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Kate Jeffery on spatial cognition and grid cells

Episode 8 15.03.2015

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Season 2015

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How does the brain build a map of three-dimensional space when a full volumetric representation would be prohibitively expensive? Neuroscientist Kate Jeffery explains why the rat navigation system appears to favor flat maps stitched together into a mosaic , and what this reveals about the evolutionary trade-offs shaping spatial cognition. Subscribe for more from the Convergent Science Network podcast series. Kate Jeffery joins Paul Verschure and Tony Prescott at the BCBT summer school to discuss her research on how place cells, grid cells, and head direction cells handle the vertical dimension. Her laboratory has found that grid cells, which fire in periodic hexagonal patterns on flat surfaces, do not produce the same metric structure in the vertical plane. On a pegboard where rats move horizontally at different heights, grid fields extend into strips rather than grids. On a climbing wall where the body is parallel to the surface, something more grid-like appears. The implication is that the system maps space relative to the plane of the animal’s body rather than constructing a universal three-dimensional coordinate frame. The discussion addresses what this means for models of spatial cognition. Jeffery proposes a multi-planar model in which the brain tiles complex three-dimensional environments with locally two-dimensional map fragments, linked by some coarser three-dimensional information. She explains why this is an efficient evolutionary solution: a full 3D map would require vastly more neural resources, while a patchwork of flat maps supplemented with elevation cues handles most real-world navigation demands. The conversation also explores how the head direction system might cope with three dimensions , whether through a spherical attractor, three orthogonal ring attractors, or a simpler scheme that just tracks yaw on whatever surface the animal occupies. Key topics include the relationship between grid cells and contextual cues, the developmental sequence of spatial cell types, the influence of deep learning on thinking about modularity in the brain, and the practical constraints that ecology imposes on neural representations of space. Part of the Convergent Science Network podcast series from the BCBT Summer School.

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Brain Flat Maps grid cells spatial cognition Three-dimensional

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Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure

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00:00:00 - All right, so you can wear the headphones and we all look more professional,
00:00:03 - we feel more professional.
00:00:05 - This is the Convergent Science Network. We do the flight of the Concorde's robots.
00:00:10 - Reading researchers in the domain of neuroscience, brain theory and technology
00:00:15 - are interviewed by Paul Boucher and Tony Preston.
00:00:19 - No, it's you that's breathing. No, no, no, no. It is. No, no,
00:00:22 - it's you. No, no, no, no. Honestly, check.
00:00:27 - Okay, glad we settled that. Wear your headphones. This is Paul Vesure with the
00:00:31 - Convergent Science Network podcast together with my colleague Tony Prescott,
00:00:35 - BCBT 2015 in Barcelona. And we're here with Kate Jeffery.
00:00:40 - And Kate, you were presenting your work, which is actually very exceptional
00:00:46 - in the sense that you look at how three-dimensional space is represented in the brain.
00:00:50 - So how did you come to studying three-dimensional space from the perspective of the rodent brain?
00:00:55 - Are there flying rodents somewhere? There aren't flying rodents,
00:00:59 - but I had been studying two-dimensional space for a long time,
00:01:03 - as everybody else had been as well.
00:01:06 - And I guess we just started to get interested in the question of whether this
00:01:10 - map is really just a flat map, or whether it's actually got some three-dimensional
00:01:15 - structure. The world is three-dimensional.
00:01:18 - And we got into this line of work because I was talking with my friend and colleague,
00:01:24 - Andre Fenton, who had been thinking along similar lines in New York and he had
00:01:27 - built this fantastic spiral staircase and the idea had been to see if these
00:01:34 - place cells would produce place fields on the spiral staircase and whether there
00:01:37 - would be a vertical kind of structure to the place fields.
00:01:41 - And he didn't have time to run the experiment so he said, look,
00:01:44 - I'll bring it over to London and you can have a go with it.
00:01:46 - So I sent my student Madeline Veriotis onto recording on this thing.
00:01:50 - It was very, very difficult because the rats go round and round and round and
00:01:53 - round and it's like running up and down a five-floor building all day long,
00:01:58 - so they got quite tired, but she managed to get some really nice data.
00:02:01 - And we found that place fields indeed seemed to extend into the vertical dimension.
00:02:08 - And then around that time grid cells were discovered, and so it became a natural
00:02:11 - question, do grid cells also show some vertical structure?
00:02:16 - So she began recording grid cells on this spiral staircase, case,
00:02:19 - and another student that I had started recording them on this other piece of
00:02:24 - apparatus, a climbing wall.
00:02:26 - And we found these results that were quite surprising and yet consistent with each other,
00:02:32 - which is that the grid cells which fire in this periodic way in the horizontal
00:02:36 - plane didn't do that in the vertical dimension on either of those apparatuses.
00:02:41 - And so then that led us to do some thinking about why that might be and basically
00:02:46 - the whole research program took off from there. Okay.
00:02:49 - So you're in this laboratory, which also includes O'Keefe, who won the Nobel
00:02:56 - Prize for his work on the play cells.
00:02:59 - So the whole environment dedicated to understanding spatial cognition in the rat.
00:03:04 - Um so were you i mean if you're looking at the opportunities you have within that playing field.
00:03:14 - Which in some sense that the structure doesn't give you a
00:03:17 - lot of different cells to look at because you would have your grid cells you should
00:03:20 - play cells and you would have heading direction cells okay so
00:03:23 - um now in
00:03:26 - in the end if you look at the three-dimensional representation or in
00:03:29 - the red brain you focus very much on on the grid
00:03:32 - cells ultimately but but we're not there yet right but because
00:03:35 - that's not necessarily where you started so how did
00:03:38 - you work your way through that system and why did you make them the
00:03:41 - different choices that you did make well it it was partly just chance so we
00:03:46 - started with place cells because at the time we had placed cells and head direction
00:03:49 - cells um and jeff talby who had been working very intensively with head direction
00:03:55 - cells had already started looking at how they they behaved in three dimensions did some really
00:04:01 - beautiful work, which I didn't fully come to appreciate until we started to
00:04:04 - do something similar. I realised it's very, very difficult.
00:04:08 - So play sales are the natural place to start, really.
00:04:11 - But then grid cells came along, and the exciting thing about grid cells is that
00:04:14 - they have this metric component to their activity where they are actually encoding distance.
00:04:21 - So they are plausibly the fabric for this map, if you like, the thing that actually
00:04:28 - gives it the capacity to do navigational calculations and things like that.
00:04:33 - So when grid cells came along, it just became a very, very exciting question.
00:04:36 - Do they measure out distances in three dimensions? And if they do do that, how do they do that?
00:04:41 - And we know they need a compass to be able to do what they do in two dimensions.
00:04:45 - So does that mean they need a three-dimensional compass?
00:04:48 - We know that they form these beautiful hexagonal patterns on a flat surface.
00:04:51 - Does that mean that they form a three-dimensional lattice pattern?
00:04:55 - All sorts of questions came along following on from that discovery. So it was very exciting.
00:05:01 - So you started out in your talk saying that to navigate, you need a map,
00:05:05 - a compass, a way of measuring distance and a way to self-localize.
00:05:09 - But then as your talk progressed it
00:05:12 - seemed that maybe in the rat things aren't quite
00:05:15 - so clear-cut because like you
00:05:18 - say the grid cells when people first found them thought this is the metric this
00:05:23 - is telling me how far i am in space relative to some starting point but then
00:05:28 - as the evidence came in these grid cells also appear to depend upon contextual
00:05:34 - cues and you talked about boundaries reason you talked about odors.
00:05:38 - So, I mean, can you just explain how you think these other conceptual clothes
00:05:44 - are affecting the grid cells and what impact that has on this.
00:05:48 - Idea of how we navigate in space? Well, so contextual cues are things that characterize
00:05:54 - a space but don't have spatial information in and of themselves.
00:05:58 - So in the rat, we use the manipulations of the color of the environment or the
00:06:04 - smell of the environment or things like that.
00:06:06 - For ourselves, we could think of things like the decor in a room or something like that.
00:06:11 - So that helps you know which room you're in, but doesn't really tell you where you are in the room.
00:06:18 - My lab had been interested for quite some time in how those contextual cues,
00:06:22 - those non-spatial cues, modulate the activity of place cells.
00:06:26 - We had come up with a model that suggested that the place cells are getting
00:06:30 - spatial information from somewhere that's fairly raw metric information about
00:06:35 - boundaries and distances and directions.
00:06:37 - They're getting contextual information through another pathway,
00:06:41 - and those two pathways interact, act and the contextual cues act to select which
00:06:47 - of the spatial inputs a given place cell will respond to,
00:06:50 - so that was the model that we had at the time that grid cells were discovered
00:06:54 - and then when they were discovered it seemed like they might be the spatial
00:06:57 - component of this model so these are these things that seem to be spatial but not,
00:07:02 - much more than that as far as we could tell,
00:07:04 - so then we sort of became curious well what do they do when we change the context
00:07:08 - and we actually thought changing the context would have very little effect on
00:07:11 - grid cells because we thought Their job is just to mark out distances.
00:07:14 - Why should they care about the colour or the odour of the environment?
00:07:18 - So it was a little bit surprising when we found that they do actually respond.
00:07:24 - So now, what we're thinking, trying to put together what we've observed together
00:07:29 - with what makes adaptive sense, when you think about the evolutionary function of these things,
00:07:34 - my thinking is that the contextual cues indeed interact with the spatial cues
00:07:40 - to drive the place cells.
00:07:41 - Cells the place cells in turn are helping the grid cells know where to fire
00:07:45 - so we're thinking and it's not our idea it's something it's an idea that many
00:07:50 - people have contributed to but we're thinking that there's this to and fro interaction
00:07:53 - between the place cells and the grid cells where the grid cells help the place
00:07:57 - cells remain oriented in the middle of a big open space,
00:08:01 - and then the place cells help the grid cells to know which environment they're
00:08:04 - in using the context XQs and all the rest of it.
00:08:07 - And that makes sure that the grid cells will fire in the correct place for a given environment.
00:08:12 - So the two cell types are kind of helping each other out.
00:08:16 - And that is a little bit how people go about building simultaneous localization
00:08:22 - and mapping SLAM systems in robots.
00:08:25 - So I know that SLAM was actually informed by research on rodent navigation.
00:08:33 - David Reddish actually was pointing this out to us a few weeks ago.
00:08:37 - To what extent is your research now being influenced by these ideas from modeling
00:08:43 - and maybe even from robotics? David Reddish,
00:08:47 - Well, one of the things that's come along recently,
00:08:50 - which I admit to not knowing very much about, but I'm finding increasingly intriguing,
00:08:55 - are these deep neural networks that roboticists have started to use in some
00:09:01 - of their kind of robot models of navigation.
00:09:04 - And I was always sceptical about neural networks as a general kind of concept
00:09:10 - because a neural network is a very homogeneous thing to my untrained eye.
00:09:15 - Whereas we could see in the brain that it's very modular. There's a module for
00:09:19 - processing compass information, there's a module for processing distance and
00:09:22 - a module for processing this and that.
00:09:24 - It just feels to me like the architecture is very much more intricate than you
00:09:29 - get with a neural network.
00:09:30 - So for a long time I had felt that neural networks
00:09:33 - have their uses but they don't really explain how the brain works but it
00:09:37 - transpires with these deep neural networks that have many many layers that when
00:09:41 - you when you train them up and train them up and train them up they start to
00:09:44 - acquire some internal structure actually and when you probe elements of these
00:09:48 - things you do see things that look like they have subcomponents of the of the
00:09:52 - cognitive computation so I guess what I'm
00:09:56 - starting to take back from the robotic field is that maybe you can get something
00:10:00 - that looks like a modular system out of something that started out fairly homogeneous
00:10:03 - with some simple rules and a lot of parallel processing capability.
00:10:07 - And I think that's kind of an intriguing idea.
00:10:12 - I'm not sure how it would inform our experiments, but it certainly informed
00:10:15 - how I think the brain might come to do what it does.
00:10:19 - Right, so maybe it would inform experiments on the development of this system?
00:10:25 - I don't know if there's a big literature on that yet, is there?
00:10:27 - A literature is starting to develop. So recently, people from UCL and also from
00:10:36 - Trondheim in parallel have been looking at development of the glugurud cell
00:10:40 - and place cell in the head direction. system.
00:10:43 - And these cells come on stream very early.
00:10:47 - Interestingly, the grid cells seem to come on latest of all.
00:10:50 - And the first thing to come along is the head direction cells.
00:10:53 - Which makes a certain amount of sense, actually, because you can imagine that
00:10:57 - compass direction is primary.
00:10:59 - But we were starting to think that place cells emerged from the activity of grid cells.
00:11:05 - And then when these developmental findings came along, we started to think, actually maybe
00:11:08 - it's the other way around and maybe um maybe it's
00:11:11 - far more interactive than we had realized um i
00:11:15 - think the development development story
00:11:19 - assumes a certain amount of hardwired modularity so i don't think it's completely
00:11:24 - analogous to the deep deep learning networks quite yet but i think we will probably
00:11:29 - be moving towards some kind of hybrid system where the deep learning networks
00:11:32 - have a modularity to start with and then they take off and you probably know
00:11:36 - far more about this food than I do.
00:11:37 - Well, I think, yeah, we've had some interesting discussions about deep learning.
00:11:42 - We've managed to avoid this as a topic for this summer school until now,
00:11:45 - so I'm pleased it's come up.
00:11:47 - I know Paul has some strong views on deep learning.
00:11:51 - I thought I detected some raised eyebrows there, so it would be interesting
00:11:53 - to hear the pros and cons, actually.
00:11:56 - I think they're deeply overrated and overhyped in computational neuroscience,
00:12:02 - these ideas years of, let's say, uniform computational principles giving rise
00:12:07 - to cortical-like filter hierarchies are around since the late 80s,
00:12:12 - where people have been calling them objective functions.
00:12:15 - So we can think about building classifier hierarchies using.
00:12:19 - Let's say, the reduction of redundancy in the course of sparse coding,
00:12:25 - which Olshausen and Fields proposed first.
00:12:27 - Other people like Peter Koenig, myself, have been emphasizing issues like smoothness,
00:12:34 - like you can acquire a whole cortical-like hierarchy from V1 to hippocampus
00:12:40 - by hippocampal-like place cells by just optimizing the slowly varying features
00:12:46 - in your input and decorrelating them within layers.
00:12:48 - So this stuff we know for at least 20 years.
00:12:51 - And deep learning is a variation on that theme.
00:12:54 - It's just done by people who tell a somewhat different story and who are a little
00:13:02 - bit less informed about the neuroscience, so they have a tendency to overgeneralize.
00:13:06 - Because the point is, what does it mean to explain the brain?
00:13:10 - You can take inspiration from whatever you want. You can take inspiration from
00:13:13 - this cup of water. You can take your inspiration from a deep learning network.
00:13:17 - But explaining the brain also means that you have to account for the constraints
00:13:20 - that you find in that system.
00:13:21 - And if you look at the grid cell system as an example, just the anatomical structure
00:13:27 - has very specific properties that you just don't get for free.
00:13:32 - They have a very interesting dorsal ventral organization.
00:13:35 - They are organized also in sort
00:13:38 - of in the laminar sense in a very curious kind of way
00:13:41 - these are the kinds of things you must explain right.
00:13:44 - And that you can have a model that learns these grid-like properties
00:13:49 - in some way okay but that's that's not really explaining
00:13:51 - anything that tells you okay i can get a grid-like
00:13:55 - response in something but i think you have to
00:13:57 - also show that that you do it with networks that are
00:14:00 - informed and constrained by the known physiology and anatomy
00:14:03 - to me and that's usually not done for instance there are
00:14:06 - a number of so-called attractor models of grid cells where people
00:14:09 - show i can get grid-like responses if i
00:14:12 - have an attractor which basically means i have the liberty
00:14:14 - in the world to wire up a bunch of cells and now i have a
00:14:17 - grid-like response but that's not the grid-like response you
00:14:21 - have a grid-like response if you can show if you drive your map
00:14:24 - with the velocity vector that is continuous and relates
00:14:26 - to the movement of your agent that then you have a repetitive pattern
00:14:29 - of firing that follows the a good cell structure and that's
00:14:32 - a very different kind of of challenge than replicating
00:14:36 - a picture you took from a journal of neuroscience or from
00:14:39 - nature neuroscience in med lab it's not building a
00:14:42 - model yeah right so in this sense i'm we have done it
00:14:44 - we've seen it i don't think we are explaining much with it
00:14:47 - yeah no i agree that i think
00:14:50 - these one-size-fits-all models don't work because you know i i just think the
00:14:55 - brain is far too complex but i think you can learn you can get insights from
00:14:59 - some of these models that make you think about the system in a way that maybe
00:15:04 - you weren't attending enough to certain kinds of things.
00:15:09 - For example, you may have been postulating more complexity in the assumed wiring
00:15:14 - than is necessary to get the complex wiring.
00:15:18 - Kind of behaviors that you see like cell types with extremely
00:15:21 - specific properties for example and um and
00:15:24 - and so i think it just makes you think about things in a different
00:15:27 - way as a physiologist you for example i've started to think maybe maybe our
00:15:33 - parcellation of cells into these categorical um you know categories like like
00:15:39 - grid cells and play cells maybe we're being constrained by our predetermined modular model.
00:15:45 - And in fact, if we really approached it from a slightly different perspective,
00:15:49 - we'd see that there's a continuum of response types in this area.
00:15:53 - And when you look in entorhinal cortex, indeed, it's true there are grid cells
00:15:56 - and there are head direction cells and there are border cells,
00:15:58 - but there's everything in between as well.
00:16:00 - And so we may have been over-egging the modular side of things.
00:16:06 - Yeah, I think the point is that the interaction goes both ways.
00:16:09 - So the biologists learn from the people who are developing models and also AI
00:16:19 - systems about the power of learning.
00:16:21 - But also self-organization to restructure networks.
00:16:27 - Networks um and then actually there's
00:16:30 - a lot coming back the other way so that the um without
00:16:33 - necessarily acknowledging where it came from people are
00:16:36 - using ideas from the brain to develop these these new
00:16:39 - intelligent self-organizing systems so um i
00:16:43 - think we can all agree that that when
00:16:46 - we look at deep learning systems as they are now they're not very
00:16:49 - brain-like but what i think is has
00:16:52 - been shown in the last decade say is
00:16:55 - that with sufficiently powerful computers and the
00:16:58 - brain is a very powerful computer some simple principles
00:17:01 - can give you really powerful performance and
00:17:04 - that's now coming through in the world of technology so
00:17:07 - i'm just saying tony that we already knew that
00:17:10 - well you know i'll start at old house in the
00:17:13 - fields as a huge tradition look this
00:17:16 - is the the whole problem right we're always riding this wave of amnesia like oh
00:17:20 - i rediscovered something people i wrote up five years ago but
00:17:23 - but the time constant of collective memory in the field is just so short
00:17:26 - people all forgot about it but it's not just that i think that's deeply annoying
00:17:29 - people dismissed it because they said well these things are limited in terms
00:17:33 - of what they can do no they're not giving us the solutions we want let's go
00:17:37 - and look elsewhere and then you see 20 years later the computers just get more
00:17:41 - powerful and these things are delivering some of the things that Well,
00:17:46 - there's a dirty trick, though, that people don't talk about.
00:17:48 - The dirty trick is it's all supervised.
00:17:51 - It's all supervised learning. The brain doesn't have this luxury.
00:17:53 - There is no supervisor in the brain telling, oh, wait, no, no,
00:17:56 - this grid cell response is actually wrong. No.
00:18:00 - So, sure, great if you have the superpower of a supervisor who knows everything,
00:18:06 - like God is training you to be a world champion.
00:18:09 - Fabulous. But that's just not the luxury that the red brain has.
00:18:13 - So, this is my whole point about constraints. So, we must satisfy the pertinent
00:18:17 - constraints. And that's the discussion we have to have between the theatricians
00:18:20 - and the biologists is not like, oh, let me overwhelm you with my mathematosis.
00:18:24 - No, it's about what are the specific constraints that I'm satisfying that you
00:18:29 - have identified as experimentalist and what are the testable predictions I'm giving back to you?
00:18:33 - This is a dialogue that we need to establish. And that's not happening.
00:18:36 - People make a lot of noise about how fantastic this all is.
00:18:40 - And they can deliver even more cat videos to your doorstep with deep learning.
00:18:45 - And it's deeply uninteresting. I think that's a good point to go back into your
00:18:49 - talk because actually you were identifying some constraints because this whole
00:18:53 - question around how does the rat brain actually generate these different elements
00:19:00 - of a system that can map space.
00:19:02 - And there's this interesting question as to whether, for instance,
00:19:06 - they really have a full 3D map, even if they live in a 3D world.
00:19:09 - And you began by talking about the problem of just encoding head direction when
00:19:14 - you're moving on something that's not just a flat plane.
00:19:17 - And there I think you were also arguing that this isn't a system that can represent
00:19:22 - orientation sort of in a universal way.
00:19:26 - It's very much grounded in the ecology of the rat and the kind of life that it has.
00:19:31 - Yes, well we're still collecting data on this, it's early days, but certainly.
00:19:36 - What we're seeing in the grid cells and the head direction cells are sort of
00:19:39 - hints that the system is preferring to just make a flat map of local space.
00:19:45 - And that makes a lot of sense from an evolutionary perspective,
00:19:49 - because supporting a brain is enormously expensive in terms of energy.
00:19:55 - And to make a 3D model of the world, to make a full 3D model of the world,
00:19:59 - you need vastly more neural resources than to just make a flat model of the world.
00:20:04 - Of course, a flat model has a lot of limitations, and I did talk about some
00:20:08 - of those. You can only get so far with a flat map when you're on a hilly surface.
00:20:12 - It would be not very good for planning optimal routes across hilly terrain, for example.
00:20:18 - But if you take a flat map and then inject a little bit of three-dimensional
00:20:22 - information into it, then you might have something that works pretty well for
00:20:27 - all practical purposes.
00:20:28 - And that's really what the problem that RAT has to solve is how to practically
00:20:32 - get by in the world with the minimal expenditure of energy.
00:20:35 - Yeah, I think you distinguish different ways in which you could think about three dimensions.
00:20:41 - You could say, well, I'm interested in surface structure, so if it's undulations,
00:20:45 - I care about whether I'm going up and down.
00:20:47 - And then you talked about if I'm in the ocean or in the air,
00:20:51 - I'm interested maybe where I'm in a 3D volume,
00:20:54 - which would then, as you say, require more space to encode all of the information about that volume.
00:21:02 - And then you talked about some mixture models. And I think where you're going
00:21:06 - in your work is towards more of a mixture model. Is that right?
00:21:09 - Yes, I think so. So we call it a multi-planar model.
00:21:12 - I don't know if that's the best word for it, but the intuition is that the map
00:21:17 - of three-dimensional space, at least, I should qualify it and say that we're
00:21:21 - studying surface-dwelling animals like rats and mice and probably humans.
00:21:26 - So the map of a complex three-dimensionally, topologically undulating world is a lot of,
00:21:35 - we think of it as like mosaic fragments, each local one of which is two-dimensional,
00:21:40 - but which are related to each other with some three-dimensional structure.
00:21:45 - And we are in the process of collecting data about how that might work and for
00:21:49 - example how the head direction system could cope with a system like that and
00:21:54 - how it would avoid accumulating errors and so on.
00:21:57 - The acid test is really going to be what happens when we get rats to move through a volumetric space.
00:22:02 - And we've started these experiments in my laboratory at the moment where we're
00:22:06 - training rats to move through a lattice that's conceptually like moving through
00:22:10 - the branches of trees in a forest or something like that.
00:22:13 - So where the rat really can move in all three dimensions. and then we'll have
00:22:18 - to see what the grid cells particularly do in that situation right so but then um,
00:22:24 - If we now look at this situation from also this perspective of modularity,
00:22:28 - in some sense you can also say that head direction cells and grid cells have also great redundancy.
00:22:33 - Because in some sense, the grid cell gives you a spatial representation of the
00:22:37 - temporal signal you get from your head direction cells.
00:22:39 - It's the velocity vector that is driving the grid cells, right?
00:22:43 - So there's really redundancy there.
00:22:45 - So would you find cells that are, let's say, partially head direction cell and partially grid cell?
00:22:49 - Would you have mixtures? Well, it's certainly the case that there are so-called
00:22:54 - conjunctive cells, like grid cells that only produce their grids when the rat's
00:22:59 - facing in a particular direction.
00:23:02 - So I don't know if that was the kind of cell you meant, but they certainly have been well reported.
00:23:09 - But the other interesting thing is that if you deprive ordinary multidirectional
00:23:15 - grid cells of their place cell input, then what's left seems to be a head direction signal.
00:23:22 - So this is work from the MOSA lab where they inactivated the hippocampus.
00:23:27 - And so it looks like the head direction signal may be a sort of a fundamental
00:23:30 - input to grid cells, and that helps the grids to become oriented.
00:23:34 - But I'm not sure that you could function only with well-oriented grid cells
00:23:39 - in the absence of head direction cells, because of course there's the six-fold
00:23:43 - redundancy or replication ambiguity, I suppose is the word.
00:23:48 - You wouldn't know which of the six directions you were moving in necessarily
00:23:51 - if you didn't have a proper head direction system.
00:23:54 - But now your reference model for the head direction cells to now jump to three-dimensional
00:23:58 - space is the ring attractor, right? So basically you have a bunch of neurons
00:24:04 - that are coupled together in a ring.
00:24:06 - Every neuron is an encoding, a heading direction, and activity is just moving
00:24:10 - through this ring, exploiting the fact that heading directions also continuously change.
00:24:15 - So and then the question is, okay, how could I exploit such a ring attractor
00:24:19 - that works great in a planar space for a three-dimensional space?
00:24:23 - So your idea then is that the ring becomes a sphere, so I'm covering now also
00:24:28 - all elevation values, values?
00:24:30 - Or do you have another model in mind? Well, if the brain tries to form a fully
00:24:38 - volumetric 3D map, then I think you would want something,
00:24:41 - by extension from what we know in two dimensions, I think you would want a fully
00:24:45 - three-dimensional compass, which is to say a spherical attractor.
00:24:49 - Which also, unlike in two dimensions, also has to take into account the orientation
00:24:55 - of the body of the animal, because of course in three dimensions it can roll
00:25:00 - around the long axis of its body.
00:25:04 - So, theoretically speaking, I think that's what one would need.
00:25:07 - Now, we've not tried to model a spherical attractor,
00:25:11 - but it seems to me that it would be vastly more complicated than a ring attractor
00:25:17 - because of the problems that I talked about with things like the non-commutativity
00:25:20 - of rotations and all of these things.
00:25:23 - The problem of extracting azimuth from your rotations when the animal is not
00:25:29 - actually in a horizontal plane and all of these things that make it very complicated.
00:25:32 - So if we're thinking that this is a system that's trying to be economical,
00:25:36 - a more economical solution would be to just stick with your ring attractor
00:25:39 - and just to have it work on whatever plane you happen to be
00:25:41 - on regardless of its orientation and then figure
00:25:45 - out a way to map that signal back onto the horizontal and i i suspect that's
00:25:51 - what's happening with the so but an alternative might be that i just take my
00:25:56 - cardinal axis of movement and have single rings for those and maybe i take two
00:26:00 - populations that are tuned to this cardinal axis of movement and I have a little offset between them.
00:26:04 - And then via interpolation, I could actually extract all my possible heading
00:26:08 - directions in three dimensions.
00:26:11 - So why did you not consider this more, let's say, simplified version?
00:26:16 - Yeah, it's a possibility too. You mean like to have three orthogonal ring attractors?
00:26:20 - Yeah, exactly. Yeah, that would be a possibility.
00:26:21 - And Cynthia Moss has shown that
00:26:23 - that would account quite well to make a three-dimensional compass signal.
00:26:29 - I think that's entirely possible. It doesn't fully solve the problem because
00:26:33 - if you're on a plane that's not orthogonal to any of those three ring attractors,
00:26:42 - then you need to kind of map the yaw rotation that you're making onto one of those.
00:26:45 - So you've still got a transformation that's modulated by the angle between the
00:26:49 - surface that you're on and the three now ring attractors.
00:26:53 - So it's still not an entirely simple problem.
00:26:55 - On the other hand, the brain is quite good at solving non-simple problems.
00:26:59 - So I wouldn't rule it out at all.
00:27:01 - But now the intuition would be that we, so let's say, whether it's just a set of rings or a sphere,
00:27:07 - let's say we have now a 3D heading direction system, and then the intuition
00:27:11 - would be, okay, if these guys now drive my grid cells, I would have 3D grid
00:27:14 - cells, right? This is roughly the idea.
00:27:18 - But this is not exactly what you found. Right.
00:27:22 - Brad, you didn't literally find grid cells that are tuned in 3D.
00:27:27 - It was a bit more complicated than that. It's a bit more complicated than that,
00:27:31 - but we haven't done the acid test.
00:27:33 - So that's the experiment I mentioned a moment ago where the animal really can
00:27:36 - move freely in all three dimensions.
00:27:39 - So the experiments that we've done with grid cells in three dimensions have
00:27:42 - been with the animal on a surface that extends into the vertical dimension.
00:27:46 - Mentioned. And we don't know how that surface is constraining the signal and
00:27:52 - how it's producing a signal that might not be there if the rat was really able to move freely.
00:27:59 - So what we see is on a vertical surface, the pattern depends on the orientation of the animal's body.
00:28:05 - So if the animal is oriented horizontally, as it is on the pegboard where it's
00:28:10 - standing on pegs that stick out of a wall.
00:28:13 - So it's oriented horizontally, but it's moving up and down as well as in horizontal dimension.
00:28:20 - There we see that the grid cells don't seem to map out distances in the vertical dimension.
00:28:25 - So they're not, in that case, mapping out distances in the direction orthogonal
00:28:29 - to the plane of the body of the animal.
00:28:33 - If, on the other hand, the plane of the body of the animal is parallel to the
00:28:36 - wall, so the rat's actually walking around on the wall and climbing around on chicken wire.
00:28:42 - Now we see something that looks more like a grid. So it looks like there's some
00:28:46 - attempt by the system to perform odometry.
00:28:49 - So the relationship of the animal to the surface is really important,
00:28:54 - at least in these particular environments.
00:28:56 - Now whether that generalizes to a fully volumetric environment, we don't know.
00:29:01 - There was something interesting about the pack board result.
00:29:05 - So you have this wall with these little sticks sticking out,
00:29:08 - and the animal can make horizontal trajectories essentially across this wall.
00:29:14 - Essentially, what you would sort of see there is sort of a strip-like organization
00:29:18 - of the grid cell response, right?
00:29:21 - So does it imply that in that case,
00:29:25 - the medial entorhinal cortex is cutting through the three-dimensional plane
00:29:32 - a bunch of horizontal planes and saying, okay, actually, this is like a number
00:29:36 - of alleys that I'm running through,
00:29:38 - and I just ignore then the third-dimension part of it.
00:29:42 - I just map out every local horizontal stretch.
00:29:45 - Yes, I think that's one interpretation. That seems to be the likeliest interpretation.
00:29:52 - As to what it is about the dimension that the grid cell is not mapping out,
00:29:57 - so in the vertical dimension,
00:29:59 - I don't know, because we haven't done all the various control experiments,
00:30:03 - I don't know whether the system just doesn't like to do odometry that's not
00:30:08 - in the direction that the rat's running, which is a possibility,
00:30:10 - or if it's something specific to the direction that's orthogonal to the plane of the animal.
00:30:16 - So there are various experiments we need to do to distinguish between those possibilities.
00:30:20 - It may turn out the grid cells, they really only are interested in counting
00:30:24 - footsteps in the direction that the animal's running or something quite simple.
00:30:28 - Modulated, of course, by a running direction.
00:30:30 - But if the rat were to run sideways, we might also see that grid cell odometry fails.
00:30:35 - So we haven't tried that yet, nor backwards.
00:30:38 - I think going back to your suggestion that the grid cells are maybe being driven
00:30:44 - by the head direction cells,
00:30:46 - these results maybe make sense in the light of the study that you told us about
00:30:52 - where a rat is climbing, I think, on chicken wire around a sort of square pillar.
00:30:58 - And you're looking at the head direction cells when the rat is on either side
00:31:04 - of the pillar and how those changes it moves around the pillar. And you are saying.
00:31:10 - That it wasn't changing in a way consistent with having a full 3D compass,
00:31:19 - and it wasn't changing in a way consistent with having an entirely local compass.
00:31:23 - It was something that you called, I think, it was either locally global or globally local.
00:31:29 - Can you explain what you mean by that? Well, so what we found,
00:31:33 - which is very similar to results from Jeff Tauby a few years ago, show.
00:31:39 - But what we've done is show that this is an active modulation process rather than a passive one.
00:31:45 - So what Taobi's group showed is that if a rat walks from a horizontal surface
00:31:50 - to a vertical surface, then the head direction cells essentially remain unchanged.
00:31:56 - So as the animal walks towards the wall, the head direction cell that's active
00:32:00 - while it's on the floor will continue to be active as the rat walks up onto
00:32:04 - the wall and is now facing upwards.
00:32:06 - And then when the rat does yaw rotations on the wall, then the activity moves
00:32:10 - around the ring of head direction cells in the usual way.
00:32:14 - So what we have done is take that experiment a step further and shown that when
00:32:19 - the animal moves from one vertical surface to another differently oriented vertical surface,
00:32:24 - then the head direction cells actively rotate their signal by 90 degrees as
00:32:30 - the rat goes around a 90-degree corner.
00:32:34 - So that the...
00:32:38 - The representation is still essentially related to the representation that would
00:32:44 - be on the horizontal surface, but it's been updated by a movement that wasn't a yaw rotation.
00:32:50 - So in other words, non-yaw rotations around the vertical axis can update the
00:32:54 - head direction signal when the rat's not on a horizontal surface.
00:32:57 - Surface and the the consequence of that
00:33:00 - is that when the rat goes back down
00:33:02 - onto a horizontal surface then the signal has been appropriately
00:33:05 - updated such that it's consistent and still pointing
00:33:09 - the correct way so so what
00:33:12 - we have is a very simple rule basically for updating the head
00:33:14 - direction signal as the rat goes around vertical corners so what
00:33:18 - the rat really cares about is uh knowing
00:33:22 - where it is in the horizontal plane is that right
00:33:25 - i i would say that's a fair interpretation that's certainly
00:33:28 - our working hypothesis and that's the
00:33:30 - main thing if that then is the signal driving the grid cells then does
00:33:35 - that perhaps explain some of the grid cell results because now the head
00:33:37 - direction cells don't care so much about where you
00:33:40 - are vertically but they care a lot about where you are horizontally so inevitably
00:33:44 - the grid cells are going to be coding much more strongly for the horizontal
00:33:49 - uh dimensions well so the the head direction cells I'm not sure we could say
00:33:55 - that they don't care about the vertical encoding because,
00:33:57 - in fact, the specificity of the signal on the vertical wall is just as good
00:34:01 - as it is on the horizontal.
00:34:05 - Maybe it's not vertical versus horizontal, but it's something about the surface
00:34:09 - that you're on, but then how that surface is relative to the true horizontal,
00:34:14 - which you know through gravity.
00:34:16 - So those are the things that the head cells care about, is that right?
00:34:19 - Well, yes. Yes, I think that ultimately the consistency that the system is trying
00:34:23 - to maintain is consistency in how they encode azimuth.
00:34:28 - So in other words, horizontal direction, compass direction, essentially.
00:34:31 - So I think that these rules about updating the signal for rotations around the
00:34:38 - vertical axis, I think the function of those rules is to maintain horizontal consistency.
00:34:44 - Now how that maps to what the grid cells are doing, it's not quite so clear,
00:34:48 - because the head direction signal is very much the same on the vertical wall
00:34:53 - as it is on the horizontal floor.
00:34:55 - But the grid cell signal seems to be quite different. So the scale is expanded.
00:34:59 - The space between fields is relatively much larger than it should be.
00:35:05 - And something has changed about the oscillatory activity, the theta rhythm and so on.
00:35:12 - Something is different about how the grid cells are computing distances on the
00:35:15 - wall. and yet the head direction cells are just doing what they,
00:35:19 - should do. So there's a slight dissociation there, which we haven't yet understood.
00:35:22 - But isn't that Tony's point?
00:35:23 - Because I think the point you're making is that if I'm on this vertical wall,
00:35:27 - I'm still mapping my head direction cell back to a horizontal plane.
00:35:32 - So now I get also sort of rounding errors and I get imprecisions in that mapping
00:35:37 - because my head direction cell is still believing we're moving around on this
00:35:41 - horizontal plane, but actually it's a vertical plane.
00:35:44 - So that means if this is now the key driving input of my grid cells,
00:35:48 - this also will get distorted.
00:35:50 - And that might lead to sort of a collapse or a remapping of the grid cell response.
00:35:57 - I think this is what you had in mind, Tony, roughly.
00:36:01 - That wasn't quite my idea, but my thought was that if you had a perfect 3D compass,
00:36:06 - the grid cells might automatically develop a nice volumetric mapping.
00:36:10 - But I don't know how we would test that. Okay, it's the other extreme then, okay.
00:36:15 - Because for instance, Kate, you showed this really beautiful experiment where
00:36:19 - you had these animals crawling around this cube on the chicken wire.
00:36:24 - And then what you showed is that as the animal turns the corner in these two
00:36:28 - vertical planes, goes from one vertical plane to the other vertical plane,
00:36:31 - there's a very rapid shift of the heading direction response with 90 degrees.
00:36:35 - So you would expect that if you then look in that condition to the grid cells,
00:36:39 - that there should be some massive change in the response of the grid cells.
00:36:41 - Well, we might predict that all other things being equal, that the grid pattern on the.
00:36:51 - The two vertical walls would be 90-degree rotations of each other.
00:36:56 - But there are all sorts of qualifications to that. One is that,
00:37:00 - as I've mentioned, the pattern of the grid cells on the vertical wall is so
00:37:05 - different that it's not even clear that it's a hexagonal close-packed array.
00:37:09 - So we don't even know that we could determine what the orientation was of the
00:37:14 - grids. The other thing is that we've shown that the grid cells are somewhat
00:37:19 - sensitive to context information.
00:37:21 - And of course, the east wall and the south wall could, to the system,
00:37:26 - seem like different contexts.
00:37:27 - So maybe the grid cells would just do something entirely different.
00:37:31 - So it's a little difficult to predict what we would see. And I'm not sure how
00:37:36 - easy it would be to interpret what we saw.
00:37:39 - So right now, we're really analyzing this three-dimensional representation of space.
00:37:44 - Or the representation of three-dimensional space from the heading direction system perspective.
00:37:51 - So the heading direction response is, again, very much predicated on what your
00:37:55 - vestibular system will tell you.
00:37:56 - So it's maybe this inability to really map out three-dimensional space accurately
00:38:01 - in the rodent dependent on just getting signals that are less reliable or more
00:38:07 - noisy or less precise in the vertical plane as opposed to the horizontal plane.
00:38:11 - So it's just a matter of the sensory front end, the sensory apparatus,
00:38:15 - not providing you with the information to actually have an accurate head direction
00:38:20 - response in the third dimension.
00:38:22 - Well, the vestibular system is pretty good at providing information.
00:38:27 - So, you know, it's sensitive to angular and linear information in the various different directions.
00:38:34 - But the hypothesis that we're toying with at the moment is that the vestibular
00:38:43 - signals that are feeding into the grid cell system that normally work on the horizontal plane,
00:38:49 - they comprise all of the semicircular canal information,
00:38:52 - so all of the rotational information,
00:38:55 - together with linear information from the otolith organ.
00:38:59 - So our kind of working hypothesis is that on the vertical surface,
00:39:02 - now the otolith organ, which is sensitive to acceleration in the horizontal
00:39:07 - plane and normally has the gravity vector orthogonal to that,
00:39:11 - is now in a different state of alignment and that perhaps the system copes with that.
00:39:18 - And rather than developing a whole new way of processing the signal,
00:39:23 - just says, let's just do without the otolith signal.
00:39:25 - So we'll just work with the semi-circular canals and let's forget the whole
00:39:29 - linear acceleration thing.
00:39:31 - So that may be why the grids are expanded on the wall because they're missing
00:39:35 - one of their vestibular inputs. Right, that would give quite a bias,
00:39:37 - right, because if we now go to these annoying bats that show actually a three-dimensional
00:39:42 - representation in their place cell system and they're.
00:39:47 - Do they have an oscillate type canal also running in the orthogonal axis, in the vertical axis?
00:39:55 - Is that what helps them to develop a three-dimensional representation?
00:39:58 - I think, as far as I know, the vestibular system is pretty similar. Okay.
00:40:04 - And we don't fully know the details about the three-dimensional nature of their representation.
00:40:09 - So Nakamulanovsky and his group have done some really beautiful work showing
00:40:14 - that place cells seem to form place fields that pack of volume,
00:40:18 - as you would predict for a three-dimensional map.
00:40:21 - The head direction cells are sensitive to all three of the directions, but not equally.
00:40:28 - So there are many more azimuth-sensitive cells than there are cells sensitive
00:40:33 - to pitch, and there are very few cells sensitive to roll.
00:40:37 - There are a small handful that are sensitive to combinations of all three of
00:40:42 - those angles, so they are true 3D compass cells but there are very few of them.
00:40:48 - So I don't think that even the bat which moves a lot through three-dimensional
00:40:52 - space really has a true volumetric compass that works evenly in all three of the dimensions.
00:40:58 - I think it's still biased towards encoding the horizontal plane.
00:41:02 - The story for grid cells and bats we're waiting with bated breath to see what
00:41:06 - happens there and I think that'll raise some very interesting questions.
00:41:10 - I'm looking forward to those data coming. But then if we take a rat and we sort
00:41:16 - of glue it to a drone and we have the rat fly around in the lab to get its food.
00:41:22 - Would you predict that it would develop three-dimensionally tuned play cells as the bat?
00:41:30 - So you're asking a question about experience and is experience enough to create
00:41:34 - a 3D bat? Right, because apparently the periphery is, as far as we know, rather comparable.
00:41:39 - Yes, yes. So I guess the answer would have to be, I don't know.
00:41:45 - No, we want the prediction. Well, so we have been raising rats in a fairly three-dimensional
00:41:51 - environment so as to have subjects that are as 3D competent as we possibly can.
00:41:56 - So they spend all of their time climbing through climbing frames and up and
00:42:01 - down things, and they're pretty competent. They don't fly, and we've not put
00:42:05 - them on a drone, but in all other respects, they're pretty good at this volumetric.
00:42:10 - Navigation. But we don't see any difference in the encoding of their neurons.
00:42:16 - So if I had to guess, I would say that I don't think experience is going to
00:42:20 - create a 3D map if there isn't one there already.
00:42:22 - But now we know that maybe in the rats that you're allowing to develop in a
00:42:29 - 3D spatial space in the lab, they could still get away with just slicing it
00:42:33 - up in many horizontal planes in which they operate, no?
00:42:36 - Right, because they move around on surfaces. Exactly. So if I glued a rat to
00:42:41 - another bat or to a drone, I have linear motion, but then really in three-dimensional
00:42:45 - space. Yeah, I don't know.
00:42:47 - I think we'd have to do it. I mean, there are hand-waving arguments for why
00:42:51 - they might be able to do that.
00:42:53 - One of them being that we all evolved from fully 3D-competent animals,
00:42:57 - so fish, which move around in a volumetric space.
00:43:01 - And it's quite possible that all of this evolved eons ago and that it's become
00:43:07 - somewhat vestigial, or at least we don't use it.
00:43:09 - Surface-dwelling animals like us and rats and mice and things don't use it,
00:43:15 - but it's still there. So that's possible.
00:43:18 - In support of that, there's research on astronauts, or observations on astronauts,
00:43:24 - not formal studies as far as I know.
00:43:26 - No, but observations of astronauts find that in the first few days of weightlessness,
00:43:30 - they tend to try and form a flat map, if you like, where they reference their
00:43:37 - knowledge of the layout of the environment to a notional floor.
00:43:42 - So they define a nearby surface as the floor and then the surface opposite it as the ceiling.
00:43:46 - And then if they drift across that space in their weightlessness and find themselves
00:43:50 - too close to the ceiling, then they suddenly reorient their sense of direction
00:43:53 - and now the ceiling becomes the floor.
00:43:55 - So that suggests to me that they didn't start out with a three-dimensional map
00:43:59 - because why would you have this reorientation if you were really quite happy
00:44:04 - floating around in a space and just able to encode X, Y and Z with equal facility?
00:44:10 - On the other hand, this disorientation abates over time, as far as I know.
00:44:14 - So it's possible that they do develop some competency.
00:44:17 - Whether it's a kind of a kludge, or it's just a kind of a hack,
00:44:22 - not really a fully three-dimensional map, but one that functions like one,
00:44:26 - I think we'd have to do probably some electrophysiology on them,
00:44:30 - and I'm not sure we'd be allowed to.
00:44:32 - But yeah, it's an open question. It's an interesting question,
00:44:34 - I think, whether we have the capacity to do that, even if we don't use it.
00:44:39 - One thing I think is quite potentially interesting is when we move to virtual
00:44:42 - reality and we become more competent in navigating virtual environments where
00:44:47 - we don't have the same constraints,
00:44:49 - could we engage a fully three-dimensional map and could we even create a four-dimensional
00:44:54 - one or more higher dimensional representations?
00:44:57 - And that I think would be interesting to play around with. Are we being taken
00:45:02 - in too much by this concept of a map?
00:45:06 - Because yeah, if I have a good map, I have a compass, I can count steps,
00:45:11 - I can know where I am in space, but now...
00:45:14 - Uh if i if i'm in
00:45:17 - the wrong place if i'm not where i'm on my map i can get lost but usually
00:45:20 - there are ways of recovering from that for instance uh
00:45:24 - i can look and see where some tall building is
00:45:27 - or if i'm you know a bird i can
00:45:30 - look at the constellation of the stars or where
00:45:32 - the sun is in the sky or i can tell from the direction
00:45:35 - the wind is blowing you know if i'm a seal maybe
00:45:38 - it's water currents you know thermal gradients uh gravity
00:45:42 - of course is always there as a queue so these systems
00:45:45 - give us queues that will help us localize
00:45:49 - ourselves or at least get something like a homing vector even when
00:45:52 - the uh the hippocampal map fails so
00:45:56 - maybe is it possible the hippocampal map is is just part of this bigger navigation
00:46:01 - system and it's one of the things that are constraining our choices about going
00:46:05 - the world but we're not tied to it in such a strong way yeah i think that's
00:46:10 - exactly right i think there are you know there's quite a lot of research to
00:46:13 - suggest that there are multiple spatial systems and what we've been calling map-based navigation,
00:46:19 - which really refers to using constellations of cues to extract distance and
00:46:24 - direction metrics for navigation.
00:46:27 - I think that's only one of several different strategies, and you've mentioned some.
00:46:31 - So beacon navigation, for example, where you just head towards the nearest tall
00:46:34 - building or whatever it was.
00:46:36 - You may not necessarily know where you are, but you can see where you need to
00:46:39 - get to, and so you just head towards it.
00:46:43 - Or remembering sequences of left-right turns that you have to make.
00:46:48 - I think a lot of our navigation is this stimulus-based, route-based navigation,
00:46:54 - where we're not really thinking about where we are within the global scheme
00:46:57 - of things and not really computing directions, but remembering patterns of behavior anchored to landmarks.
00:47:02 - When I go to work in the morning, I drive to the corner and I turn left.
00:47:05 - I don't think about the overall direction I'm going. I just know my route, you know.
00:47:11 - So, yeah, I think the brain has multiple systems, and I think it's possible
00:47:16 - to exploit that for various purposes.
00:47:18 - For example, I think it's something that robotics could usefully do,
00:47:21 - is to have these parallel multiple systems that interact.
00:47:26 - And one of the things that's emerged from animal studies is that certainly the
00:47:31 - root-based and the map-based systems seem to operate almost,
00:47:35 - I mean, not in opposition, but in an either-or fashion.
00:47:38 - So you tend to use one or the other. You switch between them.
00:47:41 - But wouldn't there be some of these other mechanisms could be involved in stitching
00:47:45 - together these sort of globally local hippocampal maps?
00:47:49 - Because what I got from your talk is that if I'm exploring on a piece of flat
00:47:55 - ground, I have a map for this.
00:47:58 - But then if I decide to run up this tree, I'm into another map.
00:48:02 - But I need then to know how these maps fit together in sort of bigger space and.
00:48:08 - It seems less likely that I have a global map, maybe at a coarser scale,
00:48:12 - but maybe I'm using other sets of cues to try and integrate between these local patches.
00:48:18 - Yeah, I think there's probably more than one way of creating a larger scale patchwork map as well.
00:48:25 - I think you've identified one, so it is quite possible that there could be these
00:48:31 - root-based ways of stitching together your behavior, if you like.
00:48:36 - As for stitching together
00:48:39 - the actual fragments of the map to make a larger map i think there
00:48:43 - you would want a brain system that had some notion
00:48:46 - about about direction because ultimately even if you're using a coarse more
00:48:53 - topological map where you've not got fine-grained distance information you still
00:48:58 - need some general idea of which direction to go and so i think it's more
00:49:03 - likely that it would be some brain system that's interacting with the head direction system.
00:49:07 - So we think that route-based navigation depends on the striatum,
00:49:10 - which is involved in controlling behavior in response to stimuli in the environment.
00:49:16 - But I think a more spatial, a more globally spatial system, more connected with
00:49:22 - the compass system, would be likely for the larger scale map.
00:49:25 - And we've been looking quite a lot at retrospinal cortex and other cortical
00:49:30 - regions, which talk to the hippocampal system, but they also talk to the head
00:49:36 - direction system and also quite a lot to other sensory systems.
00:49:40 - It seems to be quite a waypoint for many converging information streams.
00:49:45 - What about humans? Because I know some that have no sense of direction.
00:49:49 - I mean, I don't know about myself, but yeah. Members of my family,
00:49:53 - for instance, have no idea which direction they should go in.
00:49:56 - There's a lot of interesting research on humans.
00:49:59 - It's quite an enormous literature.
00:50:01 - And people vary a lot in their spatial capabilities.
00:50:06 - And one person who's been doing some interesting work on this is Eleanor McGuire,
00:50:10 - who has been looking recently.
00:50:14 - She's done a lot of work over many years looking at different aspects of navigation.
00:50:18 - But recently she's been looking at how people encode landmarks and use them in navigation.
00:50:23 - And she's found that people vary in their ability to decide how permanent a
00:50:29 - landmark is, which is an odd thing to have doubts about.
00:50:33 - But apparently people vary along this continuum.
00:50:36 - And she finds that people who are not very good at specifying how permanent
00:50:39 - landmarks are also not very good at navigating.
00:50:42 - They perform quite poorly in tests. and interestingly the brain structure that
00:50:46 - lights up when people are deciding about permanence of landmarks is retrospinal cortex.
00:50:52 - Now that's a brain structure that my lab has been interested in recently because
00:50:55 - it's very interested in landmarks and it has a lot of head duration cells and
00:51:00 - we think that it may be doing the job of processing landmarks and deciding to
00:51:06 - what extent they're useful to the head duration system and then attaching them
00:51:09 - or not attaching them to the the head direction signal.
00:51:12 - So this is work that's just beginning, but I think we're slowly starting to
00:51:16 - build up this picture that it's not really all about the hippocampus.
00:51:19 - The hippocampus is the core of a much bigger system.
00:51:24 - That's also what you argued towards the end, right?
00:51:27 - Because, okay, so in some sense, I guess you were hoping to find a clean three-dimensional
00:51:33 - tuning of, let's say, the grid cells, although we didn't really talk too much
00:51:38 - about the place cells in this context.
00:51:39 - But in some it doesn't come out so clean right and
00:51:42 - then you propose that maybe these are like the grid
00:51:45 - cells could be could be thought of as cylinders that cut through
00:51:48 - it of a uniform way the third dimension but
00:51:52 - the physiology was maybe not always that clean
00:51:55 - with respect to then still a grid-like structuring of the response because i
00:52:00 - remember on some of the walls you get a huge clustering of the response in one
00:52:03 - corner or the other corner right right and then And the point you made is that
00:52:08 - the modulation of this response is strongly dependent on gravity and the orientation of the body.
00:52:14 - So how does this now come in to the modulation of this grid cell response that you recorded?
00:52:22 - Well, the hypothesis, and it's really only a hypothesis, we don't have any data
00:52:27 - on the role of gravity, but the hypothesis is that the grid cell system The
00:52:32 - system wants to create a grid,
00:52:36 - and the grid is essentially a flat thing, and it needs to decide what surface
00:52:42 - is it going to lay its grid on.
00:52:45 - And normally, in a normal environment,
00:52:47 - we were just walking around on the floor, then it uses the floor.
00:52:49 - And of course, when we record rats in the laboratory, normally they're walking
00:52:52 - around on the floor and we see grids on the floor.
00:52:55 - But it's when we start to have rats walking around on things that aren't the
00:52:58 - floor, that we start to see this slight modulation of what the grid cells are doing.
00:53:03 - And the simplest explanation for
00:53:06 - the patterns we see is that the grid cell system sometimes chooses the wall
00:53:10 - as its reference plane and it's trying to produce a grid on the wall and sometimes
00:53:15 - it uses the floor even if the rat's walking around on a wall if the rat is actually
00:53:20 - oriented horizontally and the floor is beneath it and it can see it.
00:53:24 - So the pattern that we see on the pegboard with the stripes,
00:53:28 - we think that's happening because the grid cell system has decided to use the
00:53:31 - floor as its reference plane and not the wall.
00:53:33 - Even though the rat's climbing on the wall.
00:53:38 - That kind of simple model has to be qualified because of this finding that when
00:53:42 - the rat is walking around on the wall, we don't see these neat grids.
00:53:46 - Possibly they're neat grids if we could have a huge wall and have the rat walk around on it.
00:53:51 - I'm trying to persuade my PhD student, Julia, to do that experiment.
00:53:56 - It would be a very difficult one. Okay.
00:53:58 - So then the grid cells, the story
00:54:02 - is not finished. In which perspective is tuning in the third dimension.
00:54:06 - However, I think there's this strong conviction by everybody in the field that
00:54:11 - they do represent a metric, right?
00:54:13 - They really contribute to having a metric representation in this case of distance.
00:54:17 - But then in some of the heuristic is often like, well, you know,
00:54:21 - grid cells give you this great representation of space.
00:54:23 - And with those, we can build place cells. So that's fantastic.
00:54:27 - This was the original intuition, and then it came out that also if you look
00:54:32 - at development, it actually not occurs in that order.
00:54:34 - It's much more that the play cells help you to structure grid cells.
00:54:38 - So that raises a question about
00:54:40 - also the directionality of the information processing in this system.
00:54:45 - So you could also argue, look, if we take the cortical sheet,
00:54:48 - then at one end we will have this entorhinal cortex running across it,
00:54:54 - and then from there hangs our hippocampus.
00:54:56 - So we have now this interface between hippocampus and cortex through entorhinal
00:55:00 - cortex where we have this metric.
00:55:05 - It could also actually be a metric that helps Cortex to read out what the hell
00:55:08 - is going on in hippocampus.
00:55:10 - Is that an option you would consider? Yes. Yeah, I think the system is unlikely
00:55:16 - to have a directionality.
00:55:18 - I think the system is very bidirectional, very highly interconnected, in fact.
00:55:25 - The only exception to that really is this relatively one-way flow of information
00:55:30 - through the hippocampus itself.
00:55:33 - But even then, there are multiple shortcuts. So information coming from the
00:55:36 - entorhinal cortex to the dentate gyrus also takes a shortcut to CA3 and information
00:55:41 - going to CA3 also takes a shortcut to CA1 and so on.
00:55:45 - But then the output goes back to the entorhinal cortex and then it goes back out to cortex.
00:55:50 - So I agree that I think that the information flow goes both ways.
00:55:56 - I think all the structures depend on each other to some extent.
00:55:58 - So I think that the play cells, they're getting lots of information other than the grid cell input.
00:56:05 - In fact, you could knock out the grid cell signal and the place cells still
00:56:08 - produce quite nice fields, so they're quite capable of forming place fields.
00:56:12 - But I think what we will find if we do the relevant experiments is that in that
00:56:18 - situation, there's not really metric information.
00:56:21 - So the animal, for example, can't path integrate.
00:56:24 - And in fact, there's some work from Marseille that shows that if If you lesion
00:56:30 - into a rhino cortex, then animals lose the ability to calculate distances properly.
00:56:34 - So I think that right now,
00:56:39 - I would probably favor a model in which the place cells form a sort of a,
00:56:46 - what I think of as almost like a pixel map of the environment.
00:56:50 - So they sort of respond to the constellation of sensory cues that are present
00:56:55 - at each particular point in space.
00:56:58 - Together with a grid cell input, but they don't have to have the grid cell input.
00:57:02 - And the grid cells, in turn, use the place cells to know how to attach their
00:57:06 - grids to a given environment.
00:57:08 - And they do need the place cells. So if you knock out the place cells,
00:57:11 - the grids become very unhappy.
00:57:13 - And so the function of the grid cells is to help the place cells appropriately
00:57:19 - position their fields, for example, in the middle of a large open field where
00:57:22 - you're not near any boundaries and you've not got a lot of other information
00:57:25 - and so on. or if you close your eyes and walk around in the dark or something.
00:57:28 - So the grid cells are basically providing metric information that can substitute
00:57:33 - for the sensory cues to the place cells if the sensory cues drop out for some reason.
00:57:38 - So yes, I think it's very bidirectional. I think there's a lot of mutual dependency
00:57:42 - and the brain is highly interconnected and systems are all helping each other all the time, I think.
00:57:49 - But then the grid cell is the system that now can bidirectly interact between the two.
00:57:54 - It's driven by a velocity signal. This velocity signal also comes through quite
00:57:58 - a cascade of processing stages, including the thalamus.
00:58:02 - So this might suggest that there are other systems than your vestibular system
00:58:06 - that might grab hold of driving this velocity signal.
00:58:09 - So it means I could actually start to distort this metric or I could even impose
00:58:14 - a completely different kind of metric.
00:58:15 - Yes. Do you consider that option?
00:58:18 - Yes. So a lot of very elegant work has been done using virtual reality to independently
00:58:26 - manipulate various aspects of the signals that the brain could be using to extract velocity.
00:58:33 - So that includes things like motor cues. So how many footsteps and how quickly
00:58:39 - are they being produced?
00:58:41 - How quickly is the optic flow signal moving past the eyes and so on?
00:58:46 - So there are other cues than just the vestibular signal.
00:58:49 - And so people have played around with independently varying these to see what happens.
00:58:55 - And I think the story that's emerging is a little bit complex.
00:58:59 - One of the things that is quite striking to me is that nobody has yet shown
00:59:04 - a head direction signal in virtual reality.
00:59:09 - So I think the head direction signal is possibly quite dependent on the vestibular signal.
00:59:15 - Of course, the vestibular signal is the thing that's missing in virtual reality. Exactly. Usually.
00:59:19 - At least that's true for animals where the head is fixed. If there are some
00:59:23 - variants of virtual reality, for example, David Tank's group has a virtual reality
00:59:27 - setup where the animals can rotate, but they can't move in linear space.
00:59:33 - So they're running on a ball and they can freely rotate so they can get the
00:59:36 - angular component of the vestibular signal and there there are head direction
00:59:40 - cells and indeed they see grid cells and place cells but the grids are quite expanded,
00:59:47 - which I think is interesting because.
00:59:51 - And slightly analogously to the findings that we have on the wall,
00:59:56 - the thing that's missing in
00:59:57 - that apparatus is a linear acceleration signal from the vestibular system.
01:00:01 - And that was one of the things that made us start thinking maybe that's what's
01:00:04 - wrong with our grid cells on the wall is the absence of the signal.
01:00:06 - So I think the vestibular system is not the only velocity signal.
01:00:11 - It's not necessarily even the most important velocity signal,
01:00:15 - but I think it normally is there and it normally is quite supportive to the system.
01:00:20 - It's it's a little bit worrying that you're
01:00:23 - not getting head direction cells given what we've talked about uh in
01:00:27 - these virtual reality environments where you know
01:00:30 - animals are running on balls and things because that's uh one way in which the
01:00:35 - field has moved quite a lot just in the last decade you have an animal head
01:00:39 - fixed so you can do a lot of detailed recording but you've thrown away um perhaps
01:00:45 - quite a significant amount of the ethological relevance,
01:00:47 - to what those actual recordings might mean yeah and it's
01:00:51 - it's it struck me as well listening to your talk today that you're actually
01:00:55 - using uh very enriched environments but uh and the system you're you're looking
01:01:00 - at is then perhaps a bit closer to the you know the natural animal and its free
01:01:06 - state than some of these other environments that um.
01:01:10 - People have been using to look at the hippocampal system before.
01:01:14 - And you also, you know, you noted that when we went from testing animals in
01:01:17 - small boxes to testing animals in slightly bigger boxes, two meters,
01:01:21 - which isn't huge, then we discovered grid cells.
01:01:24 - So I'm just wondering, you know, what else are we going to discover when we
01:01:30 - really take seriously the lifestyle of the animal?
01:01:33 - You know, sort of these animals live in tunnels a lot. So, you know,
01:01:37 - they move around nocturnally most of their time, and we're testing them under lighted conditions.
01:01:42 - Are we missing other important things?
01:01:45 - I think that's a really, really important point. And I totally agree that the
01:01:50 - conditions that we're recording in at the moment are not very ethologically valid.
01:01:55 - And there are pluses and minuses to that. So one of the things that you do,
01:01:59 - of course, is to reduce the complexity, and that enables you to look at factors in isolation.
01:02:03 - But, of course, what you lose is the real-world relevance. So ultimately,
01:02:09 - you have to start putting all of the stuff back together again.
01:02:12 - And I think one of the big outstanding questions for me is what do grid-in-place
01:02:16 - cells do in a normal cluttered environment like a burrow system or a field with
01:02:22 - trees and rocks and things like that? it.
01:02:24 - And my belief, which we've not tested yet, but it'd be relatively easy to test, is that.
01:02:34 - A day in the life of a grid cell wouldn't result in a nice hexagonal close-packed array of firing fields.
01:02:42 - I think if you just recorded a grid cell for a week or two in a rat,
01:02:45 - you'd find it almost never produced a hexagonal close-packed array of firing fields.
01:02:49 - Most of the time it would produce blobs scattered at what look like random places
01:02:53 - around the environment because of course the rat is, you know, walking.
01:02:56 - Rats, they have these very stereotyped kind of behaviours in familiar territory
01:03:00 - where they have these little rat runs, you know, little paths that they like to follow and so on.
01:03:04 - They don't tend to forage in an even way across the surface of the environment.
01:03:09 - So I think when we start to think about what are these cells actually doing
01:03:14 - for the animal, we need to bear that in mind, that we're very captivated by
01:03:21 - the regularity of the pattern that we're able to elicit.
01:03:23 - But the regularity of the pattern may not be the thing that the brain really
01:03:26 - cares about with grid cells. It may be something else.
01:03:30 - It may be, for example, the function of a grid cell is just to separate out
01:03:34 - pieces of the environment so that the representations of them don't kind of
01:03:39 - bleed into each other or something like that that's kind of a bit different
01:03:42 - from how we've been thinking.
01:03:44 - So we really don't know what they're for yet.
01:03:46 - And so I think your ethological point is extremely valid.
01:03:50 - So they might as well be de-correlating spatial representations given that they
01:03:55 - have only, let's say, interleaved responses to space.
01:03:59 - Possibly, possibly. I think it's a hypothesis worth considering.
01:04:04 - So, Kate, I mean, you have attacked these really complicated problems of, you know, rats in space.
01:04:12 - And you're going to be busy with that for a little while. You also have been
01:04:17 - very close, if you want, to the discovery of grid cells.
01:04:20 - You've been exposed to all this work around spatial cognition, place cells.
01:04:25 - So, in that sense, you really represent a very specific tradition in neuroscience,
01:04:30 - a systems neuroscience. science, trying to link behavior to the neural substrate.
01:04:34 - So if we now would like to follow in your tradition, what would be Kate's Law
01:04:38 - that we have to write on the wall and read every morning when we wake up?
01:04:43 - Kate's Law. Ooh. Um.
01:04:48 - Okay, you've caught me on the back foot there. I'd have to go away.
01:04:51 - I mean, I can, I've, you mean, you mean a law as in a law about the functioning of the brain?
01:04:56 - No, a law that we have to adhere to in terms of studying and understanding the brain.
01:05:00 - It's kind of your advice to your new grad student.
01:05:03 - I think. Look at me as a new grad student.
01:05:08 - Well, one law is the brain is very complicated. The other, the other thing is
01:05:14 - the brain is very simple.
01:05:15 - I think the brain is made of these very slowly computing blobs of jelly.
01:05:28 - And I think wherever possible, it's trying to optimize the problems it's trying
01:05:30 - to solve so as to save itself as much work as possible.
01:05:33 - And I think we have to remember that, particularly when we're designing artificial
01:05:36 - intelligent machines and things like that, we have to think to what extent are
01:05:40 - the systems biologically realistic and to what extent should they be?
01:05:44 - Because biology really is just trying to keep the animal alive and it may not
01:05:47 - be trying to do it in the most elegant way so much as the way that works for
01:05:51 - the environment of the animal at the time. So I guess that would be it.
01:05:55 - Okay. So then Tony actually likes trains a lot and he also likes to take the
01:06:00 - train often from Sheffield to London and five years from now I'll buy him a
01:06:04 - train ticket to go to London, visit you,
01:06:06 - and he will visit your lab and he will come with a piece of paper that says, okay, Kate,
01:06:10 - five years ago you made this prediction and today we want to know whether it
01:06:15 - was verified or not or rejected so what's the most important prediction you
01:06:20 - would like to make today in this time window of five years that you really want
01:06:25 - to see tested in that time frame um,
01:06:31 - Well, several predictions. I mean, five years is not very long,
01:06:33 - so I should hedge my bets.
01:06:37 - Don't, you can't wait that long, Kate, sorry. Sorry. Important prediction.
01:06:42 - So one thing I think is I really like this idea that the vestibular cerebellum
01:06:48 - provides a signal that modulates the updating of the head direction signal in
01:06:56 - three dimensions and allows us to essentially to relate all of our frames of reference.
01:07:01 - So I think that the function of the cerebellum may well be thought of as a way
01:07:06 - of transforming between reference frames, and that it uses the gravity signal
01:07:11 - and the other vestibular signals to do that.
01:07:12 - So that's something that I would like to have at least started on in five years,
01:07:17 - and we're just thinking about how we might do that.
01:07:20 - Second thing, grid cells. I would like to have found out by then whether grid
01:07:26 - cells form a hexagonal close-packed lattice in three dimensions.
01:07:29 - And I'm honestly agnostic about that. I've been selling this multi-planar idea,
01:07:33 - but I have an open mind about whether that's really true.
01:07:38 - And then the third thing is that we're very interested in the retrosplenial cortex.
01:07:44 - I think it's a really interesting and barely understood structure.
01:07:48 - And I'm I'm hoping that we will have made a big step towards understanding what
01:07:53 - it's doing with all that information that it's getting.
01:07:56 - Great. Okay, Jeffrey, thank you so much for this conversation.
01:07:59 - Thank you. Thank you. Thank you.
01:08:02 - The CSN podcast was produced by the Convergent Science Network of Biometrics
01:08:08 - and Biohybrid Systems, a project funded by the European Sevens Research Framework
01:08:15 - Programme. Oh, that was fun.
01:08:16 - Yeah. You did so well. For more interviews, recorded lectures,
01:08:20 - or upcoming conferences in the field of biometrics and biohybrid systems, go to csnnetwork.eu.
01:08:28 - Thank you for listening.
01:08:33 - I'm trying to think of all the people I've insulted along the way write all
01:08:38 - these apologetics oh I didn't mention so and so oh I attributed this to them no don't worry,
01:08:45 - now it's interesting that indeed the system is indeed not as clean as we would
01:08:52 - like it to be and how we want also how it is represented in literature,
01:08:57 - right this is I think we have to be so careful with that Because we built this
01:09:03 - caricature of what this is.
01:09:04 - I mean, of course, as soon as you get closer to more realistic task conditions,
01:09:08 - it will not look at all like this. So maybe even take the wolf situation.
01:09:13 - Maybe this big blob you see in the corner is much closer to an ecologically
01:09:17 - valid response of your grid cells than the beautifully, nicely, right?
01:09:21 - It's definitely possible. We cannot exclude that right now.
01:09:23 - Yeah, yeah. And it's sort of a scary thought.
01:09:27 - Yes. Well, scary, but also interesting. I mean, I think the field tends to attract
01:09:35 - a lot of engineers and physicists and people who have a certain way of thinking about things.
01:09:41 - And I think the sort of niche that I inhabit is a slightly unusual one because
01:09:45 - I'm more of a psychologist slash ethologist by inclination.
01:09:50 - I look at these beautiful elaborate models of interacting oscillations and this,
01:09:56 - that and the other. And I think there's a spectacular intellectual achievement.
01:10:01 - But is that really what it's for? You know, in the messy real world when an
01:10:05 - animal is climbing up and down over rocks?
01:10:07 - Have we really got this, you know, these fine, great, maybe we have.
01:10:11 - But I do think we need some people who are slightly more grounded in the psychology
01:10:16 - and the behavior and stuff.
01:10:19 - Well, this is also the point that I think also the point Tony made about the
01:10:23 - robots, for instance. So to just try to convince people, look,
01:10:27 - if you want to build models of these kinds of things, link it to a robot because
01:10:30 - it gets you a little bit closer to real behavior.
01:10:32 - Because Leash, this is also a bit my beef earlier about these grid cell models,
01:10:36 - these attractive grid cell models.
01:10:38 - They all came out in 2006. We also produced one. Yeah.
01:10:44 - The only one that actually was linked up to a robot and showed,
01:10:47 - look, this gives you a grid-like response over time, doesn't want to be produced
01:10:52 - because it was linked to a robot. Right.
01:10:53 - All the others are more conceptual models. Like, okay, yes, you can imagine
01:10:57 - that, but the stability conditions are dramatically different. Right, right.
01:11:02 - So I think this is really a contribution that this more robot-oriented thing can make.
01:11:06 - You can say, look, consider the computational principles in the context of this
01:11:10 - embodied real-world system. Yeah, yeah. It has to satisfy all these constraints.
01:11:14 - If the robot doesn't give you behavior that looks plausible, forget the model.
01:11:18 - Yeah, because I mean, I think it's a really important point.
01:11:20 - Because, you know, when you're building a computational model,
01:11:23 - you can have perfect senses.
01:11:25 - You know, you can have an absolutely veridical velocity signal,
01:11:27 - but the real world isn't like that. Sensors are very imperfect.
01:11:30 - They accumulate error quickly. There's a lot of, you know, uncertainty and conflict
01:11:35 - and this, that, and the other.
01:11:36 - I mean, so you asked me what, you know, robotics has taught me.
01:11:39 - And one of the things I learned early on, so my husband,
01:11:43 - Jim, who is a roboticist, who was trying to get robots to integrate sensory
01:11:50 - inputs from different sensory modalities.
01:11:53 - And it seemed like a really simple problem.
01:11:55 - It just turned out to be incredibly difficult because when you look at what
01:11:58 - raw sensory data look like, it's just a mess.
01:12:02 - And how do you extract spatial signals from all of that? And so I think you're absolutely right.
01:12:07 - Right, I think if you want your computational model to have any kind of credibility,
01:12:11 - you need to show that it could work in the messy real world,
01:12:14 - given the constraints of noisy sensors and this, that, and the other.
01:12:18 - Right, and I think that's why you need the robustness of multiple systems.
01:12:21 - Yeah. So I think the grid cell data is beautiful, that you have this multi-module
01:12:27 - system that the Moses have shown that gives you an actual location in space.
01:12:33 - But that's still only one way of understanding,
01:12:37 - of of working out where you are and it could be wrong and then
01:12:39 - what's your backups and i think the beautiful thing
01:12:42 - about animals is they have lots of different backups
01:12:46 - and they're potentially independent because
01:12:49 - you might want them to be you don't want you don't want everything to depend
01:12:52 - on your head direction system and people without a good sense of direction get
01:12:56 - around perfectly well in everyday life so you know how they do well some do
01:13:00 - some do some well they make mistakes But they generally have strategies for compensating.
01:13:07 - And that's what I think biological things are good at, is compensating for the
01:13:12 - lack of perfect information, compensating when some parts of the system fail.
01:13:17 - And that's where robots fall down right now. We usually have one way of doing stuff.
01:13:21 - For instance, we're trying to build driverless cars now.
01:13:25 - And some people, like Elon Musk, say five years away we'll have it.
01:13:29 - But what he's got is a system that works when you've got nice white lines down
01:13:34 - the edge of the road, then you can drive.
01:13:36 - But he doesn't have anything like the multiple systems that actually give you
01:13:41 - a robust, fail-proof, fail-safe system that's never going to crash.
01:13:47 - And other people will say, look, we're 50 years away from having that for driverless cars.
01:13:51 - Another example of that is the importance
01:13:55 - of this more embodied ecologically valid approach take
01:13:58 - all this noise we had about uh what's
01:14:01 - called deep q learning like now we have a deep learning network that can
01:14:04 - also act and then it can learn these atari games um okay that's cute but actually
01:14:10 - what you see is that in order to make that work they have to randomly sample
01:14:15 - the input space now think about that right that's great when you write an algorithm
01:14:19 - when i'm a behaving system My input stream is continuous.
01:14:22 - I cannot jump around like a frog and make sure I have an even sampling.
01:14:27 - This has been documented since the early 90s in these more embodied models.
01:14:32 - This has also been shown to be a major weakness in these sort of hierarchical
01:14:36 - classifier systems that people also exploit in deep learning.
01:14:41 - That means as soon as he thinks, and this is not considered a problem right
01:14:44 - now, because no one is thinking it through in behavioral terms.
01:14:47 - And I think that's a real problem.
01:14:49 - Because now, in some sense, we're getting a lot of noise in the literature,
01:14:52 - a lot of people being distracted.
01:14:54 - And also, I think, misinformed because we're not imposing the right constraints.
01:15:00 - And in my opinion, in these models, we must insist on bringing together behavior,
01:15:05 - anatomy, and physiology.
01:15:06 - Any model must answer that. And if you're not able to do that,
01:15:10 - it's under constraint and for me, it's noise.
01:15:13 - It's not necessarily helping, but it's not, of course, it makes life more difficult.
01:15:20 - Replicating colorful pictures in MATLAB is a lot easier. I mean,
01:15:23 - it sort of depends what you're trying to do though.
01:15:25 - I mean, some people are trying to understand the brain, but some people are
01:15:28 - trying to build neural networks that do things and looking at the brain,
01:15:33 - it may give you some ideas, years but replicating the brain
01:15:35 - is not necessarily a good thing to do because it is that's
01:15:38 - fine but they shouldn't claim that they explain anything yeah
01:15:41 - so but as the people you know i can i can see you know i could see that the
01:15:45 - discipline is full of all sorts of people doing stuff that's just totally useless
01:15:49 - but i do think that there are um there are kind of insights that you can get
01:15:54 - from stuff like this as well but it's also a matter of being being upfront about
01:15:59 - what your constraints are.
01:16:01 - Of course, anyone is free to play around to get an idea about some competition.
01:16:05 - Great. Yeah. Right, but be clear about it. Don't pretend that suddenly now we
01:16:09 - have an explanation for how a brain might do something.
01:16:12 - Yeah, yeah. People are overconfident. Yes, absolutely. And there's too much
01:16:15 - methamphetosis going on. Yeah.
01:16:17 - In my opinion. Yeah. But anyway, behavior is the way forward.
01:16:21 - Well, you know, yeah. You need all of these different approaches.
01:16:25 - Would you do any modeling yourself?
01:16:27 - Um, not, not much. I've done a little bit. Okay.
01:16:31 - Um, and I've just hired a modeling person actually, who's, who's worked on the ring tractor network.
01:16:38 - And so we're going to get him to try and model a fair color tractor and just play around with it.

CSN Podcasts

Kate Jeffery on spatial cognition and grid cells

Season 2015

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Matthew Diamond on whisker system and decision-making

Stephen Noctor on cortical development and precursor cells

Ranulfo Romo on decision-making and somatosensory cortex

Narender Ramnani on cerebellum and cortico-cerebellar loops

Mark Blumberg on rem sleep and twitching

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