This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschoor and Tony Prescott.
This is Paul Verschoor with the Convergent Science Network podcast with my colleague
Tony Prescott. And we're here today with Neil Burgess, who is a speaker in our
10th edition BCBT Summer School.
And Neil was giving an overview of his work on spatial cognition,
also about how to link neural dynamics with advanced psychological function,
like our knowledge of space.
And Neil, what you also presented very much in your description of your work
is this close coupling between theoretical approaches, modeling work, and experimental work.
So where did this combination of these two methods, if you want, originate?
Well, I was originally a computational person. Having done theoretical physics,
I started to want to do computational models of memory and other forms of cognition at the level of neurons.
And well, my PhD supervisor, theoretical physicist called Mike Moore,
said, you know, if you're going in this direction, it's an experimental subject
and you need to get involved in the experiments, and I'm always grateful for that advice.
There would be no point sticking to mathematically tractable models if they
had nothing to do with the biological reality,
and so it seems very important to have your models directly address experiment
and equally for your experiments to be theoretically well grounded to answer
particular hypotheses that make sense.
And so experiment and theory should always work together.
But now, how many of the models you have generated in that period have you actually
completely rejected and changed your mind about and taken a completely different direction?
That's a good question. I think that typically you're interested in a phenomenon
and you continue to follow that even when experiments or models don't work.
And so if you're interested in explaining that phenomenon, your models might
change and indeed your experiments might. so in terms of all that rejection um.
I'm i'm struggling so i started off uh doing um kind of hopfield model type
models of memory and i was working with a psychologist graham hitch who was
interested in memory for serial order,
and it seemed that although you can remember sequences of things in hopfield
models by associating one pattern of activity to the next in in human uh working
memory for serial order Often you make paired transpositions of items.
So instead of A, B, C, D, you remember, or you output A, C, B, D.
And it's very hard to get that kind of error in a straightforward Hopfield model.
And so I started using competitive queuing type models.
And I haven't really looked back on the Hopfield type models,
although I still believe that the attractor network idea is key to how much
of the brain works, including hippocampal area CA3.
I haven't really gone back to those models as just a pure attractive model of memory.
Right. So then you started out by saying,
by pointing to this Wang and Simmons experiment that makes the point that actually
there are a number of cues or a number of streams of information that come together
in our understanding of space.
So why do you think that that experiment is sort of critical as a starting point of the analysis?
Well, it's just one experiment. I don't think it's critical,
but it very nicely presents the fact that you can remember where something is in many different ways.
And so it's sometimes good to start with that example, so that even though I
then might drone on about hippocampal place cells for a long time,
the audience already knows that that's only one kind of strategy for remembering
where something is and that other parts of the brain would be doing equally
useful things that can help you remember stuff in different ways.
So of these different possible ways to deal with space, as a concept,
how would you then define the way the hippocampus solves the problem of space?
Well, I mean, all of that part of the model is very much driven by the place
cell phenomenon and surprisingly single neurons encoding for single places.
I mean it's a remarkable sort of one-to-one mapping aside from the fact that
different cells will fire in different environments and provides a very easy to use code.
You could associate things that happen in places with the activity of the place
cell that fires in that place very easily or you could,
having found a place cell that fires in a given place it could
be that its firing rate is modulated by some other factor
that happens in that place and that's just a very simple starting point
for thinking about a memory system
for location yeah i think
going back to this question about parallel representations a lot
of the hippocampal models and i think yours start from this assumption that
the hippocampus represents one solution to the question of where something is
and but an alternative Bayesian view might be you'd represent a probability
distribution across multiple solutions.
I mean, is that something that you entertain as a possibility?
Yes. So what's been lacking is direct experimental evidence that you might be
weighing up the different alternatives and seeing place cells firing for both
different alternatives.
But in many situations, you can see, for example, as I showed,
the single firing field of a play cell becoming bimodal when you stretch the
environment, for example.
And it's possible that you could
think of that as part of a system that's trying to estimate location.
And because it's been predominantly taking evidence from distances to boundaries,
it's now got a hypothesis that
has two peaks in it and that you are indeed representing a distribution.
And in the experiment that I showed where people had to remember where something was,
The population vector overlap with the stored place cell representation captures
well the distribution of responses across subjects in that experiment,
rather than indicating a single location.
Yeah, but I think Tony's question is also annoying, right?
Because in some sense, Tony is saying like, well, sort of this spatial cognition
can happen in many different ways, in many different areas of the brain,
it becomes a little bit like wishy-washy, like, okay, it's sort of not such
a unique feature then of a single structure like the hippocampus.
That's how I take that question. If it's just Bayesian, we integrate multiple
factors, and so then why worry about hippocampus?
But I still believe that hippocampus is making a unique contribution to solving that problem.
Well, I would agree with that. But the question is that, I mean,
so I think it's definitely true that we have at least two solutions.
But I'm here to make Neil's life difficult. Just to clarify my position.
There's a striatal solution to the navigation problem.
There's something in the hippocampus. There may well be other ways of navigating,
certainly towards sort of nearby targets and so on.
So you can almost say, well, in hippocampus, we might be representing multiple
solutions, and then the decision gets made in some other part of the brain about which way we would go.
And there are models of hippocampus that are consistent with the idea of representing multiple hypotheses.
I want Neil to tell me now which one it is. Yes.
Well i mean as as i showed um there are
some things that we do know about the brain and this packard
and magor experiment was again it was
only a summary of lots of things that people knew up to that point but it
was very nice clear expression of the fact that different solutions
could um seem to be supported by different parts of the brain and you could
actually inject anesthetic into the two different parts of the brain and see
the different strategies being expressed in behavior so um in answer to tony's
question i think well and indeed yours that um.
If i want to characterize the hippocampal contribution to these
kind of spatial memory tasks you know it it seems to
be representing a location relative to
the environment probably as a conjunction of many kinds of cues some
of which are distances to boundaries in different directions whereas
other parts of the brain might be remembering more uh you
know i turned left at the shoe shop or a sequence of turns on
a well-known route or in other parts of the brain still
you know a visual visual snapshot matching uh
this is this looks like where i was before and all
of these things are happening in parallel and it is an interesting question
what how they get combined who chooses what and indeed on the input there's
different kinds of evidence coming in and they are probably waiting in a bayesian
way with more things that seem to to be more stable over time or more certain,
being given a stronger weight than things that seem more variable.
But would it be fair then to say that the hippocampus really is critical in
generating this allocentric representation of space?
Yes, I would think so, yeah. But in many of these experiments,
they're a memory experiment.
You have to remember a location and go there.
And we know that the hippocampus is critical for many forms of memory.
And so in a spatial memory experiment, yes, if you inactivate it,
then often the person or animal will not perform very well.
So there's good evidence that the hippocampus is responsible. responsible and
in that pakada magore experiment you could see that
it was particularly responsible for one of the two available
strategies for that task yeah the general hypothesis
that you're following then is that the there's a population response in hippocampus
of of play cells which in some way represent the brain's best guess as to where
it is in the world in an alec-centric coordinate frame in those tasks yes yeah
okay we're done so now we we have an hypothesis
about what the hippocampus is doing.
And it gives you this, an ellicentric representation of space.
So in world coordinates, which is, which is a fantastic accomplishment.
And now we have to try to understand how this is done.
Right. And, and then you build very much your, your theory or model on that
around, at least in the, in your presentation now around this notion of the boundary vector cells.
This is really the starting point of your whole analysis, right?
So, so why, why do you feel that this notion of boundary vector cells is then
so decisive to understand this ability to generate allocentric representation of space?
Well, first of all, the head direction cells, I think, are fundamental because
this overall orientation, sense of orientation, is important for everything
that comes subsequent to that.
But taking that as granted, then you need some distances from some things to know where you are.
And really, I think that if you were to imagine an animal with a sort of range
finder, looking at the distances to stuff around it in full 360 degrees,
you know, obviously, the biggest contribution will be to either things that
are very nearby or things that are quite extended.
And so topographical features of the environment that are extended are going
to dominate that kind of representation.
And I think that that's really the boundary vector cells.
Cells we heard also and and
i talked about object vector cells and we heard from them from edvard
moser you know the
boundary vector cells would do actually respond to uh
small objects just they have a very small firing
field is provoked by a very small object and so
they're sort of responding like a a range
finder tuned in a particular direction allocentric direction
presumably governed by the head direction cells and and
you know with once you're oriented the next
thing you need is distance and and these are range finders
tuned to directions and they seem to
provide the simplest explanation
for the sort of broad qualities of the firing fields of place cells when you
put an animal in different environments environments that differ in shape and
size but not differ in in texture and odor and and so on enough to make different
play cells fire in which case you wouldn't be able to try and analyze these different kinds of.
So then you're saying, look, I have a heading vector. This gives me,
if you want, that's my compass. That's my directional system.
And now I glue to that some sensory feature.
Might be somatosensory. It might be auditory. It might be visual.
And that now gives me, let's say, a reference in space of how that compass coordinate
maps onto that specific environment.
Environment well i should say that in most normal environments
there'll be a you know a broad range of different
local cues also which would help you in a very direct way
to localize yourself and they are probably also important to
play cells but in most experiments most people
do they try to control for those cues so that they're only
the cues that that they're aware of remaining like
the boundary of the environment these distant orientation cues for the
head iteration cells and so um given
that you've tried to remove reliable local queues
also usually the floor is rotated between trials then
what you have left you have distances to to
extended things right but now the head direction system depends on path integration
path integration is noisy because it's an integrator um so isn't that a little
bit of an obstacle for for the such which is to keep on working reliably over
extended periods of time. Yes, indeed.
So that's an interesting thing that I didn't talk about, but exactly the same
concerns about how you translate between egocentric and allocentric in terms
of place cells and sensory inputs, which have to be egocentric.
Applies to head direction cells.
So a head direction cell will fire whenever the animal's facing,
let's say, north. I don't actually mean compass north.
Wherever it is in the environment. And so actually in all experimental situations
when they've been recorded.
That tuning is parallel across the environment, but if they were simply responding
to sensory cues, you would see parallax, which you do not see.
It is an allocentric response, the head direction cells. And so again,
you need to have this translation mechanism between egocentric sensory input
and this allocentric response.
And so we think probably retrosplenial cortex, as in the model that I explained,
has to do that same job for the head direction cells so that they can be reliably
anchored to sensory input to prevent the accumulation of error that you would
inevitably get if you're just integrating angular acceleration. Right.
But that is the sensory cue that you link the heading vector to.
That in itself might already be an invariant representation of some sensory state.
It might not be necessarily egocentric. right if
we go through some some perceptual hierarchy and if
i climb up that hierarchy i might get to more invariant representations
of space maybe i have a very abstract representation say
just okay this is a wall i'm not
interested anymore in its orientation or its color or
its size a wall so so how would that notion
of invariant representations of the sensory feature then actually
be a challenge to that to that model well i
think it's um the invariance we're
talking about uh is exactly you know the head direction
cells show that very nicely they're at
the top of the hierarchy for direction they're invariant to all of the parallax
and sensory stuff that happens as you move around and they extract what would
be a compass you know and and uh the rest of the system is probably built on
that and now if it would replace the heading vector with a movement vector?
Would that change your approach very much?
I'm not sure what you mean. As opposed to integrating, let's say, my rotations in space,
I could also say I just interpolate between subsequent movements,
and this gives me a vector that I might then use to get to some sort of LSM
representation of space.
Well, so there's two separate things here. One is how do the head direction
cells fire to encode the orientation of the head. If you then move on to path
integration and perhaps what drives grid cells to fire, then you have to integrate
movement rather than head direction.
So you need to know which direction you've moved in, not which direction your head is facing.
Exactly. So would that make a big difference to your proposal?
Not for the boundary vector cells, I don't think. Because usually the head of
the rat is... Because the boundary vector cell model of place cell firing is
not path integration. It's just feed-forward sensory input.
What direction from the head is the boundary feed forward.
So there's not an issue there. When you come on to how you do path integration,
how you integrate your own bodily movement to estimate where you are,
which may well happen through grid cells,
then indeed you need to know your movement direction, your movement vector,
not your heading vector. That's right. Perfect, yeah.
And so there's various hypotheses as to where this signal comes from because
it can't be just the head direction. That's right.
Multiplied by running speed. has to be movement direction. Would it be your grid cells then?
No, what we're talking about are what are the inputs that would go to something
like grid cells that is the movement signal that is integrated.
If I would just look at subsequent grid cell responses, I could infer a movement vector.
Yes, you could do. So that's what I was after. Yes,
so I think maybe this question came up
with Edvard Moser's talk that whether you could
look at it either way if you think grid cells know uh
how to fire because they're doing path integration you need
an input which is a movement vector or you could say well
grid cells fire as they do in which case a movement
vector could be an output yes that's it yes exactly indeed it could be but then
you're still left with a question of how the grid cells fired in that way in
the first place right the um so the the activation of the play cells you have
of being driven by the boundary vector cells.
And so it's basically a thresholded sum of the inputs from the boundary cells.
Clearly, you could choose the weighted sum of the inputs as just the first thing to try.
Are there other motivations for doing it that way, for example,
from environment warping experiments that say that it's the weighted average,
it's the best thing to use?
Well, it was the simplest thing, the first thing to try.
But in some experiments as you um expand the
box you have a place field which slowly reduces in
firing rate and disappears right which implies a
sum with a threshold you know as the
over initially overlapping inputs from opposing
walls uh move apart then the the bit you've got left slowly falls below threshold
and the cell stops firing so there's there's good reason to use a thresholded
sum there's a reason for a threshold and um we really did see um in some cases individual,
sub peaks being moved apart which means you have
to sum the two rather multiple if you multiply them then
you get for example a bayesian you know average position
in between the two peaks as they well this is what i was
getting at is there support for multiple hypotheses in some
of those well um just to
finish answering question there's there's clearly adding these
inputs from the different walls and having a threshold was the
most obvious thing to do given all the things we've said um so if you were trying
to combine um hypotheses probabilistically you would probably multiply and that's
not what we saw right um it's interesting because of these experiments by um,
galasell and cheng and also by uh cartwright and collett um john o'keefe in
this original stretchy box experiment was expecting to see.
Cells, place cells, firing.
In the constant ratio of the distances across the box, rather than the sub-peaks
being pulled apart and sticking to the fixed absolute distance from the walls
as they were pulled apart. And so...
If what he'd been expecting had happened, that would have been an argument for
multiplying and having the combined estimate of two probabilities distributions,
but that isn't what we saw.
But it sounds like what the experiment says is a bit more sophisticated than the averaging.
So there might be something to look at there in
the future in terms of how in this inconsistent
consistent environment that's you know where uh the
boundary effect cells don't all point to
the same place in space then you might start to represent
multiple hypotheses in terms of place cell activity
well i don't i i don't know if there's
evidence for that i think there is definitely evidence for um
hypotheses about what happens in
a place you know place cell a place cell
might fire in the same place trial after trial but its
firing rate might vary quite strongly according to
other hypotheses to do with sensory stimuli
that are present whether objects are nearby smells how
fast you're running so I think there's a lot of
orthogonal hypotheses to place which also modulate the firing rate but it more
like a gain field representation okay so the actual firing field itself uh isn't
obviously doing something nice and bayesian about estimating location.
Uh when you do these weird experiments when you deform the environment which
are rather unnatural but they probably are combining the cues to them such as
we haven't talked about the path integration input and the sensory input are
probably being combined in a more bayesian way yeah and also as you say maybe
there's something happening in time you know that,
you can swap between hypotheses over time and explore different interpretations.
Well, actually, on that point, if you do this stretching experiment and you
get a single place field stretching into two place fields as you've expanded the box,
then you notice that each place field fires a bit more according to running direction. So the...
The place field that appears to be attached to the wall behind the animal fires
a bit more, so that when you're running in one direction, one peak will be higher,
and when you're running in the other direction, the other peak will be higher,
which we interpreted as being this path integration input,
adding to this environmental sensory input, and being stronger if you just run from a wall.
Obviously, you have a good estimate of how far away it is from path integration,
so that peak gets a bit higher because it's got a stronger path integration
input but down the peak that's attached to the opposite wall that you're running towards.
Yeah, yeah. But I find it very interesting.
You guys seem so, let's say, motivated to interpret this in Bayesian terms.
Well, if you look at the dynamics of CA3, CA1, where the real action in the
end happens, it's also highly competitive, and in the end, it's relatively sparse.
You can also think about it much more as, let's say.
The endpoint selector that sits on top of some Bayesian integrator as opposed
to being a Bayesian integrator itself because you just don't have the dynamics
for it because it's rather sort of selective,
rather sparse, and these are not the features you want in your Bayesian integrator.
So is it not fair to place that bit outside of the hippocampus, gentlemen?
No, I think that's fine. As I said initially to Tony, there isn't good evidence
of multiple hypotheses being entertained at the same time.
It would be a good way for the system to work but it may well not do that.
And then can't we not explain the elongation of the place field also in terms
of a perceptual learning process because the animal starts also carve out specific
trajectories through that space.
It's just not moving randomly, right? And the cells you
measure from is probably also on trajectories the animal
will visit more frequently than other trajectories so it
will actually be moving more often from that specific
initial position to a final position and therefore expose itself more to the
specific sensory features that are already associated with that place field
and therefore strengthen the specific intra-place field associations and then,
if you want, elongating its response.
Well, there are interesting trajectory effects on place cell firing,
but they're rather different than these effects in this unusual situation where
we change the shape and size of the environment.
I mean, I think that's an unusual experimental manipulation that shows us something about the inputs.
However, the trajectory effects are interesting so that after... So if the...
The animal's always running in the same direction. You see that firing fields
tend to elongate backwards a little bit along the track. So my anchor, Mater, noticed this.
But that's been interpreted as perhaps the play cell beginning to fire in expectation
of getting to its, quote, true place, original place.
And there's some interesting theoretical work by Matt Botvinick and some co-authors
suggesting that these play cells might encode a successor representation for
reinforcement learning,
which is an idea from Peter Diane in the 90s, that if you were to tweak your
state representation to actually include the likelihood of eventually ending
up at that state, even when you're at other states,
then that enables you to estimate future reward at a given location much more efficiently.
And so it's possible that if you have repeated trajectory, trajectories,
then you can build a little bit of the probability that you now know that if you're here,
you're likely to end up somewhere else into the state representation so that
you can then rather more easily estimate likely future reward from a given location
because you've built in the transition probabilities,
which are now not uniform because I always run in this direction when I'm in this place.
So it's the kind of model that reduces the whole brain into the hippocampus.
Well, no. I mean, it may be that that kind of representation,
you know, perceptual learning and so on, affects, you know, it could explain
what happens in lots of parts of the brain, not just hippocampus. Right, okay.
But now, so in your physiology, so then with that model, you went to look at the physiology,
and then you show that these sort of vector border cells are to be found both
in the subiculum and the entorhinal cortex, which is interesting, right?
Because if you look at the overall loop, we start in entorhinal,
we go to the dentate, gyrus, CA3, CA2, CA1, then subiculum, and then we go out to entorhinal cortex.
So isn't it really strange that we find these cells actually at the input stage
and the output states of that whole loop?
It's like we're wasting all this circuitry in between to do nothing,
and we just have our border vector cells.
It is. It is strange. I mean, it is a loop, and we may be interpreting how it
works incorrectly when we think of entorhinal as the input and subiculum as the output.
I mean, there's plenty of connections from subiculum back to entorhinal,
and presubiculum has very strong connections to entorhinal.
My colleague Colin Lever is always keen to point out that something that Pat
Sharp originally noticed, that when you get a complete remapping of place cells
in CA1 between two environments,
there's actually spatial responses in subiculum that are nice and stable that
you can record, and they show no remapping at all.
And yet the standard model would be that the major input to subiculum is the
output of CA1, and yet you've got this complete change in CA1 representation, no change in subiculum.
Them so yes it is a puzzle you know maybe
subiculum is the input and it goes around to enter
and then into you know who knows but it is a puzzle
it's puzzling from the anatomy you wouldn't expect it no but
it's quite a challenge there's something there that but it
is a challenge to your model right because you want to say these border vector
cells are if you want a representational primitive on which i built everything
else but now we see whatever wherever you want to put the input and the output
they still sit there well and and what is also true if you you know you You
could also ask me the sort of functional difficult question would be the place cells are a basis set.
You know, it could be that boundary vector cell firing responses are made out
of weighted sums of place cell firing. That could be possible.
But now, if you look at subiculum and entorhinal, how big a proportion of cells
in those areas would actually show these properties?
So I think it's around 20% or so is Colin Lever's best estimate in subiculum,
and it's a bit less, maybe more like 10%, but still quite common,
these border cells that Edvard Moser described, i.e.
The boundary vector cells that fire quite close to the boundary. boundary
they're quite common there have been a few reported
that fire at a distance but it's a handful and so that's
another another strange yeah we that remains to be um the exact relationship
between the border cells and the boundary vector cells and the object vector
cells is still not quite clear okay so but then could we argue that in entorhinal cortex there's if you
want a rank order of cells and in terms of how foundational they are.
You could argue, well, grid cells might be very foundational.
Single modality cells in lateral entorhinal cortex might be foundational.
And now I start to merge them. I start to merge them in your border vector cells.
So because now I have within entorhinal cortex, I have access to both movement
vectors, grid cells, and have access to sensory features of the world,
lateral entorhinal cortex. and I just have a subpopulation of cells that is
associating the two, and now I have my border vector cells.
Yes, the grid cells are also a basis set, and you could form any responses out of them.
So it's a good question, although it's not quite clear what foundational means in your question.
And so we have looked developmentally. So Francesca Cucci in John O'Keefe's
lab was looking at this and also Edvard Moser's lab at the same time.
And what you see during development as pups learn to start to crawl around and
open their eyes and so on is that the head direction cells are foundational
in the sense that they seem to be there as soon as you can record them.
There's also theta rhythmicity as early as you can record them in this situation.
Then you also see place cell responses as they start to crawl around,
the rat pups, and they get better with experience over several weeks.
So there's a slow improvement in the spatial tuning of the play cells.
Grid cells are not present until four or five developmental days after you see these other cells.
And so they're not foundational in that sense, it seems.
And this has led to people suggesting that maybe
the grid cells require some stable spatial input maybe from
play cells to wire themselves up to fire
properly i mean uh the
head direction cell seems to be a very strong attractor network
so that the the head direction cell responses are
mutually coherent with each other even if they are drifting a little
bit early on in development it may be the same with the grid cells that they're
mutually connected well so that it's an attractor it's just drifting so much
relative to the world that you can't ever record it that's a possibility and
then you only see it when it gets attached to stable sensory inputs because
then you can average over movements across the environment.
But yeah they're all interesting questions and you can try the good
thing about this field is that you can try to answer them with experiments absolutely
and the developmental trajectory you're talking about there is based on the
chemical properties of the cells not not their functional properties no no no
sorry the function this is recording um uh the the the spiking activity as
pups first start to move around as they start to explore around the nest.
Francesco will give a talk about this tomorrow, I think. Right.
But now the object vector cells, this is the next complexification that also
Edward talked about yesterday.
You see them as a further variation on that same theme, or should we now be
really shocked and say, oh, this is again completely different?
And oh well they're obviously closely related to
boundary vector cells in the sense of firing at a allocentric displacement
vector from something and the only thing that's different is that they
seem to be specifically tuned to small objects right
so a boundary vector cell as i said will fire to an extended object but will
also fire to a smaller object but just with
a very small firing field because uh obviously it
only has to move a little way and then it's not in in the receptive
field for that cell whereas an extended boundary will produce a long stripify
the object vector cells seem to be different in that they uh don't seem to respond
to the extended boundaries of the environment they just respond to objects put
within it now it could be that over time uh.
There's something to do with the object being novel and the boundaries being
familiar means that it discriminates because the same object vector cell will
respond similarly to different objects.
It's not specific, it seems, to specific objects, but will fire to any small
object, but not to an extended boundary.
But so far, the experiments, I don't think, have really been done to see whether
if you put a novel extended object in that looks a bit like a boundary,
whether those cells will fire or not. Exactly, that's the question, right?
When does an object become a boundary? The reverse experiment has sort of been
done in that Bruno Poussey's group had showed that some wine bottles put inside
one of these boxes, when you're calling place cells,
typically don't cause much place cell firing.
Although we know now from Jim Nerium's work that a small number of place cells
will fire relative to these, indeed, looking like object-based cells.
But when they placed three wine bottles in a row together, they started to have more influence.
Probably because of an extended thing, they would be driving more boundary vector
cells. But the same kind of experiment hasn't been done with these object vector cells yet.
But the interesting thing about
now these object vector cells is that they appear in CA1, apparently.
Well, a very small proportion of CA1 cells, yes.
Yes, but Edvard was reporting a medial entorhinal and some interesting sort
of memory object, well, memory times object cells had been reported in lateral entorhinal.
But can't we, but these are highly plastic circuits and they are exposed to
multiple, let's say streams of modalities and submodalities.
So if we just combine these two considerations, is it then not sort of predictable
that these kinds of combinatorial or conjunctive encodings emerge rather naturally.
Well, yes, but before we proposed them from our analysis of play cell firing, nobody knew that.
An offset vector and essentially offset vector was one of
the ingredients that would get combined with boundary detectors or
whatever and until the moses recorded grid cells
nobody thought there's this sort of furrier like representation of space would
would exist or needed to exist and so yes in retrospect but before the fact
no no i didn't want to trivialize it i just want to say this might mean that
if you now go for some ambiguous border object object and for sure
you'll find a cell that'll respond to it because we look at this combinatorics.
Maybe, but I mean, the interesting thing, I think, as Edvard pointed out about
the hippocampus and entorhinal cortex is that coming from the point of view
of a Hopfield model for memory,
you might expect a random distributed binary code that was uninterpretable.
And in fact, you see these incredibly clear, discrete responses,
say to compass direction or to location, or a Fourier-like like grid or,
you know, a boundary vector.
Now there are, as Edvard said, you know, you do get conjunctive directional
grid cells, for example, and there may be other conjunctions,
but the overall impression is that, and there are plenty of spatially modulated
cells that haven't been characterized also in entorhinal cortex.
They're probably now less than, you know, they're probably a minority of all
the cells because so many cells have been characterized. But still,
the overriding impression is of these incredibly discrete types of encoding being present.
And sure, they are present in some combinations also, but overall,
you wouldn't say this is just a mush of everything.
You would say, Jesus Christ, why are those such specific responses there?
But as a mathematician, it must appeal to you to perhaps to find some theory
that explains the emergence of these different kinds of cell types,
which are particularly useful for navigation.
And then to be able to perhaps generalize that and say what further kinds of
cell types we might be able to predict that we should see, perhaps because they're
adding more orthogonal information to the mix,
perhaps sensory cells, for example.
And I think one of the things you alluded to, and also Edvard in his talk,
was how the grid cells have this sort of change in scale, which follows a sort
of mathematical law, which is consistent with efficient coarse coding.
So it looks as though there is, again, something which doesn't look at all accidental,
but must be the result of this system responding in some very effective way
to choose the right inputs for mapping space.
Yes, I think when you come to the grid cells and the discrete jumps in scale.
That is clearly well arranged for large-scale navigation.
You get a big combinatorial power for the range over which you can encode locations
with that kind of representation.
So what is your prediction for the next kind of cell type?
Um...
And it's funny, I don't think the field at the moment is working that way.
It's true that we predicted boundary vector cells from looking at how place cells responded.
And going back, that argument, that way of thinking was certainly used by O'Keefe and Nadel.
And John O'Keefe, having found place cells, predicted there should be directional
cells and something like path integration or distance cells.
And so early on, having found place cells, he did predict the head direction
cells and then something to do with distance.
And they have been borne out. And something to do with vectors,
it seems like, is necessary. And we have boundary vector cells and object vector cells now.
I think the interesting thing, I mean, so there's already a lot of ingredients
there for making quite a sophisticated system for space.
And at the moment, I think people are thinking more, how could all this stuff
we've learned about space tell us about more general memory properties of the hippocampal system?
That you know how does it store other information which
which we know that the human hippocampus is required for all
sorts of forms of memory and i think that's interesting
so i haven't predicted any more kinds of
spatial cells i must say but that's particularly what i was getting at you know
sort of human episodic memory which is more than about a lot of things more
than space um and where it's it's less easy sort of coming at it from the geometry
or whatever to say what kind of basis functions you would want to have.
So we might look at some mathematical way of finding appropriate basis functions
for learning about episodes in time.
Yes. Well, so I think it's very interesting that the cells in the human hippocampus,
most famously Jennifer Aniston type cells, are.
They're a local tuning curve to a concept, in this case of Jennifer Aniston,
among all famous people or people you might have heard of.
And a cell will fire to that concept, whether it's a written name or however you bring it to mind.
And other cells will fire for other concepts, be they spiders or semantic well-known
landmarks or famous actors.
And so you might indeed see
the sort of locally tuned place cell response as very
similar to these locally tuned responses in semantic space and
this very interesting um stretchy bird experiment by
tim behrens and his group showing what looked like grid-like responses in in
humans to semantics two-dimensional spaces and uh in rats to one-dimensional
continuously varying tones showing play cell-like responses from David Tank's group,
do imply that these principles that have been easiest to spot in space and freely navigating animals.
Might well apply to all sorts of other kinds of information and how it's organized
and how it's retrieved and so on. And then that would obviously be a...
Starting with these very well-characterized spatial responses,
I think, is a good place to try and understand everything else,
which is obviously much harder to get a grip on.
So, I mean, that would suggest that one thing we might look for is sort of compression-type
algorithms, which will give us a low-dimensional.
Or relatively low-dimensional description of the current context,
which we can encode then in entron and cortex and use to run our sort of location
finder or memory finder algorithm.
Is that a good metaphor? Well, the grid cells certainly appear to be a compressed
code like a Fourier code or something that would be very efficient compared to the place cells,
which in their own way are very useful for tagging specific information to specific places,
but are less efficient for covering large areas in a metric way where you can
infer the vector between any two parts.
If I have a place cell that fires in one place and another one that fires in
another place with no overlap, it's hard to know how they're related to each other.
Whereas with grid cells, you can infer the vector between two grid cell codes
for two different locations. And so that's very powerful. It's a compressed representation.
You know, it looks a bit like a Fourier pattern. But equally,
Dory Durdickman's group has pointed out that if you do PCA on the place cell
input that you get as your animal is running around, then you get things that look like grid cells.
So indeed, the grid cells might be doing a PCA of this place representation.
And there's a similar argument to be made for the successor representation that
I mentioned for place cells in that the eigenvectors of the successor representation
also look like grid cells.
And so they're clearly related, the.
Eigenvectors of the covariance matrix being the same as the
pca it could well be that the grid cells are a
compressed way of arbitrarily representing
any of this location coded or you know single peaked tuning curve uh grandmother
type cell response uh representation of arbitrary information which is very
useful for then associating things to that information in the hippocampus yeah
so i think if i maybe put words into your mouth but you're agreeing that
the entorhinal is a compressed representation of the context,
and then that's expanded out in CA1, CA3,
perhaps after being sparsified in dentate gyrus, as often as that people have.
Well, you know, David Marr's model of hippocampus remains the best model of hippocampus in memory.
You know, it's probably surviving that and his cerebellum model and surviving
really well, maybe even better than his later work in vision.
Um and yes those ideas hold a lot of currency although it's important to remember
entorhinal cortex is not just grid cells so even within medial entorhinal cortex
it may be that head direction cells are more numerous than grid cells and there
are also these border cells.
Conjunctive cells and spatially modulated but not grid cell cells so you know
yes for the for the grid cells they look like compressed code uh there's other
things going on also uh sensory inputs to place cells,
we think, not just the path integration type inputs.
But aren't you, first you guys are all swept away by Bayesian,
and now you're all swept away by, let's say, compression, which in some sense
is already happening to a large extent outside of Hippocampus.
And earlier, I thought we had agreed that Hippocampus is contributing to constructing
allocentric representations.
So, let's not worry about compression then, but now you guys are all worried about compression.
So, what is it? Compression, allocentric, or is
it both what do you want uh well you're
comparing apples and oranges i i don't i think you
can have an allocentric or an egocentric representation and you
might want it to be compressed or not so we i don't
know we can compare allocentric egocentric no no you have no no what i'm saying
compression or allocentric is it both is it combined is one building on the
other are these independent orthogonal interpretations well so so the those
codes the head direction cells and the place cells and grid cells appear to
be allocentric in the sense that...
The reference to the world, it's which way you're facing in the world,
where you are in the world.
But they are where the animal currently is, which you might say is egocentric.
It's referring to the animal's own location or orientation.
Having said that, I think then the grid cells look like a compressed version of the play cells.
So it may be that an expanded representation is useful for attaching things
to, and that might be the play cells.
And a compressed representation might be useful for generating large-scale metrics
within which to compare or know the relative positions of lots of information.
About 10 years ago, we showed that grid cells actually carry more information
about space than place cells.
So if you do position reconstruction from grid cells versus place cells,
you have more accuracy from the grid cells.
So that would be then inconsistent with the idea that they compress the place
cells because then the place cells would have more information for position
reconstruction. Are you comparing the same number of place cells and grid cells?
Yeah, this was all control.
Yeah, no, well, exactly. So if you have a sparse representation,
which is easy to attach things to, then obviously it's more efficient. It is compressed.
You can have a coverage of a larger environment with the same number of grid
cells. That's why it's a compressed representation.
Okay, look. But now the other... So this is a controversy we saw,
which we will solve later,
but now you presented a model where you wanted to explain how now you could
use allocentric representations to also reconstruct, if you want, imagined locations,
how you could use imagery to sort of make now predictions that would be relevant for navigations.
What's the contribution of that model now to our understanding of the hippocampus?
Well, that model was trying to take these sort of spatial things that we recorded
in and around the campus and say, how could they apply to human episodic memory?
And, you know, the experience of human episodic memory is reliving the event.
And in terms of the visual content, it's imagining the scene.
And so the proposal was that this spatial system is a way of pulling out the
stored allocentric or abstract information that you have in your temporal lobes
to create a coherent spatial scene from a single location with a single direction,
you know, provided by the head direction cells and the place cells respectively.
And then how you could project that information into an egocentric frame so that you can imagine it.
So that's a necessary thing that has to happen if we want
to make contact with with human imagery and so
that's why i put it in the model um in principle you could solve vector navigation
just using the the grid and place cell model and maybe do that you need this
extra stuff to be able to sort of visualize uh but of course that gives you
know extra functionality you might Right.
In principle, if you've got a lot of stored abstract information,
you could pull it together to give you the viewpoint, if you like,
on all of that information that's consistent with being in a single location.
And that's a very powerful way of reducing the enormous amount of stored information.
You're just going to pull out all those bits that are consistent with me being
here, possibly facing this direction.
Now what what is around me and if
you think conceptually you've got
a whole lot of stored information a whole sort of life's worth of experience
if you like uh if i ask you about jennifer
aniston you probably want to pull out just the information relative to
to that location in in the conceptual space
of all actors or people that you've ever seen and so
uh you know it's more that these are
ways in which memory could function you have to consider the retrieval
process you could store an awful lot of information but
how do you retrieve it and episodic memory seems to a very specific way of retrieving
information you impose a particular location and perhaps a particular direction
to interrogate your stored data
with but now in the model itself gives you like a lookup table, right?
You can sort of project back from the egocentric views to the allocentric views.
And to what extent is the model then able to capture also the physiological
and anatomical characteristics of this hippocampal system? Yeah.
Well, it's quite a high-level model because it's trying to deal with cognition,
and we know so little that it would be inappropriate to try and build too much
physiological and anatomical detail in from the start.
But this translation circuit, as shown by Alex Puget and various other people,
corresponds to the observed parietal gain field neurons that have been recorded
and should function as a translation circuit irrespective of what it is you're retrieving,
from allocentric or egocentric coordinates and translating into the other.
I'm not sure if that answers your question, actually. Well, because it's also
a bit a step towards the experiments you did with human subjects where you then
start to match the response of the model to fMRI experiments with human subjects
that were in some sort of navigational test.
So how well was the match then between the fMRI results and the predictions of your model?
Well, in terms of the fMRI, it was more that we could have, to most of the areas
of activity, the areas in the brain that showed increased metabolic activity
during this kind of task,
we could ascribe some kind of putative function.
It's very speculative, but the model says what that activity should be actually
doing. you know in the hippocampus the play style should be doing this and in
retrospinal cortex it should be a translation circuit or whatever and so it's
not really that there was a um.
Particular the model wasn't good enough
to constrain um what activity
you should see it's more that my choices of
where these bits of the model should be in the brain were broadly
borne out and they give us some kind of way of trying to understand what that
activity might represent in terms of mechanism so the model would be as heuristic
to help you not to further think it helped to interpret those results it wasn't
that we predicted results and you know We could measure the error between the
predicted results and the actual results.
But now, in terms of the performance, so you had humans in a virtual reality
environment that they navigated around.
There was a target that they had to sort of memorize.
So we did do a behavioral experiment where we did match the behavioral predictions
of the model and the behavior of the people.
And in that case, yes, of the models we tried,
the boundary vector cell place cell model was the best match to the distribution
of responses across participants of where they thought this thing was when you
change the shape and size of the virtual room.
Right. But we discussed this also during your talk which is I think an interesting
challenge now because you basically took the response of all the subjects.
The subjects had to estimate where they were relative to this target.
So this gives you distribution over all estimates over all subjects, right?
And then you show that the model,
your model could capture well the distribution over that whole group.
But now if we split it out to the individual level, and also you showed that
data, you actually see that the individual distributions can have a rather different shape.
Actually, it's clustered in, let's say, three different groups.
Much less variance within subject than between subject.
So then in the end, what are we modeling, right? Are we modeling statistically
over populations, or do we have to take into account, must we be able to also
account for these individual differences?
Because you saw that the subjects that were accurate had a pretty nicely clustered set of responses.
And indeed, the ones who were inaccurate. Well, to be fair, actually,
in that experiment, because you've changed the environment, there's no right or wrong, in fact.
You know, you can say... They had a task. They had a task to put their marker
where they thought the flag was.
That's it. But the encoding environment had changed.
Sure. So it's a bit of an arbitrary question, and they did their best.
And some people, they made different guesses.
But to answer your question about what the model was showing,
I think the model was showing that as a population,
people had a vague idea where things were that related to the walls of the environment,
and the model captured some aspect of that but didn't capture the individual
differences by any means at all.
But it was interesting that as a group, they kept within this sort of very broad framework.
Distribution that the the model could show i mean we could have
cranked up the model and said just sliced off the very top uh
likelihood match for location and
that would have been you know a very narrow uh distribution
and some subjects put their responses there and others put them miles away and
some subjects were also confused right did unacceptable things but looking at
the at the distribution across the subjects we see three groups right there's
one group that say let's call them accurate and they're they're at the northeast
corner of the environment close to the wall.
The second cluster is sort of southeast, so close to the wall,
but they're like reversed, right? They've mirrored, if you want that space.
And the third group is completely off in the opposite corner.
So is there a set of parameters in your model that would be able to then tune
the model to capture each of those clusters of responses accurately?
Well, I think that there was one participant that was off in the corner.
I think we have to just ignore, who knows what
they were thinking about then there's two other groups of
responses and indeed they're not uh one is within
to see the blue one there is actually some they've responded in
both um groups um but
it seems like of these subjects more of them
because the original location was nearer to
the north wall and the south wall more of them were if you
like trying to replicate that distance to the south wall although
they don't replicate that actual distance it's stretched down somewhat and
then fewer of them are perhaps if you like paying more
attention during encoding to the distance to the south wall
and they're replicating that to a greater extent and so
who knows as I showed their viewing direction it's not that the ones paying
attention to the north wall were all looking at the north wall some of them
were but there's a few that were looking at the north wall that matched the
distance from the south sure but it's interesting because then the model replicated
something that was not present in any of your subjects Dudes.
Well, we did discuss this during the talk. I think some of these subjects who
are what you call quite accurate. The blue one, okay.
Yeah, well, and maybe this sort of reddish one that's in there as well.
A few of them may have actually fitted.
But yes, the variance across subjects is not captured in the model,
and it seems like you'd have to have different weights for the north-boundary
vector cell and the south-boundary vector cell and tweak that between subjects
to get this sort of response in a way which wouldn't be very satisfactory.
No, exactly. But you could do many, many trials and then see if you're still
predicting their responses, if you could find some way of knowing which wall
they're going to be paying a bigger weight to.
We didn't try and do that, but you might be able to. But do you see this as
a critical challenge to the model, or you think that this is a detail that you can handle?
Well, so that model only
requires the boundary only vector cells and the place cells and so now
we have this more elaborated model which includes the the visual visual
imagery and would allow us to also incorporate what some of them seem to be
trying to do by lining up their viewpoint at encoding and retrieval which would
be to match the current visual scene with the imagined visual scene from encoding
we could try to add that that would be you know these three different frameworks that i mentioned at the
beginning i think the spatial
updating or path integration probably can't be
used here because there's a random teleportation to
a different spot uh before they make their response but the other two the visual
matching and the environmental location surely are relevant so we could try
adding the visual matching to this and see if that improves and maybe if something
about the the orientation at encoding gives some of this inter-subject variation,
because the encoding positions are all different.
That might be a way to go. We haven't tried to do that so far.
But that would mean that models on navigation would have to start to include
a notion of individual style.
Only if you want. I mean, yes, if you're interested in those inter-individual
differences, or you might say like we did in this original experiment, who cares?
Let's see if we can get any kind of match to anything averaged over whatever.
Yeah, but now you have matched to an average person that doesn't exist. Yeah.
It's a typical problem in modeling, right? Well, and other good points could
be made, like all models are wrong. Yeah, sure.
But actually, a model is only replaced by a better model also.
Yeah, no, no, absolutely right. So, look…,
Let's get to the – you then started to map these ideas, also reasoning from these models.
You looked at the response in the human brain using fMRI.
But now the second problem that you could face there is, of course,
also the matching in time, right?
Because initially we talked about grid cells, place cells, so on,
and we talked about electrophysiological data. This is all happening in milliseconds.
And now we are validating the model against fMRI experiments where we actually
look at a very different spatial temporal window of response that's measured in seconds, right?
So how do you overcome that challenge?
And in some sense, the dynamics of the model is completely outside of the window
of the measurement technique that you use to validate the model.
Well, we made predictions that were appropriate for that kind of testing.
We also use MEG and intracranial recording, which has higher temporal resolution, of course.
But for predictions for fMRI, the most common kind of prediction is that,
on average, during this task, this particular part of the brain is going to
be a bit more active because there's neurons there that are doing something
that are required for the task.
And that's the kind of prediction that we made. though you may be referring
to the sort of grid cell like experiment where we try to make a prediction for
for what would be the time averaged bold signal as a function of running direction,
over the whole trial um by noticing that the the grid cell firing patterns tend
to be aligned across the whole population of grid cells we try to make a prediction
for a difference according to to alignment with the grid axes or misalignment
with the grid axes in terms of your running direction,
which we could look for on this time-average data over the whole trial. Right.
But that was a brilliant idea.
How did you stumble into that? Or was it really like a sudden insight,
like, okay, the orientation of the grids has a certain discretization.
And given that discretization, we must see alignment or misalignment,
and we can exploit that in our bolt signal?
Was it really like... Well, I should say, so the other two authors who were
both postdocs in my group at the time, Christian Dohler and Caswell Barry, had been wanting...
So Caswell Barry was recording grid cells in rodents and Christian Dohler was doing fMRI.
And he really wanted, the two of them really wanted to be able to see something
in fMRI that might somehow relate to grid cells. And so we did discuss it for,
you know, some years before.
And Caswell noticed that these grid cells were aligned, the grid patterns were
aligned. And so we tried to eventually...
I had heard of quadrature filters,
which are a good way of looking for a particular phase orientations.
And so, you know, we put this method together and eventually applied it to data
that we'd actually recorded over the previous four or five years for other reasons
prior to coming up with this particular idea for looking for a grid-like signal.
Right. It's an amazing result. But now it's the 60 degrees.
Is that, let's say, the optimal, if you want, misalignment between grid cells?
Is that where you would find the strongest competition between their responses?
Or do you see the 60 degrees as some sort of canonical feature of their alignment
in the human entorhinal cortex?
No it's it's just that the actual firing
pattern itself is um a regular
triangular grid and so it has
orientational symmetry of 60 degrees and
so that's why every 60 degrees of rotation you should see a similar if you're
looking ahead you're running in a straight line ahead then you should see a
similar pattern of uh firing fields of all the different grid cells in front
of you because it looks the same every time you go through 60 degrees.
That's because it's a regular triangular grid.
But that would mean any multiple of 60 should also work. Yes.
Yes, it does. I mean, the control experiments in that paper was looking at things
that were not multiples of 60.
30 also should not work because that in fact should be the optimal disambiguated.
And that's what we compared, sort of 30 versus 60.
But we also looked for you know, instead of six-fold rotational symmetry,
seven-fold, five-fold, four-fold, eight-fold, and we did not see that.
So that was the control for our method. Right. So why do you think the triangular...
Well, actually, that's an interesting point. So if you were an engineer,
which you probably are, I don't know.
I'm a psychologist. You're a psychologist, okay. Well, if you were an engineer,
then you might design some kind of navigation system or something to provide
a metric on an X and Y right angular axes.
So why do we have sort of axes that it's more like triangular axes with a 60
degrees instead of 90 degrees?
And um one i think um one reason
you can see from um from dead
reckoning so in sailors that were navigating they would um try to have multiple
lines that they were integrating across and uh the reason why three is better
than two because if i'm estimating my displacement in x and in y it gives me
a location but if i'm doing it on three axes then any pair of them will give me a location,
and I can check for my error by comparing it with any other pair.
And so by having more than two axes, it's redundant, but the redundancy is useful
because it tells you when you're accumulating error.
Right. Well, an alternative is that a triangle is the optimal way to cover a
sphere, which would be interesting for grid cells because that means it's sort
of a never-ending, right, attractor system.
Them um well you
you could so it's close-packed and so
you might think that you you have um you
know 2d space represented on a torus in fact yeah and you have close-packed
representation on that surface and and indeed you know hexagonal close packing
is is is likely um is another good good reason for why that you might end up
with grid cells that's right there are models of how how grid cells could be formed, say,
from place cells in terms of compression or whatever,
and you would end up with hexagonal closed packing.
That's right. Although in some cases, if you do the PCA, certainly in rectangle
environments, you end up with 90-degree grids also.
So in our models, it's a twisted torus, essentially, with triangular packing
because that's optimal. Yeah. Okay.
So the other thing that you showed towards the end of your talk was actually
an interesting effect that if you have connected rooms but not continuous rooms,
and you measure the grid cell response,
along these rooms, that they actually are sort of adjusting themselves with time, right?
So how large can these adjustments be, right? So what kind of shifts can you observe?
Because it seemed to look like if these are connected rooms,
They converge onto still a consistent mapping, a consistent coverage of then
these two rooms as if it is one.
So what was the question? Well, what kind of modulation can you expect to see there?
Well, so Francis Carpenter, who did this experiment with Caswell-Barry,
he recorded for up to 21 days of experience of the rats walking between these
two rooms along the corridor.
And after that long length of time, the grid-like patterns were becoming,
in the two adjacent boxes, becoming more like a global grid that covered both
of them. but still hadn't got the whole way there.
And so I guess what you could expect with experience would be adjustments that
can cover up to half a grid wavelength so that the two grids can come into alignment
whatever their starting phases are.
It's interesting, in that experiment we had.
Perceptually identical, as far as we could make it, boxes so that the orientations
of the grids would be aligned.
I don't know if it might be much harder for the system with misaligned grids
into two rooms to bring them into alignment.
Although experiments by Jeff Tauby's group has shown the equivalent thing in head direction cells,
that if you have two different boxes in which head direction cells have different
directional tuning and you join them with a corridor that the rat can walk through,
then eventually they line up and become consistent.
And I would see these all as part of the same process of trying to build some
kind of representation of space which is consistent to path integration between
the different bits of space.
And do you see that as an adjustment driven by, let's say, CA1?
Or is it extra Hippocampal? No, I would see in that case,
because it's the path integration by walking backwards and forwards,
which is presumably driving the reorganization because that's what tells you
the relative locations in the two boxes.
So it's probably the grid cells trying to, if you like, align according to their
movement-related inputs that
drives the slow remapping that you see in CA1 in this type of situation,
where place cells that are originally firing identically in the two boxes,
begin to slowly disambiguate the two boxes.
But there's still some monitoring process that can then check whether the grid
cell response becomes more or less regular at some periodicity, right?
Otherwise, you won't get the correct alignment because there's a phase and spacing
and orientation that has to be aligned.
Yes, I'm not sure there's anything checking. it's more like simultaneous localization and mapping.
So in robots, you know, you come into a new environment, you try and you've
got movement detectors, you've got sensory inputs, you try and build a map that's
consistent with everything.
And I think in this situation, perceptually identical, the perceptual feed forward,
you know, boundary vector cells, and so on to place cells was dominating and
making identical replications of patterns in both boxes.
But eventually, it manages to do something like slam
and make a representation which is
consistent with the path integration okay but
i would see it as you know you can't put one
before the other it's trying to come to a compromise of both
and there's nothing checking okay but the
plasticity sits in the entorhinal cortex driving
this or the or in the hip or in the ca1 or ca3 well
there must be plasticity uh i
was going yes i'm not sure actually i was what
i was going to say was there must be plasticity between the play cells and the
grid cells because the alignment must be but i'm not sure now actually it may
be that um you know the play cells are trying to anchor the grids to the environment
and the grid cells providing path integration input to the play cells it may be that.
That doesn't require plasticity, and that the plasticity happens as the grids, if you like,
pay more attention to their movement-related inputs than their sensory-driven
inputs from place cells and become more path integration-driven.
And that, as a consequence, changes the place cells to become more consistent with that pattern.
Exactly. And that actually didn't require plasticity between the place cells
and the grid cells. Right, yeah.
And on top of that... But on top of that, then you must have plasticity with the two kinds of inputs.
So I've posited the movement-related input that we think is driving the grid
cells and the environmental sensory input, which I think is driving the place cells.
There must be plasticity in both of those inputs, because in the end,
a place cell in one of the environments will be firing differently relative to its sensory inputs.
And initially, the grid cells will be firing inconsistently with path integration
because they're driven by the sensory input.
So the plasticity in that model would be not between the place cells and grid
cells, but between the place cells and the central input and the grid cells
and the path integration. Right.
But also because this emerges relatively slowly, right? It takes a long time to get this alignment.
So how many hours does the animal spend between these two? Well,
that would be a couple of hours a day for many, many days. Exactly.
And so one valid question is if you made the task depend on knowing where you
were in each box separately, maybe it would happen faster. Who knows?
That might be worth trying because it's a horribly long experiment to run in
its current form. But we don't know. Okay, great.
So we talked earlier about the concept cells, and in some sense it's now...
The hippocampus is almost making Jennifer Aniston more famous than she was ever before.
But we also have a new concept cell, which is now representing the concept cell of Jennifer Aniston.
So if we start to think about generalizing away from spatial cognition and just behavior in space,
and we start to think about other domains in which you can use these capabilities
of the hippocampus, we still would need some sort of path integrator that drives that system.
So imagine we're going to apply this now to, let's say, a complex cognitive
task, and we're going to use the computational capabilities of the hippocampus.
What would then stand in for its path integrator if I'm not moving in physical space anymore?
More well i think as
we um discussed with this stretchy bird
experiment and the grid cells if you have a
nice grid cell representation it's very powerful and can represent the vector
2d vector relationships between uh any two points in a very large space this
could be very useful for mapping all sorts of things concepts for example where
the vectorial relationship would
be on vectors of neck length or leg length or who knows how much I like them
versus how much they wear expensive clothes or anything.
But it would be a powerful system for representing whatever that conceptual space is.
And although path integration was probably useful for wiring up these grid cells
in the spatial situation, if you have that representation somehow.
In this non-spatial situation, and maybe it's developed as a PCA of these non-spatial firing patterns,
or maybe it's somehow the conceptual problem is mapped onto space implicitly.
Either way, you then got a very powerful system for understanding relationships
between different concepts.
Okay. So Neil, to finish up,
okay, you come from physics, went to theory, and then to experiments,
now you combine this in animals and humans, and you also have been part of some
really great discoveries in this field,
and which also was recognized recently by being elected as a fellow of the Royal
Society, which is a big compliment to your work, so congratulations.
But the question now is, So given that experience, what would you see as Neil's
law in the study of the brain?
I don't think there's any such thing as Neil's law. I do think that if you're
trying to model something complicated like cognition.
Then at the level of neurons, you need to start with the simplest possible model
because we have so little idea about how neurons do represent things like cognition.
Obviously, in the spatial domain, we have some great clues from all of this work we've heard about.
And so i tried to always use
the simplest model um and also
it might not be a mathematically tractable model although
it would be great if if it was but it doesn't have to be it's not clear the
brain is going to be mathematically tractable and it needs to make experimental
predictions and indeed experiments have to um have something to say to to theory
other you know they each only exists with the other in some useful sense.
If you do an experiment, it doesn't impact any theories.
What was the point? If you have a theory that doesn't impact any experiments,
you know, maybe it'll be useful one day, but it's not that useful right now.
But wait, the law of defining laws is they must fit on a t-shirt. So what's the law?
Uh keep it well i
think i think there's a um somebody famous said something
like you know if i could capture my contribution maybe
it was feinman in in one sentence it wouldn't be much of a contribution okay
i don't think that's true i think there are some great discoveries which can
be captured on a t-shirt but i'm not sure but i thought you were saying you
know i thought you were saying keep it simple yeah that's what i that would
be fine keep it simple keep it grounded in experiment.
Okay, good. Great. It's not a very exciting t-shirt.
Well, it depends what else you put on it.
Or who's wearing it. Maybe Jennifer Aniston. But look, five years from now,
I'm going to smuggle myself into the UK because by then, after Brexit and the
whole disaster that goes along, I will not be allowed to enter anymore in any legal way.
And I'm going to come to your lab because by then you'll still be there.
And I'm going to check whether a specific prediction you made today was actually verified or falsified.
So what's the one prediction you really would like to see tested in that five-year frame?
Well, there's a few.
I mean, I didn't really talk about it because there wasn't time and it seemed
a bit complicated, but I really would like to know if.
Temporal coding and theta phase procession has something to do with path integration.
And it may take nearer to 10 years but I hope that one point we'll be able to image grid cells,
and their dendritic inputs to work out what it is that's making them fire in
a grid like pattern and there's a clear prediction that you should see oscillations
of different frequencies in these different dendrites and I would I would like
to see that at the more cognitive level there's some
applications to what happens in post-traumatic stress disorder in terms of different
forms of memory supporting imagery or impacting on them in different ways.
And it would be nice to see if some of those predictions have come out that
this model of imagery had some clinical relevance.
All right, great. Neil Burgess, thank you very much for this conversation. Thank you.
The CSN Podcast was produced by the Convergent Science Network of Biometrics
and Biohybrid Systems, a project funded by the European Sevens Research Framework Program.
For more interviews, recorded lectures, or upcoming conferences in the field
of biomimetics and biohybrid systems, go to csnnetwork.eu.
Music.