sample points
00:13:47.17 to actually capture the movement.
00:13:49.17 And similarly, if we have large pixels,
00:13:51.11 we're losing the fine spatial detail.
00:13:54.16 So, performing spatial and temporal summation
00:13:56.23 means that we're losing spatial and temporal resolution.
00:13:59.02 And that's actually great for me as a neuroscientist,
00:14:02.02 because that gives me something to look for
00:14:04.15 in the responses of the neurons.
00:14:06.17 So, the loss of spatial and temporal resolution
00:14:09.16 is a signature I can look for
00:14:11.22 to know that there's spatial and temporal summation in the brain.
00:14:14.26 So, to summarize,
00:14:16.28 what I hypothesize is happening in the brain of the hawkmoth
00:14:19.19 is spatial and temporal summation
00:14:22.01 that improves sensitivity,
00:14:24.05 and I will be looking for a reduction in spatial and temporal resolution
00:14:26.25 in the responses of the neurons
00:14:28.27 to see if they're actually performing this summation.
00:14:31.26 So, let's get to it. What did I actually find?
00:14:36.05 In order to see if the neuron's spatial and temporal resolution changes,
00:14:41.14 I'm recording from the same flight neurons,
00:14:43.26 and now I'm showing them, still, black and white moving bars,
00:14:46.22 and to test their spatial resolution,
00:14:49.00 I'm showing them coarse bars and fine bars
00:14:52.14 to see the whole range of their spatial acuity.
00:14:57.11 And similarly, in the temporal domain,
00:14:59.09 I'm showing them very slow bars
00:15:01.10 and very fast-moving bars
00:15:03.11 to test their temporal range.
00:15:06.04 And you will see the responses of these neurons coming up, here,
00:15:09.19 and you will see on the x axis the spatial responses
00:15:13.26 and on the y axis the temporal responses.
00:15:15.15 On the x axis left side are the coarse responses,
00:15:18.26 the right side the fine responses,
00:15:21.07 on the bottom the slow, and on the top the fast responses.
00:15:25.03 And now, here is an example of a response
00:15:27.24 of such a flight neuron
00:15:29.22 to all these different spatial and temporal patterns.
00:15:31.17 And I just want you to focus on the part
00:15:33.21 where the neurons are responding the strongest,
00:15:36.07 and that is this dark red part in the center.
00:15:39.00 We will now step through the light intensities,
00:15:41.10 from sunset to starlight,
00:15:43.29 and I will indicate the spatial patterns
00:15:46.24 the neurons are responding to,
00:15:48.23 and you can see that indicated here by the white stripe.
00:15:51.11 So, as we're stepping from sunset,
00:15:54.01 down to moonlight intensities, and to starlight,
00:15:57.09 you can watch how the neurons' responses change
00:15:59.20 with respect to these spatial patterns.
00:16:02.21 And what you see is that as we go to lower
00:16:05.09 and lower light intensities,
00:16:07.08 the neurons are responding to coarser and coarser stimuli.
00:16:10.10 That means their spatial resolution is decreasing.
00:16:14.17 We can look at the similar responses,
00:16:16.29 but for the temporal acuity,
00:16:19.09 again indicated by the white stripe here.
00:16:21.12 And we start at sunset intensities.
00:16:23.12 We go down to moonlight, and to starlight.
00:16:27.12 And I'll show it to you again.
00:16:30.03 You will see that as we go lower and lower in light intensities,
00:16:33.27 also the neurons are responding
00:16:36.00 to slower and slower patterns.
00:16:37.19 That means their temporal resolution
00:16:39.26 is decreasing.
00:16:41.28 So, in the responses of the neurons,
00:16:43.22 we see that as we go to lower and lower light intensities,
00:16:45.26 their spatial and temporal resolution decreases.
00:16:48.19 And that's exactly what we expected to see --
00:16:51.13 that's our signature to find,
00:16:54.02 that there is spatial and temporal summation in their brain.
00:16:56.09 So, we can conclude from these...
00:16:58.19 from this data that hawkmoths
00:17:01.07 indeed perform spatial and temporal summation
00:17:03.09 in their brain,
00:17:04.26 and that it helps them to improve their sensitivity
00:17:06.27 by about 100 times
00:17:09.18 compared to what their eyes do.
00:17:11.07 Now, if you think about this for a moment,
00:17:14.06 this is actually a pretty simple trick.
00:17:16.15 The hawkmoth can see 100 times better
00:17:18.13 than it would be with its eyes alone
00:17:20.07 just by summing information in space and time.
00:17:24.15 And this simple and effective algorithm
00:17:26.25 is actually indicative of the insect brain in general.
00:17:29.24 I said in the beginning insect brains are tiny.
00:17:33.17 They only have about -- and then they're lucky --
00:17:36.17 a million neurons,
00:17:38.05 whereas we have about 86 billion.
00:17:39.29 That's the size of San Francisco
00:17:41.21 versus ten times the world population.
00:17:44.10 Yet insects find food, they court mates,
00:17:46.28 they find and avoid predators,
00:17:49.23 and they move through the world just like we do.
00:17:53.08 But they have a lot less neural capacity to do this,
00:17:55.29 and that means they have to find simple and effective solutions
00:17:59.09 to do it.
00:18:00.24 And that makes insects and their brains very attractive
00:18:02.18 to everyone who wants to develop technological solutions
00:18:04.29 that also can't have a lot of processing capacity,
00:18:08.25 such as autonomously moving cars or drones
00:18:12.13 that can't carry gigantic supercomputers with them.
00:18:14.15 And insect brains can be a blueprint
00:18:16.22 for algorithms controlling such technical devices.
00:18:19.26 One example that has been inspired
00:18:22.18 by the spatial and temporal summation
00:18:25.07 I found in the hawkmoth brain
00:18:27.08 is processing software for camera images
00:18:29.14 that helps to improve their sensitivity.
00:18:31.17 And I will show you how they work.
00:18:33.23 On the left side, here,
00:18:35.13 you will see a raw camera image of a camera
00:18:38.04 moving through a little scenery,
00:18:39.23 and on the right side you will see
00:18:41.23 the same image but post-processed
00:18:43.19 with this software that includes spatial and temporal summation.
00:18:47.25 And if you watch the camera feed,
00:18:51.14 you will see that actually this post-processing
00:18:54.08 has been able to regain
00:18:58.14 a lot more structures from the image,
00:19:00.21 and actually improve greatly the sensitivity of the scene,
00:19:03.24 just as spatial and temporal summation do
00:19:05.18 in the hawkmoth brain.
00:19:08.17 Seeing well at night is just one of the many things
00:19:13.00 insects can achieve with their tiny brains.
00:19:14.19 Others can migrate over thousands of kilometers,
00:19:16.27 memorize the colors and positions
00:19:19.14 of their favorite flowers,
00:19:21.19 or strike precisely using stereo vision.
00:19:23.23 So, next time you see one of our pea-brained friends,
00:19:25.16 take a minute to marvel
00:19:27.20 at these fantastic behaviors
00:19:29.14 that their tiny brains control.
00:19:31.17 Think about all these neural strategies
00:19:33.24 that are still there to be discovered,
00:19:35.27 just like the spatial and temporal summation
00:19:38.07 that the hawkmoths use to bring light to the dark.

This Talk

Speaker: Anna Stöckl

Audience:

Educators of H. School / Intro Undergrad
Student
Educators of Adv. Undergrad / Grad
Researcher
Educators

Recorded: May 2018

Talk Overview

Elephant hawkmoths have excellent night vision. But how can these insects see so well under low light conditions? In this talk, Dr. Anna Stöckl reveals that the secret behind their improved night vision is neural summation, a process by which neurons integrate different inputs or stimuli. She shows that these neural strategies boost the sensitivity of the elephant hawkmoth’s night vision, allowing the animal to see 100 times better than they would with their eyes alone. Stöckl also discusses the elephant hawkmoth as a novel model organism to understand neural processing.

This talk is part of the Young Scientist Seminars, a video series produced that features young scientists giving talks about their research and discoveries.

Speaker Bio

Anna Stöckl

Anna Stöckl received her bachelor’s degree in biology in Heidelberg and master’s in neuroscience in Munich. She then joined the group of Prof. Eric Warrant at Lund University in Sweden for her PhD work, where she investigated the neural strategies that allow hawkmoths to see at night. Anna used electrophysiological, anatomical and quantitative behavioural tools… Continue Reading