You look worried, Edward. Yeah, I'm not sure what's coming now.
This is the Convergent Science Network podcast. So it's good cop,
bad cop, and Paul's the bad cop.
Leading researchers in the domain of neuroscience, brain theory,
and technology are interviewed by Paul Verschure and Tony Prescott.
After he's finished beating you up, I'll give you some easy questions.
So, all right. So this is Paul Verschure, together with Tony Prescott for the
Conversion Science Network podcast that we're recording at our BCBT summer school
here in Barcelona, 2015.
And our guest today is Edvard Moser, who gave a fantastic talk this morning
about how the brain knows about space.
And you started your talk emphasizing this whole challenge of combining psychology
with physiology to find, if you want, this sort of physical mechanistic perspective on psychology.
Is that really the motivation that drives this work? Yeah, in a general sense, it has always been so.
When we started out as psychology students many years ago,
it wasn't possible to say much about the physical substrate of psychology or behavior in any sense.
But still that has been a major driving force.
The fact that I ended up in work on space is kind of a coincidence.
It's partly because I started out in the hippocampus and partly because it turned
out that this part of the brain has cells that are so directly related to what's going on in the outside.
It's actually an easy way into the cortex, but it could have been any function.
So, I mean, whether it's space or if it's some other cognition, doesn't matter.
I think what is the underlying drive to me is that it is informative about the workings of the cortex.
So, in a more general sense, it helps us to begin understanding how the cortex
computes, how functions might arise.
Of course, only an early beginning, but it's an easier place to start than many other brain areas.
But now when you made the decision to then go for hippocampus,
was it in any way, let's say, inspired by the cognitive behaviorism of Tolman?
To some extent it was. It was inspired by the fact that much of,
this was in the 80s and early 90s, and then there was a huge interest in LTP,
long-term potentiation, and its relation to memory.
And of course memory was strongly linked to the hippocampus.
So there was much interest at that time in finding a cellular mechanism of a
behavior, which then in that case was memory.
So that was the background for starting with the hippocampus.
At that time there was not much work or interest yet in neural networks.
That came or increased quite a lot during the 1990s, but nonetheless,
the possibility for bridging two levels was perhaps more developed in hippocampus
than most other parts of cortex.
But in that sense, I was a bit surprised that you didn't mention Pavlov as a
source of inspiration, because what always struck me in Pavlov was that he had to make that decision.
Vision like here the dog has expectations so what
do i do i'm going to speculate about it or i'm going to build a physiology of
this of the psychic reflex as he called it right yeah
no no that's pavlov was uh was really uh he really changed the field just in
that sense that he dared to link the two levels so um i mean i could have mentioned
pavlov to my history i lasted about four minutes So I couldn't mention everyone,
but of course, Pavlov is a major part of the history of physiological psychology.
So now your entry point into, let's say, this neural substrate of psychological
function is hippocampus, right?
So you started looking at place cells and place cell responses.
So, however, that was not where you ended up.
So, what was the trajectory there of, let's say, discovery that brought you
to these extra hippocampal areas?
So, it began with place cells. And around 1990,
it was a common view that place cells were formed quite strongly by intrinsic
processes in the hippocampus.
And the reason was that at that time, no really specific spatial signal had
been discovered in the entorhinal cortex outside the hippocampus.
So it began with, as I mentioned in the lecture, a study where we disrupted
the intrinsic hippocampal circuit and then saw that in the remaining part,
there were still spatial signals.
So it sort of forced us out to the next stage, which was the entorhinal cortex. Cortex.
And then, uh, because we had, uh,
and that collaborated strongly with a neuroanatomist, Menno Witter.
Who has later moved to our institute in Trondheim.
Then we had an expert on how the entorhinal cortex was organized and what would
be the best way to target electrodes into it.
So we dared to jump into that area, and then suddenly we found spatial cells,
cells and later found also that they were actually quite strikingly hexagonally organized.
But now, how many observations did it take for you to be convinced that these
cells had these very specific properties that you found? Now that took a while.
I mean it was gradual because we realized quite early that they had spatial
fields like place cells in hippocampus.
And we also noticed that there There were regular patterns, and we had,
along with the first paper that we published in 2004, we noticed that it was
extremely regular, much more than you would expect by chance.
We showed that, but the data were not sufficient to really tell what kind of
pattern it was, and that required bigger environments.
And then the year after, we then tested them in larger environments,
And then it was very clear, really.
So, I mean, that didn't take a lot of work,
but we also needed to rule out pretty obvious things like whether the grid pattern
maybe was an artifact of some part of the electronics of the system or so,
because it was so regular that then alarm clocks started to ring.
But that was not the case.
But now, I remember these first publications that came out in 2005 about on
the grid cells, it still looked like a very risky proposition.
It looked like iffy in some sense, like, oh, well, maybe they're over-interpreting this data.
So you found these cells in an medial entorhinal cortex, supposedly an input
station to the hippocampus, even though this has an interesting twist a bit later on.
So, you say, okay, there's a grid-like response, they have a triangular kind
of response field in the environment with a certain facing and orientation and spatial scale.
So, but how much resistance did you then receive in actually getting that published? Yeah.
Well, not a lot of resistance. Actually, people tended to believe it right away.
So I think people were amazed, but there was very little skepticism.
And I think the data were quite clear. I mean, you could see it in individual
cells. It didn't really depend on any sophisticated analysis.
So it was very hard to actually think of alternative ways that you could get this data.
You mentioned that you had the help of somebody who knew entorhinal cortex.
So is that why you found these? Because people had looked before and hadn't
found anything that was a signature for space.
Yeah, and also what we did was that we started recording in a more dorsal,
more superficial part of the entorhinal cortex where no one had recorded before.
And in the collaboration with this neuroanatomist,
it was possible to target the electrodes precisely to an area that had the maximal
connectivity with the play cells that had been recorded in the hippocampus.
What had been done in the earlier studies was that people recorded in areas
that were too ventral, too deep into the brain.
And they were probably also grid cells, but because the scale is so different,
the distance between the fields is different and the fields are so much bigger,
then when they recorded in standard size boxes, they didn't see the periodicity
simply because they didn't have enough fields.
So, okay, you discover these cells, people buy it, and they're convinced because
there's no alternative explanation.
Yes. But now, what do you see as their key properties?
And how do you see these key properties organized in this piece of brain?
Well, it has several key properties. It's quite actually organized in the sense
that they vary along several dimensions. They have different phase or XY firing
locations, they vary in scale, they vary in orientation.
So one of the key properties, it
has turned out later, is that they are organized in what we call modules.
So clusters of cells with very similar firing properties.
So there are at least four or five of them, maybe as many as ten,
of cells that within each module, the grid cells behave in a very rigid way.
So that two cells that, for example, have similar phase or similar firing locations
in one environment will also have it in another.
If they have a special orientation difference, then they would have it in a different one too.
So the whole map is very, that's another very salient property of the network,
that it's extremely rigid.
So you can almost take the map from one environment or from one grid module
and apply it onto another environment and you will see the same relationship.
And that's probably a property you would expect of any system that serves at
least partly as a metric for space because you don't want to reinvent that mechanism
for every representation of maybe several thousand environments that you have stored in the brain.
So that at least two very important
properties of the network but
now these cells don't emerge by magic right
they themselves are also dependent on on external inputs yeah of course they
are embedded in a wider network and and first of all although we believe that
the clues to the the hexagonal pattern lies in the cortex or maybe even in the
entorhinal cortex itself.
It cannot arise in isolation.
So it depends, for example, it must depend on,
on both speed and direction inputs that are likely to come from outside because
there's no other way that you can actually create a dynamic map that reflects
the distances that an animal moves in the environment.
So fundamental inputs are information about speed and direction,
instantaneous speed and instantaneous direction.
In addition to that, so this is what is required to generate a map that is based on self-motion.
But in addition, you need to calibrate that map all the time against other sensors,
for example, visual inputs.
So there is probably a continuous correction process going on all the time as
well. What do you mean with correction in this case?
Yeah, so path integration is a clue here. So the grid cells,
if you let an animal walk in an open environment, it may have a grid pattern.
So if there are lots of visual inputs available, then the grid pattern will
use those visual inputs for the cell to fire at the same locations all the time.
So that's a stable grid pattern. If that input is not available,
it will start to drift over time, because even if it uses the animal's own motion
to generate an approximate firing map,
then there are occasional errors and they add on to each other unless you have
other inputs that tell you that now you're drifting off.
And what I believe happens in real life is that when lots of other cues are
available, like visual cues, visual information, then this is used to sort of
get the motion-based map on track again. Mm-hmm.
Yeah, so this would also allude to, let's say, these experiments you've done,
even though you didn't discuss them here, where you, for instance,
morph the environment slowly, right?
Where you can really sort of try to show how this kind of integration of other
sensory states, like visual information, with space might occur in this whole
loop that then starts with the entorhinal cortex.
Yeah. So, but then do you still see that there is a special role for grid cells
in that integration process?
Because it means, okay, here I have sensory states in the world.
They come in over my lateral interanal cortex.
I have heading direction and velocity driving my grid cells, tells me about space.
This information gets further processed in the hippocampal loop.
But then do you see these two sources of information as being equally weighted
in that integration? or do you see the grid cells as having a higher priority in that process?
Well, I wouldn't say that one has a higher priority than the other.
I mean, the grid pattern is probably intrinsically generated and as such is
quite fundamental, but it needs that other input to be aligned to the environment.
And of course, both of them are equally important. And I should also add that
grid cells may help to create a spatial reference frame,
but what goes into the hippocampus is equally much dominated by,
for example, inputs from the lateral entorhinal cortex,
which we understand much less, but which may be informative about all the other
types of changes that occurs in an environment like the experiences that an
animal has while it's walking around in the space.
But now if we just look at, let's say, cell numbers, which of these two divisions
would be, let's say, dominant from just a perspective of cell volume?
You mean medial versus lateral entorhinal cortex?
I would think that they are both important. I mean, the number of cells is about the same.
It's just that we don't understand the lateral input much at all. Right.
You mentioned that the grid cells occur in these modules, and the modules are
at different spatial scales.
Does that mean that across the whole set of modules, you're imagining that the
animal has access to a unique code for its location in space.
Will it operate in that way? Because obviously within any single module,
you know you're on the grid, but you don't know exactly where you are on the grid.
But maybe relative to the other modules you can build up a more unique… Exactly.
You need to use the modules in combination to get a unique code,
because otherwise there are multiple solutions. Yeah.
So that can either happen within the entorhinal cortex itself or just as likely
it may use the hippocampus where the modules are likely to converge onto play cells.
So how that occurs is not known,
but I would think that it would be very smart of the brain to actually compare
the activity of different modules at any given time and not just let them drift on their own.
And does the connectivity into the hippocampus suggest that,
for instance, place cells have that ability to read out from the multiple modules?
Well, that's what we are investigating now. It's not a very simple environment
because you need actually to determine what are the inputs to an individual place cell.
But now with rabies virus tracing, you can actually find the functional inputs
onto single place cells.
And the aim then is to determine whether grid cells from different modules converge
or whether they actually somehow stay separate in hippocampal cells.
So in this example, we are following, let's say, a causal chain that would go
like, well, we have entorhinal cortex, these inputs go into hippocampus,
grid cells are a part of that, 50-50 with lateral entorhinal cortex telling you other stuff.
But this provides some key information for a place cell to give a response to a location.
And this if you want we could call this sort of the standard interpretation for quite a while,
but that that interpretation is now under some
under some challenge right because apparently also without grid cells or without
this connection or the grid cells might not even target those cells directly
them or you can even get rid of them so what's the situation there in your perspective
are the grid cells really key in driving a play cell response or they're extra Yeah,
I'm pretty sure that the grid cells are still key in driving place cells under normal circumstances,
but the place cells appear to be responsive to a lot of inputs and can probably
make some sort of spatial signal even out of other cell types that have a spatial bias,
like cells in the lateral entorhinal cortex that are also weakly spatial,
but still have enough information that at least if you average over many cells,
you can tell quite precisely where you are.
And place cells seem to be able to use that.
It doesn't mean that they don't normally rely on grid cells.
I would still think that grid cells and border cells are the major inputs.
But play cells are able to do the best out of very little.
Though when play cells, in cases where the medial entorhinal cortex is lesioned
or grid cells are somehow otherwise inactivated,
the play cells are usually not very normal.
I mean, they're very unstable, for example, and they are also apparently not
able to switch between environments, so changing maps is also not easy.
So it's not a normal network, but
the threshold for hippocampal cells to become a play cell is quite low.
But that means that you see actually two complementary models of play cell formation,
because one could be also just this older idea from O'Keefe and Burgess and
others, that you just have weakly tuned spatial responses coming in from the
lateral and thoracic cortex.
And by just integrating over those, you can then get a space-specific response.
And the complement would be the grid cells that will give you a redundant response.
But by averaging over many of them, again, you get specificity.
So then these two modes would be operating in parallel.
Yeah, it's two ways. And they are not mutually exclusive.
But I think what it shows is that it helps
to have these spatial inputs so you can probably use several of
them and then there must be some intrinsic hippocampal
processing that we still don't quite understand may involve neuronal plasticity
may also involve other circuit mechanism that then that help shape a play cell
out of maybe not so precise spatial activity right so the other thing you can do is
you can combine if you like the sort of metric properties of your grid cell map
with the sort of more topographic relationships that
you would get from sort of cues in the environment yes
and so you could build up an idea about what's adjacent to what based purely
on those features and that could give you activity in your play cells independent
of your metric map yeah i believe that both both mechanisms are likely to be
used so it is somewhat redundant.
That's also probably explains why at
very early ages when grid cells are still not highly periodic and in an immature
state you can still get nice place cells because for example you have the border
cell inputs are already intact from the first day and there's also evidence from.
From groups at UCL in London, which suggests that at that early stage,
the place cells are more precise near the borders of the environment and then in the middle,
which is consistent with grid cells having a role where they sort of map the
entire environment and the metrics of the environment where border cells are
maybe more responsive to the specific landmarks and especially in geometric references.
I think also we touched on the talk on the effect of environmental context and
things like the presence of daylight.
So if you're moving in darkness, you may be more reliant on this grid cell map. Absolutely.
If it's a nice sunny day and you've got access to lots of visual cues,
maybe you don't need that information so much. No, no, that's true.
So, I mean, in most cases you have much more cues than you actually need.
So it's quite hard to perturb the system.
But in darkness, self-motion is more important.
Although you can still do quite well even with just tactile cues.
You bump into the corners and so
on, so that you can at least occasionally reset and get the map to work.
But of course, as you get out in the open space, there's no other reference
than your own motion. And for that, grid cells are probably quite important.
But now the situation has gone, actually has become more complicated because
now you have also identified many other cell types in this little bit of brain, entorhinal cortex.
Like we're now with border cells, with speed cells, we have head direction cells, right?
So what's the relative proportion of these different types of cells in entorhinal cortex?
Yeah, in our experience, still the most abundant cell is definitely the grid cell.
At least if you search in the superficial layers in layer two and also to some
extent in layer three, maybe even almost half of the cells in layer two seem to be grid cells.
It's a bit hard to tell because as you get, at least if you get further deeper,
then you get cells with larger scales, not always so easy to tell if they are grid cells.
But in addition then, we also have, as you said, the head action cells there.
In layer 3 and 5, they are very abundant, maybe the most abundant cell type.
And then you have border cells, seem to be around 10%.
They are in all layers, but especially 10% also in layer 2.
And speed cells are maybe about 15% across all layers.
But for the speed cells, for example, even if they are only 15%,
many of them seem to be interneurons. They have interneuron-like properties,
at least, which means that they connect to a large number of cells.
So their influence, even if it's only 15%, is probably extremely important.
They probably influence every single grid cell.
But you interpret, so as you say now, right, you see those different cell types
very much as forming local circuits that help the grid cells to stay on track?
Or you see them as a forward pathway into the hippocampus?
No, well, both, of course, but I do think they are important for the local circuit
because the grid cells need the speed and the direction cells to stay updated.
But, of course, the result of all of this is then fed into the hippocampus.
So, I mean, they're not mutually exclusive. But so now, also in your talk,
you then gave us an interpretation of how these different cells might work together.
In particular, you were talking about how the border cells might actually be
interacting with grid cells to sort of help them in aligning to an environment.
So how does that work out?
Yeah, I mean, what the role is of each cell type is of course still a matter
of speculation because we don't have the tools to manipulate it.
But what we see with the grid cells is that they are heavily influenced by the
borders of the environment.
So as you get close to the borders, then the grid cells tend to get distorted.
So they are not perfectly hexagonal any longer.
And that can be explained by forces that operate along the walls and then both
deform and rotate the grid patterns in certain ways.
So most likely this is mediated through border cells because they have activity
that corresponds to the orientation of the walls
and also activity that decreases as you get away from the walls.
But how this is implemented exactly in the network is still very much an open
issue. I don't know how that happens.
Because there are different ways to… Maybe we could argue that the notion border
cell might be a bit too restricted interpretation of what they do.
You could also argue that maybe these are just cells that go for let's say salient
aspects of the environment that can be exploited as anchors.
So it's more like a salience or a landmark cell.
It could be. Now we have put individual more point-like landmarks into the environment
in the past and usually they have quite limited influence on the cells that we have recorded, but.
At least in the medial entorhinal cortex. In the lateral entorhinal cortex,
there are cells that actually do respond to particular objects and their location.
So they fire around those objects, even if you then remove them afterwards.
But those are not really part of the medial entorhinal network.
But your interpretation would really be like, I have my grid cells.
They give me, let's say, an initial description of a space in which I can operate.
This space is seen in a planar perspective it's
not three-dimensional it's a two-dimensional plane and my
border cells would really be telling me where the where the
end is of that of that flat world in which i exist you would agree with that
uh yes but not only where the end is i would rather say where there are signal
significant or salient uh reference directions so if you put a wall into into
the middle of an environment,
you will still get the border cells, some of the border cells to fire along that wall too.
So it doesn't mean it's the end, but they're very significant for anchoring the grid.
And for anchoring, it's of course most effective actually to use straight lines wherever they are.
But now you could also argue that maybe the border cells are just responding
to the dynamics of the grid cells because if I'm running over,
let's say I'm running over this table and there are edges, that also means there
are certain positions in space.
In other words, there are certain attractor states of my grid cells I will never reach.
And these might be giving you transient responses in the population of grid
cells that you can pick up and then say, aha, this is an important transient in my dynamic.
So that's the grid cell driving the border cell.
Would you buy that interpretation or there's something missing in that?
No, I think it goes both ways. So I think the border cells influence the grid
cells, but the grid cells will then also So again, influence the border cells.
I think they are probably bidirectionally connected and they work together all the time.
Okay. So now we have our map of our grid cells.
And this is tightly coupled to hippocampus, which sort of now loops back the cortex onto itself.
So what is the hippocampus doing with this information?
Well, one striking difference between replace cells in the hippocampus and the
grid cells, or all of the cells actually in medial entorhinal cortex,
is that in the hippocampus, replace cells form individual maps for every single environment.
So for every environment where a rat is tested,
it seems to generate orthogonal maps almost,
maps that are completely independent, which fits very
well with what you would expect from a memory
perspective on the hippocampus that you actually form discrete
representations for different experiences
in the animal's life
which is very much in contrast to what we have seen in
entorhinal cortex where the firing relationships
are sort of preserved from one environment to the
other and so that that is
an important feature of hippocampal activity how
that is transformation is generated that's a very important task that we still
have no data for it can happen perhaps by combining activity from different
modules because by differentially combining activity
from modules, you can get a large number of activity patterns.
But that's still quite uncertain. But of course you could also argue that entorhinal
cortex is maybe not a main source of information for hippocampus,
but it's actually a main source of information for the rest of the cortex.
So how do you see that exchange? Well, the exchange between entorhinal cortex
and rest of cortex is not very well understood at all.
Of course, just based on pure connectivity, we know that entorhinal cortex is not an island.
It really interacts with almost the entire rest of the cortex.
And we also know that navigation is not only a hippocampal entorhinal phenomenon.
The navigation maybe the creation
of an internal map involves those two structures particularly
but the internal map needs to be
used for the animal to get from a to b and for navigation in a broader sense
beyond forming maps you actually need the entire brain and then that involves
for example prefrontal cortex which is important for planning how you get from one place to the other.
So it's very important to remember
that the entorhinal hippocampal network is part of a wider system.
And I think in the future, we'll probably try to understand these other cortical
regions too, although it's a quite challenging task.
Of course. But now the other amazing result you presented today was in some sense taking
away all doubt anyone might have about the metric properties of these grid cells
because you show how amazingly precisely they are aligned with the space in
which the animal operates.
So what are the basic observations there?
With regard to the metric, I mean the metric
of… well first
of all when you started out i mean the the grid
cells um appear to be
i mean we're struck by their enormous regularity and
the fact that they form a perfect grid that with 60 degree angles that repeats
itself all over the space but then as we start looking closer and especially
in large environments it's easy to observe that the grid is actually slightly
deformed especially near,
the walls or the ends of the environment where you see that borders,
walls have strong influences and sort of deform the grid and of course that
then raises the question whether does this have any consequence for the use
of the grid to infer directions and distances.
Instances, I would still say that, by and large, you can infer position and
direction from the grid even as it is, even with these slight distortions.
But of course it would be interesting to see if these distortions that are present
in the map would also transfer to behavior, so maybe our judgment of position
might be slightly distorted also.
That's something that would be interesting to have tested at some point.
But you also showed that the grids align with the cardinal axis of the environment. Yes. Right? Almost.
Oh, and this is the weird thing about it, but it was sort of perfectly aligned with a tiny offset.
And they are an offset of seven and a half degrees on average.
So that's the axis of the, the grid has three axes.
So the axis that is closest to one of the walls is usually offset by on average
seven and a half degrees.
And that we interpret then as a result of what we call a shearing process,
which probably begins on day one when the animal experiences the environment
that is forces along walls that when distort the grid pattern and as part of
the distortion process also gets it to rotate one,
especially one of its axis.
Right. But also what was really interesting is that on the one you see that
one of the coronal axis is taken as the anchor point, if you want,
and the orthogonal one is ignored. No?
In that particular experiment, yes. Ah, okay. This is not always the case.
Not always the case. So, quite, it depends.
And in that particular experiment that I referred to, where it always chooses
one of the axes, it's important to remember that the rats were introduced to
the environment in a very, very consistent way.
So, all of the rats were placed in exactly the same corner with all or much
of the focus to cues in that along one particular wall of the box.
Perhaps this may have shaped the animal as it's in its initial formation of
the grid cell map that they may have used cues along one wall more than another.
That's our hypothesis. This is, of course, something that we will need to test.
But the effect of, I would assume that the early environment,
I mean, the environment as it is on the very first experience is very important
for how the grid actually is anchored.
Right. And also there we should not forget that these animals are,
of course, come pre-equipped with a lot of, let's say, stereotype behavior.
So you put them in that corner and they will all start to do tic-mo-taxis.
It's always run around the walls, either clockwise or counterclockwise.
So that means this early experience is shaped in a very stereotyped way for all of them.
So, and this is a bit surprising, you have this sort of more mechanical metaphor
to interpret the distortion of the grid in terms of shearing.
But the alternative would be a more behavioral interpretation where you say,
well, look, if I'm throwing the environment or with all my friends always in
the same corner, And we all will have a stereotype response of Tecmo Texas running
along their walls for quite a while.
This will give me quite a bias in my input sampling that might then lead to
that distortion of my grid cell response.
Yeah, I think those explanations are not mutually exclusive.
I think you can get what we observe or mechanically can describe as a shearing
process through behavioral filtering in a way where the animals actually focus
on certain cues and perhaps have much more attention on those than others.
So I think that is a possible implementation.
Wait, we don't need to consider attention because imagine I just do ticmo taxes.
I just run around and whether I'm paying attention or not, I will drive my grid
cells in a very specific order.
And given that this is a hyperplastic system, that then gives you the bias.
That's possible. I mean, you can even do it probably at least in principle without
attention simply by activating cells. I agree with that. Exactly, yeah.
So, but now the interpretation of this realignment of the grids or their formation,
to what extent do we really have to think about the box in which the animal
is or the animal will also consider, also going back to Tolman,
right, the global cues that are out there in the environment.
So, are these controlled for? Is this all local sensory information taken into
account? Is it global sources?
No, of course it uses all kinds of cues. But yet it is, if you test an animal
in a box like the ones we do, the strong, the local cues and especially the
walls of the environment are,
have a strong, very strong influence on most of the grid cells.
At the same time, we also see that they sometimes respond to cues that are further
away and that may even differ across grid cells.
Maybe some respond, some modules maybe, depending on scale, may respond more
to distal and others more to proximal cues. Still not settled.
But in some experiments, we simply try to close out all the distal cues by simply
pulling curtains around.
And you get essentially the same results. So the local cues are very important.
But of course, they all matter. Yeah.
But now you use the concept of anchoring also for this, right?
That you have to anchor that grid cell map in some properties of the environment
or I guess some intrinsic signal.
So if you would have to define this anchoring as a sort of a neural mechanistic
sense, how would you realize that kind of anchoring?
Well, the way I often think about it is that it's synaptic plasticity.
It's a kind of learning process where you associate the firing pattern of the grid,
a free-floating grid in a way, with a certain environmental reference that is
then somehow mediated to the entorhinal cortex via, for example,
visual inputs. It could also be other ways.
And that there is plasticity taking place, usually, I would assume,
quite early on, on the first trial.
And then that sets the grid in a certain way, and then it remains that way for the rest of the time.
But that would mean that you're saying there's some external signal that says
now this is important, anchored to this, or you see it more as a continuous
sampling that converges into some attractor state that is this anchor?
Well, I think it will use whatever sensory input is present right from the beginning.
I think this happens almost instantaneously.
Exactly how it happens, that I don't know. I think whether that involves an
attractor process as such is maybe not necessary, really.
You just need to, even if you have an attractor that shapes a grid pattern,
you just need to associate with
certain inputs so that you can start out there next time you come back.
And then in that, let's say it is an attractor state, and then you get the starting
point for the next trial when you come back.
And then in that sense, re-experience activity. But have you in that sense considered
a role for neuromodulators because here I am, I'm your rat, you put me in this
environment, I've never been here before, lots of novelty, lots of stress,
acetylcholine is coming out, dopamine is being released, very strong learning
signals that can serve then to sort of define an initial anchor.
Exactly, yeah. So that happens very fast and that certainly helps to stabilize
the map right away from the beginning.
So your prediction would be if you would, let's say, use antagonists to these
kind of neuromodulators, anchoring would be, let's say, compromised.
Yeah, I wouldn't be surprised if that happens.
Okay. So now that we know a bit more about the grid cells, you have then also
looked in detail really at very
much a psychologist's question of is this nature, is it nurture, right?
Is it an innate system, it's an innate automator in some way,
or is it really dependent on experience?
So what have you learned from those experiments? Well, it's still ongoing work,
but the initial experiments that we did a couple of years ago have shown basically
that for several of these cell types,
they express adult-like properties almost from the beginning,
at least when we can measure them.
So place cells look more or less adult-like from the first day when we can actually record activity.
When animals start walking out of the nest and cover at least small environmental
spaces, you can already measure place cells.
Head direction cells have directional preferences even before the rat pups leave
the nest because we can record them even before the eyes open.
So a directional tuning is present very
very early on and border cells also like place
places are very early there grid cells though
are a bit slower so they take another week or
two before they get adult like property so
in this early stage during the
first week or two after the animals start walking around then
they they have periodic firing patterns but it's not very regular And that probably
leaves a window for experience to actually influence the network and perhaps
help with the anchoring process.
So we did then, I presented experiments that are still going on where we have
raised rats in environments where the animals are really deprived of borders.
So one group was then raised in a spherical environment until they were adult.
And in the absence of environmental borders, we then tested these animals when
they became adult, whether they actually could form normal grid cells.
And it turns out that that probably takes a longer time,
or it maybe even may not happen, at least if the environment is large enough,
of, compared to what happens if you raise the animals in environments that do
have borders and cubes which otherwise are very similar.
So is it possible that the difference between the spherical environment and
the rectangular environment is that this mechanism whereby.
The borders or the visual cues for the borders correct for the slip and the path integration.
Yeah, I think that is a major thing that the network has to learn,
both how to anchor to associate with the environmental directional cues in the
environment right from the beginning,
but also to use that as the animal is walking around to correct at the time
when the grid pattern is beginning to drift.
And if that hasn't taken place in early development, that may compromise the
grid pattern at the more adult stage.
And something else, obviously, that's happening in those first few weeks of
life is the animal is changing enormously in size and morphology.
So if the mechanisms that are contributing to path integration are to do with
gait and body length and speed, then you can't fix those in place at day 11
when you first wander out the nest.
You have to be able to calibrate as you grow in size and speed,
and it may continue to calibrate I guess throughout life.
Yeah, that's possible. I mean, I wouldn't say that it even stops at P30.
So this is probably a process that's going on for quite a while,
and to the extent that that just length of footsteps are used by the animal
actually to calibrate position.
This is something that has to go on for quite a while. But of course they don't
only use that, they can use other sources of speed information too.
But this experiment might really help us to better the causal chain of this
system because actually the grid cells appear to last, right?
So in some sense, it's telling us something really important about the scaffolding
of this learning process.
And apparently, you really start with speed, head direction,
border cells, even place cells.
And only once that scaffold is sort of put together can grid cells be formed.
Is it also how you think about it? The grid cells really floating on that information?
Information? Yeah, I mean, obviously, as you say, the grid cells,
they do still provide some metric information even right from the beginning.
So even if you don't really see that very well in individual cells,
the population would still provide the hippocampus at least with place information.
But it certainly allots us to the possibility that you,
for the other cell types like for place cells
you don't really need a lot of grid activity for
or spatial activity for a spatial signal to
be created but i would think that at that early stage there's still something
that is missing and then that which is later contributed by the grid cells which
is the precise metrics ability to actually calculate exactly where you are and
especially when you're far away from visual cues and perhaps this is something that is not
present until the animal is a bit older.
But then do you interpret this as a stage-wise development where let's say first
I get my place cells, border cells, speech cells, et cetera,
I build my grid cells and now I liberate my hippocampus to start to dedicate
itself to different tasks.
So I move now to a different phase of operation.
Or do you believe that the system will be operating in the same state as it
was in when you were born?
Well, I don't think that you liberate the hippocampus to do other things,
but I mean, hippocampus may take on other tasks.
That's very well possible, but the entorhinal network is not really mature until
very late, actually, because it continues to develop.
The connectivity, especially via the interneurons, is not mature until you're
about, if you're a rat, until you're about almost four weeks old.
So that's very late. and i think that
is important because it really allows for experience
to shape the animal and maybe experience is
important because it's not only about creating a single grid pattern you actually
need to probably link together grid patterns for small patches of space and
and that may require experience with spatial environment right okay but on the
other hand And of course,
it also means that more frontal areas in the brain are also still developing.
And the rhino cortex also has to deal with that as an interface to the hippocampus.
This might be another constraint that we have to take into account.
And especially when it comes to planning how to use the maps and linking them
to action, that may still not be mature.
That hasn't been studied, but it's very well possible that it takes even longer time. Right.
Now, you're also talking about this link to other brain structures.
You shortly mentioned this link to prefrontal cortex that you started to look
at more recently via the thalamus.
So then how do you see that interface between hippocampus and prefrontal?
You said already earlier, prefrontal will be the planning, your executive control
system, but it is extracting specific information from the hippocampus or hippocampus
extracting specific information from prefrontal cortex.
So how do you see that interaction? direction well um it goes
both ways for sure because i talked uh briefly
only about the connections from prefrontal via reunions to the hippocampus and
uh i believe that there is that is a route for prefrontal to influence uh hippocampus
but there are reverse connections to at least via the subiculum and from more
ventral parts of hippocampus was directly back to the entorhinal cortex,
so that prefrontal circuits are updated probably by internal maps of the hippocampus.
Like all other, or many other systems of the brain, it's bidirectional and then
it gets so much more difficult to understand than if it's just a linear process,
but that's the way the brain works. Absolutely, yeah.
So in some sense, early on, the study of hippocampus was very much dominated
by a neuropsychological perspective, where people thought strongly about episodic memory,
how this was linked to certain deficits that would occur in humans after lesions to the brain.
Mollison is a famous example of that.
And in some sense, now the debate has shifted very much towards a perspective
of space, more pragmatic, autometers, spatial representation,
surviving in a box, right?
But in some sense, I would imagine also as a psychologist trying to link psyche with the flesh,
you want to claw your way back to this more high-level interpretation of,
let's say, episodic memory in the context of human experience.
Well, I still believe that the hippocampus is absolutely critical for certain memory functions.
But what has happened during the last years is that we have understood much
more out of one component of the system, which is the spatial framework,
which I believe is a fundamental reference for memories to be created.
So space is a fundamental element of all episodic memories, but it's not all.
On top of that, we also have the experiences of what actually happens at those
spaces, and hippocampus is critical for that.
I think for that to be understood better, we need to know more about the lateral
cortex, entorhinal cortex input, which is 50% of the input to the hippocampus.
So, but I don't think these are mutually exclusive.
I mean, hippocampus space is a very important element of what is encoded in the hippocampus.
But unlike the entorhinal cortex, the hippocampal place signal or space signal
is part of a representation that is unique for every single environment.
So it's much more linked to individual memories.
But now, I could also radicalize the view and say, yeah, but if I can just hijack
my velocity signal, which in theory I could because it also travels over the thalamus,
then I could basically impose any kind of metric onto the hippocampus.
It doesn't know. It's not a speed.
It doesn't represent space. It represents something else.
But so do you see that grid cells
might have that flexibility to also represent other kinds of metrics?
Or do you really see it anchored to space?
Well, that's hard to say. I mean, it certainly has the potential for doing it.
But as far as we have observed, it's really related to space in the rat at least.
So hippocampus is more different. I would think that the strict relationships,
for example, between the animal's speed and the subtle changes in movement of
grid fields in the entorhinal cortex,
all these relationships are absent in the hippocampus.
It's just that the signal, the influence of speed is much more indirect,
even if it can be detected there. So I think it's secondary.
But then, for instance, one view, not certainly that the view on grid cells
has complexified in terms of
we have moved away from a linear feed-forward interaction to hippocampus.
Some people are suggesting that we can also then use that system for mind travel.
This might explain how we can then use sweeps in hippocampus to sort of look
ahead and do mind travel.
Travel so do you also see
that as a as a possible secondary function
of this system or do you see that this is physiologically not
fully supported no i mean at least in
humans or primates i wouldn't be surprised if if
mind travel is a central function of
of grid cells perhaps even in the
rat because i mean you
can recreate activity based on memories
so you can sort of re reactivate patterns
that happen in real space and to some extent you see that even in in rodents
in sleep that you that at least in hippocampus patterns of activity are replayed
from that happen patterns that happened in awake experience are replayed in sleep.
And probably, because this is expressed across many brain years,
it's very likely to happen in the grid cell system too.
So who knows? I mean, you can probably at least conceivably replay trajectories
also in the entorhinal cortex.
And if you can do that in sleep, then, I mean, that would probably also apply
in a certain drowsy awake state. you can probably also do the same I would think.
Right. So now um.
You're sort of halfway your career. You have plenty of years in front of you to change the universe.
But a lot of experience in studying the brain.
With, of course, magnificent success. We also have to mention the Nobel Prize
you shared with your wife and with John O'Keefe last year.
So in that sense, you are in a unique position to also view the process of doing our science.
The process of understanding the way in which we can make progress in coupling
mind and brain in this case, bringing psyche and the flesh together.
So in that sense, if we would follow your example, what is Edwards' law that
we should follow in the study of mind and brain?
No, that I don't know. I think one law is at least just to be brave and try to,
if I would give any advice to young students, is that take one step longer than you usually think.
So try to do what you think is impossible.
And that's what we have tried through the years. Yes, we have used the opportunities
and done experiments that perhaps we wouldn't have done if we had limited funding or so.
And that has paid off. It's high risk, high gain, it's often called.
So, Edward Sloat, be brave. Yes.
So, then, look, Tony has plenty of money, so he likes to buy me tickets to fly
all over the place. So five years from now, we're going to visit you in Trondheim
because we're going to confront you with the outcome of a prediction you're going to make now.
So what prediction do you want to make today that you will show to us you have
validated five years from now, which will be a major next insight in how the brain operates? Yes.
Well, I mean, in how the brain operates, I think I want to keep it to the circuits
that I've been usually studying.
But I think one of the questions that we really work on is, I think if we understand
how the grid cell pattern is generated, that will tell us a lot,
not only about grid cells, but actually how patterns are formed in general in the brain.
So one of the predictions we're trying to test is whether the grid cell network
actually has the connectivity that is required for such patterns to occur.
For example, if cells with similar properties are linked and if cells with,
also if they are linked in the way that you would predict if this network is
going to use inputs from the environment to update the internal map.
So that's not a very specific.
No, no. Well, I mean, the specific prediction is that cells that fire at similar
locations are preferentially connected. So that's one single.
If you want to be specific, then you get much more details.
But it's easy to test. All right, Edwin Mosers, thank you very much for this conversation.
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