This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschure and Tony Prescott.
This is Paul Verschure with the Convergent Science Network. network.
And I'm speaking here with Cyril Pennarts. And Cyril is a neurophysiologist
who has been looking at the red brain for quite a while,
and particularly looking at how different areas in the red brain operate and
interact in the context of different tasks.
So now, in your talk, you started with the notion of cognitive architecture.
So why do you think that's relevant?
I brought it into the talk because it's a theme you see recurring in almost every session.
And it strikes me because both in robotics you see the modular systems,
but also in neuroscience.
And in both fields, these questions of communication and coordination pop up.
But it seemed to look like you were emphasizing this notion,
well, we have different modules operating in this brain, and we have to think
about how these modules communicate. Is that really how you think about it?
Well, what I try to emphasize is that, yeah, there's evidence for different modules.
Anatomically, in the brain, physiologically, lesions will have selective effects on areas.
So there are functional specialization. You could say that.
Yeah, the issue of how you get a flexible communication is not really answered.
And it seemed a relevant theme for robots as well or other cognitive architectures.
But that means in your research, also the kind of work you were describing,
and we'll discuss in a bit more detail,
this notion of, let's say, well-defined modules with well-defined communication
channels between them is really like a sort of a guideline in how you investigate
these different areas you study.
Yeah, yeah. Of course, this happens a lot by human EEG research, fMRI.
But what we try to add is basically the neural coding also by way of recording spike trains. Right.
And the combination of having more mesoscopic measures of EEG plus the detail spike trains,
makes it more unique or more informative because now you see for instance all
that whereas there is specialization in the hippocampus in Central Australia
there are also commonalities but they are only revealed if you have the spike
trains and can look at this remapping phenomena by.
Reward predictive use for instance okay well but you sort of summarized It's
not everything you're going to say in the future very rapidly.
But the point is that you're saying, look, it might be nice to think about modules
from a more macroscopic perspective like using EEG.
But if we don't have the detailed information at a physiological or anatomical
level, we actually don't really know what we're talking about.
Well, you can know what you talk about if you realize the limitations of the approach.
Right. Okay. But yeah, of course, with EEG, you have source localization problems.
FMRI is better for spatial source, but not for time. So it's more volume averaged slow signal.
So yeah, I think it's really important to have the spike signals in millisecond
resolution to look at the closer synchrony.
Right, exactly. But then, so your emphasis you place in your research and also
in your presentation today,
focus very much on areas like the rental striatum and the hippocampus. And you were...
Defining or showing how these areas actually seem to follow a very specific
kind of zoning of their organization. So can you say something about that?
Yeah, what I showed was the zonation of the striatum in relation to the frontal cortex.
Second part of the talk was more about sensory neocortex and the caudal parts.
Yeah, the zonation I think is interesting because it shows how continuous the
innervation pattern is of the striatum.
So let's say you're lateral in the frontal cortex and you find a specific dedicated
area of reception in the dorsolateral striatum for that,
sensory motor, detailed associations and probably habits.
And then as you follow the band
more immediately, then you find areas protecting more and more ventral.
So it's all very nicely topographic,
but also this topography exists between the hippocampus and the striatum,
ventral hippocampus, more in the ventral, the most ventral part of the ventral striatum, and so on.
But it's also in agreement with the suggestion made that there's not just a
strict segregation within the stratum of, let's say, an actor and a critic.
Fentral stratum, according to the older scheme, as Andy Bartow proposed,
it would need a critic. Doing what?
Basically, forming reward predictions based on the error feedback received from the dopamine cells.
And then somehow the report prediction signals would also be transmitted to
the dorsal striatum acting as an actor.
While there are indirect pathways to do that, we thought there's actually more
homogeneity across the striatum in how the system works.
Yeah, but does that mean – so we look at it, so cortex provides massive input
to the striatum, loops through this whole structure of the basal ganglia,
and again, via the thalamus goes back into the cortex of this massive loop, right?
Then we would have similar kinds of loops running towards the hippocampus.
And now we see that there are zones. That means there is some sort of topography.
The specific areas are very specifically targeting certain regions in the stratum.
And then the loops are basically going to follow the zoning scheme.
So this is now an anatomical construct. Does it have any functional consequences?
Yeah, because…,
What people have done is make lesions at different sites in the stratum and
test rats on different learning tasks.
And then there's evidence to suggest that indeed you have most sensory motor
coding of, let's say, very detailed motions like arm movements in the dorsolateral stratum.
Whereas this area when lesion is also impaired in stereotyped arm movements or habits. it.
But if you go more ventral, you find specific impairments of the association
between action and outcome.
So it's more like making the head movement for a reward. That kind of association is not properly made.
And eventually you have more influences of specific cues.
Lights, coffee cups, etc. And then also space.
Spatial becomes important. So it seems to be a whole stream of information.
The difference in its content, but the computational principles, we think, are the same.
Right. For instance, if you lesioned a ventral striatum, you would lesion the critic.
But in rats or other animals, the dorsal striatum can still learn despite the
absence of a ventral striatum. Right. Okay.
But then before we get to the actor-critic criticism, so what you're saying
is, look, Look, we have these loops, these cerebral basal ganglia loops.
They have specific qualities.
It can be action, it can be cue, so sensation.
It can be value or internal state, motivational state or space.
How many of these qualities do we have, do you think?
You mean qualities as dimensions? Well, yeah, or different modalities if you want.
Yeah. Like we have cue, action, motivation, space.
What's missing from this list? What's missing?
Um, I don't think we, we miss very much.
Um, sometimes you could use time as a, as a cue, uh, sort of internal time when to expect a reward.
Uh, but usually time is accompanied by distinct signals, uh, going along with it.
But we also, uh, know now that, yeah, the hippocampus when time is relevant
for a task can encode, uh, moments of time or little episode moments.
So, that could also be a mechanism to transmit timing information.
Okay. So, if anything is missing, it would be time.
And then it's not obvious that time would be looping through these structures in the same way.
Right. Not as it is in the hippocampus, yeah.
The striatum or basal ganglia are probably important for the perception of time
or the estimation of time because drugs that typically work on stratum like
amphetamine also change the perception of time, cannabinoids, et cetera.
And that might have more to do with these ramping responses that you see in the firing rate.
So when the animal's in a situation where he expects some cue to appear or a
reward to come, you will already see cells increasing the firing rate slowly
until it finally happens.
And you believe that's an intrinsic property of these cells or of this structure?
Of the loop, yes. Yeah, but this goes with the cortex.
But where's the clock?
Well, it's possible that there are pacemaker cells somewhere, but it's not so likely.
The dopamine cells have some pacemaker properties, but then the firing of those
cells is also variable and they burst and reset. set.
So it's more likely to be an emergent property of the whole circuit, I think.
So it's not a cerebellum or so? Um...
Surveillance is supposedly important for timing, but also in the very fine time domain.
Stratum might be more for longer stretches of time, like seconds.
That's the beauty of this scheme that you didn't consider.
We have to find out why. But in some sense, the stratum gives you a beautiful event-based system,
with these different modalities or qualities that it processes while it can
then, by using the cerebellum, which is still about 60% of your brain,
get high-resolution time signals.
Because the cerebellum's timing precision stops at about one second. Right.
So you might have a dual loop, right? That you have sort of an event stream
across basal ganglia, hippocampus, cortex, and then a real-time high-precision
stream, an interval stream over cerebellum.
Exactly, yeah. Would you buy that as an explanation of time?
As a mechanism for timing, yes. Yeah, timing of movement.
No, but also timing of these ramping responses, right? For expected rewards, for instance.
Yeah, yeah. I don't know how that would work in the cerebellum,
but if you, let's say, you train animals or humans on a sequence of movements like finger tapping,
but now you change one key of the instrument and you make it hard to press,
then the cerebellar cells would notice that and you have to adjust the strength
of your finger tap on that. Right.
And similarly with timing issues.
So it sounds quite likely that Sir Benhamin there is for the fine timing.
But this sort of little discourse or detour, if you want,
was interesting to see whether notion of cue, action, motivation and space would
be the four main domains or whatever, we missed something fundamental.
And now at least we invented a story, the two of us, that would in some sense
suggest that we could leave time out.
You don't need to include time in those qualities. Yeah, at least not by a separate structure.
Exactly. But I do wonder how the cerebellar timing is coupled to the corticostratal timing.
Somewhere the thalamus is in between.
Sure, there are dense projections between these structures, right? Yeah, yeah.
Okay, this is outside a little bit of what you were presenting today.
I don't think you ever measured from the cerebellum, did you?
No, no, that's right. So it's a very global idea.
It's never too late to start, you see. But then, okay, so now we have to consider the zones.
That's good. We have the four qualities.
And now the key point you made on the basis of that is, look,
these loops with these varying qualities also carry, if you want,
motivation in one of these loops.
So why separate action from motivation or why separate value now from action
as you might do in an actor-critic system, right?
It's more a distributed solution where if you want value and action are both
processed on equal terms in some sense. Is that how you think about it?
Yeah, so, yeah, you always see, no matter what task the rat does or some other
animal, you always see this desolation of sequence.
That's also in the orbital frontal and medial prefrontal.
So every little element of task is basically coded.
But then what to do with that? Well, we regard that sequence as a scaffold to
which you can associate things like reward value.
Which would mean that, in a sense, at the straighter level, you attach a weight
to a particular action as being important because it's reward predictive or
predictive of an hour outcome.
The only exception could be that habits are not strictly dependent on reward.
You can delete reward and your habits will still keep going.
But in a sense, if you take the notion of outcome to be more abstract and not per se reward related,
you could also say, well, an action itself, the completion of an appropriate
action where you touch an object can also be regarded as an outcome.
Even for a learning infant, being able to reach some object could be regarded as an accomplishment.
But the one thing I don't understand fully is that you're saying,
well, you could attach a reward to single events. Like you have this tessellation of the response.
Like you're engaged in a task. Here I am. I'm the rat. I'm pushing all these levers and whatever.
Now, in my brain, this sort of corticostradial system and my hippocampus,
I'm now decomposing this task and all its small elements.
Relevant, irrelevant. relevant doesn't matter and now you're saying i'm now
tagging if you want some of these elements with um with value with with reward
predictions is this really what you have in mind,
Yeah, yeah, basically, yes.
Where, of course, yeah, if we train animals, the animals are trained in such
a way that they will only do or perform these tasks if finally some reward is coming.
Sure. So in a sense, we don't test for real spontaneous behavior or behavior
where there's no reward at all.
In the end, somewhere there will be a reward. so in that sense the sequence
that you see might always have to do with the final outcome.
Right. Now tell me where's the site where this n-gram so this memory trace of,
action reward is established and maintained?
We don't know but probably the,
corticosteroidal synapse itself could be a good site for that at least when
it comes to associating, for instance, place to reward or cue to reward or actions to reward.
Because then we have only to suppose that the hippocampal cells or the subicular
cells, C1, subiculum, project to the striatum. They do.
We know that there is plasticity in that connection.
The fibers are glutamatergic.
So in a fairly straightforward, Hebbian way, given the effect of reward feedback,
there can be a plasticity going on.
And this lines up with the behavioral evidence based on lesions and disconnection.
So if you lesion the striatum in that loop where you have the place representation
in the striatum or place processing….
Do you see this system compromised? Or you see this behavior compromised?
Right, yeah. So if you lesion, in this case, the part of the ventral stratum,
which receives most of the hippocampal input, then you lose your place reward
association, or at least it's not behaviorally expressed.
And if you lesion the hippocampus on one side and this ventral stratum part
on the other side, it's also lost.
Lost um this could mean that uh place reward association is association is still
else stored somewhere elsewhere but at least the behavioral expression is lost so there's no uh,
translation into invigorated actions um but for me it's more natural to think
that reward prediction is motivation it's right as soon as you have a reward
prediction that's enough to drive behavior.
Yeah, because the point of what we're really talking about is that the animal
is learning about places, right? It learns about locations in space.
And it's also associating, let's say, these reward predictions to locations
in space or to some cues in the environment. But it can be either of the two.
Yeah, or both. Yeah. But does it also mean that if you interfere with plasticity
specifically in this place system of the ventral striatum, that you then also
don't see any acquired place preference?
Yeah, you can interfere with NMDA blockers, dopamine antagonists also,
and you will, it's not been shown that place preference per se is impaired, but other,
learning processes are impaired, like the Pavlovian auto-shaping,
where you associate cues with reward.
Okay. But I would not consider the ventral stratum as a place system because
the neural firing patterns are not that spatially specific.
They're more specific for what you do at a certain place. So we're here.
If you want to get coffee, you know you have to get out of the room.
So when you get up, there will be striatal cells firing, but that's relating to your reaction.
If you would get the coffee by going in the other direction,
they would also fire, or it would be other cells firing. Yes.
So these were these experiments you have been doing in this Y maze,
right, where the animal could find reward at different sites.
Reward locations were indicated by a cue light, and then the animal should just
wait for this cue light to appear, and then it could go out and find a reward or not.
And in those experiments, you were particularly looking at the information exchange,
if you want, between the hippocampus and the ventral striatum with the idea,
okay, hippocampus we know is dedicated,
among other things, to learning about location, space.
We will have these place fields with a very specific firing responses in certain positions in space.
And the question is, is this information directly entering into this ventral
striatal system or not? Was that really what you looked at?
What I presented was not directly about the communication in terms of whether
that's involving oscillations or so.
Well, but look at the level of correlation between these structures.
Yeah, yeah, yeah, certainly.
Yeah. Well, to buttress it, sorry, you would have to present,
let's say, cross correlations.
Previously, we looked at these replay sequences during sleep where you do see
actually hippocampal play cells firing first. and then the reward action cells in the striatum.
So, yeah, you have to do these tighter correlations in the spike balance.
No, but what I'm after is that I thought that what you were after was to say,
okay, the ventral striatum has a response that's maybe action-dominated,
but it has a spatial component, right?
It's not devoid of space. and whether this space, the specificity for place
in the ventral striatum was in some way dependent on the hippocampus or not.
Yeah, but that's really a minority of cells that have this spatial specificity. Okay.
It could arise on the one hand by very sparse firing.
So it's kind of an undersampling problem where some of the spikes happen to
end up in more in one of the chambers than the other. um,
Yeah, the other possibility is really that you have some kind of heritage of the place cells.
But at least in a setting where you eliminate the local cues and you make the
behavior dependent on path integration.
We think it's more that the ventral shade of cells take the hippocampal input
and translate it into a current reward prediction and base directions on it.
If that action means initiate an approach to a goal site, then that will generalize
across all the chambers in which to make that action.
So it's not really spatially specific.
Okay. So it's really more policy dependent or action specific.
It says, look, when I see this light to the right, I turn left.
That's a great thing to do and I will do it wherever I am.
Right, exactly. Yeah. Okay. Yeah. But now the other thing that you observed,
which I found very curious, is that there was a modulation of the size of this
place field in the hippocampus by reward itself.
Yeah, so what we saw is that these nine reward sites tend to have occupancy of micro place fields,
whereas you don't see these micro place fields in the bigger non-rewarded compartments
of the maze. How do you explain that?
Uh, well, on the one hand, they're extremely relevant sites.
So it's really important for the rat to know where to stick your nose in precisely.
So a finer spatial scaling could be very useful. Uh, and it's also a couple
of more specific small scale behaviors.
Uh, for instance, uh.
Licking while you approach the site, the rat would have to stop.
That's already a deceleration motion in a small stretch of space.
Then the licking behavior, then waiting for a certain while before the reward
comes, and then licking.
So it's actually a lot happening, which all probably has to be coded somewhere.
So you're saying, if we would replot place field size versus something like
behavioral complexity, it should give us a fairly uniform form curve. Yeah, right. Okay.
Complexity on the x-axis and
the inverse of size on the y-axis give you a straight line. Okay. Yeah.
Is that a known feature of these hippocampal place cells? No, not really. No, no.
So, yeah, there have been studies that showed a greater density of place fields
around important sites, like the hidden platform in the Morris Water Maze,
attract more or less more place fields.
But it could be that this involves the same phenomenon.
So if you have a broad coverage of the space by big place fields and coverage
by small place fields, if the sites are really relevant, then you end up with
a higher density of place fields. Right.
But now the other thing that was interesting is that there also seems to be
a correlation between the peak firing rate in the place field and the size of the place field.
Right. So apparently the larger size of place fields also gave you higher peak
frequency than the small size place fields.
I mean, are these kinds of correlations intuitive to you?
Or does this make sense? We haven't systematically looked at it.
It could be the case. We'd have to go through the entire set of cells to see if there's a relation.
It does make a little bit of sense in the sense that the way you define a place
field or when you plot a bright yellow spot is somewhat correlated to the absolute firing rate.
So if a cell sort of has a dynamic range from zero to 20 hertz,
and the zero is kept for most part of the maze except in one chamber,
then it's more likely that the cell passes the threshold for the place field
more easily, so to say, utilizing the entire dynamic range. Right.
So now the other thing that you mentioned is that you have this notion that
there's something like a state transition occurring in these neurons,
both in hippocampus and ventral striatum.
So what does it really mean?
I think the interesting aspect about it is that it's a global population measure.
So there's no single cell that easily dominates such state transitions.
And yet they are coherent because you see them happening in a lot of cells.
At least there are marked firing rate changes at the point of state transition or close to it.
So it seems to be a population phenomenon that both works in one structure and the other.
And then the most interesting is that those are correlated also.
Yeah, but I don't really understand the phenomenon. I mean, so here you have
your recording of large numbers of cells.
And how big is your pool of neurons you're doing this analysis on?
This Y-Mains data set is about 600 neurons. Okay, so that's plenty of neurons, right?
And now you are sorting these neurons on a specific metric, right?
And that then gives you this notion of phase transition. but what's this metric
on which you sort your neurons?
Yeah, so basically if you would have 10 cells, you make a 10-dimensional space
and then you plot your spatial bin firing rates into that space for all the 10 neurons.
You cluster it basically based on K-means. It can be likened to maximizing the
Euclidean distance between the clusters.
Okay. So you seek for the best partitioning
plane lane okay um and yeah
so that's what you do but um remarkably it's
not some kind of arbitrary cut because the cells are visibly sensitive
to it not all of them but uh i would say a majority of the cells that do react
in advance of the state switch or have a reaction afterwards um but you know
i We would have predicted here that an event like the Q flipping on, well,
might have some effect on the hippocampus, but would be more strongly felt at
the ventral straddle level because our psychological colleagues would tell us,
well, it's the amygdala that does the transmission of this motivational Q.
So it's a bit of a surprise to see that the impact on the hippocampus is that
big, whereas it does not disrupt the finer spatial code.
Because it's expressed as a remapping effect. So, but it also means in your
physiology of this, in the population recording,
you would also see an instantaneous transient across all the cells you're measuring
from in some sense, or a big subset of them.
Yeah, but if you just look at the plane recordings as they're going on and the
rat is doing its task, it might not be that obvious, because it's very hard
to listen to all these tens of neurons at the same time. Right, exactly.
So that's why it's handy to have this algorithm to do it for you. Mm-hmm. Um.
And yeah, I should also emphasize that it's not only the cue lights that do it.
So they are sort of a strong trigger for a state switch.
But you also see significant enhancements of the switches when the rat enters a chamber.
So when he sort of knows I'm going now into a direction where I'm close to the reward. Right.
But now, are these neurons really doing something qualitatively different before
and after this transition, this phase transition?
They change their average frequency or they shut off completely or they turn on?
Yeah, sometimes they switch up completely. So there are some cells that in one
state have a place field and in the other state not.
But usually it's a bit more subtle. So you would, for instance,
have a gain modulation of a factor 2 or so or 3, and sometimes a shift in the place field.
And also at the ventral straddle level.
So the upshot of this is that we shouldn't
Only look at single-cell detailed coding, but also have the global population
picture, which says, okay, there's another type of coding change going on. Right, exactly.
But then, what's your functional interpretation of this?
The functional interpretation would be to say, well, there's,
again, this scaffold, fault you might say of basal coding in this hippocampus
in the spatial task that is a spatial layout,
but then again you can attach or associate events on top of that which do things to your basal code,
same thing for a commons yeah but if you think about it it makes sense I think because,
for an episodic memory it's very useful to have a sort of basal spatio-temporal
framework onto which you can tag important events that need to be remembered.
So in other words, it's kind of handy to have a scaffold to build your memory on.
In the artificial way that Romans had at memory art, they imagined themselves
walking into a house and storing things in caches in the wall or behind doors as a way to remember.
And maybe that could be a metaphor for how this scaffolding mechanism works.
Okay, and then you see this, what you call a phase transition,
as a signature of this kind of, let's say, attaching specific cues into that scaffold. Right.
Right, yeah. Or is the scaffold itself? The scaffold in the hippocampus here
would be the basal spatial coding, the rate maps and place fields of all the cells.
Yeah, in this case, you do have a very important event because it totally drives
the animal's behavior and becomes not known as an association to one little place field,
but rather while this cue appears in the whole space,
the animal can clearly see it.
So it becomes more of an overriding event that impinges on the global coding.
And that would be modulated by something like a reward prediction.
Because the cue light comes on. Yeah, yeah, yeah. We don't know how that would happen.
It could be driven by the visual system, which initially takes in the visual information.
But at some higher level of visual processing says, this is a reward predicting cue. Right.
It could also happen at the prefrontal level or at many places.
So now you observe this, what you call phase transition. I'm not sure phase
transition is a good label really, but okay, let's keep it for now.
You see it both in ventral striatum and hippocampus, two areas that are densely coupled.
And you also looked at the cross-correlation of these events.
So what did that tell you?
The interesting thing there to see
is that the state transitions although
they are computed only locally for
each structure are still correlated with each other and
it could be at least partly externally driven because the queue event occurs
at one moment and could trigger state transitions in both structures but apart
from the queue events there are also other are lots of more spontaneous transitions,
which do appear still to be highly correlated.
So, it doesn't mean per se that the hippocampus switches first and then predicts
its altered spike patterns due to the accumbens.
It could also be indicating that these state transitions are a more global phenomenon
and also involving other cortical areas like the prefrontal cortex.
But how about, so in terms of interpretations that I say, well,
look, You know, the animal sits here in this Y maze. There's nothing better to do.
A cue comes on and I just have a nonspecific attentional effect orienting response.
Will something change in the world?
So it's not specific in any way to the task, to reward prediction,
just something changed in the world.
I have a nonspecific attentional effect and that's this highly synchronized
phase transition that you observe.
Uh yeah um let's
see well we um we don't
have some other uh secondary kind of
cue that would signal um address still has to do something else um and we don't
have a way to probe whether this is selective attention so yeah it's possible
but yeah if i would talk here about a motivational cue that subsumes attention.
It's sort of a change in the animal's state, motivational attention.
One thing, so you would predict if you would switch a cue light that has never,
ever been coupled to reward, so it's a neutral cue light, you should not see this phase transition.
It would be very hard to have it totally neutral because a novel stimulus is
also interesting or either scary or interesting to explore. It's not equally,
it should not be equally leading to reward predictions.
Yeah, yeah. So that means especially the phase transitions in the ventral striatum
should then be sort of not there because there's no sense of reward.
Yeah, this would be an interesting experiment. You could say,
well, I'm taking a second cue maybe of a different light or a sound cue that is loud enough.
And at one point it's null, but then you keep on repeating it with the same
loudness and the animal learns to ignore it because it's irrelevant.
And then see what happens.
That will be the control test. Yeah, but I would predict that if there's this
learned irrelevance about it, that the state transitions become weaker or less
frequent. Right, yeah, sure.
Okay, so now we have this idea of the transition.
But the other thing that made me worry about an alternative interpretation is
that the latency that you saw between the straight transitions in hippocampus
and ventral stratum appeared very short.
They seemed really practically synchronous in this change of their overall dynamics.
And then I could argue, well, look, that's the perfect signature of a nonspecific
attentional global signal that sort of engages all these systems in parallel
with zero latency difference between them.
So have you worried about that?
Yeah, we did try to define our bins on an even finer scale.
But we also found that this estimation of local firing rates works best in,
let's say, a resolution of 100 milliseconds.
If you go below it, it becomes a little bit messier and more noisy.
So we can actually only say that these joint state transitions occur with a
resolution of around 100 milliseconds or a bit less.
And that's not enough to say whether the hippocampus would really switch first and then the accumbens.
But actually, you could check this in the data you have, right?
Yeah, we could do more detailed analysis, for instance.
Using more single spikes or... That's right. ...counting the number of spikes
per theta cycle or so, as the rat moves along.
Yeah, because, I mean, the expected latency, if the loop, as you described it,
is C1 subiculum in hippocampus, to your ventral striatum, right?
And then you have a latency, a transduction latency in that projection of about 25 milliseconds.
So that means you would expect a very specific patterning of this phase transition
if this is the projection that's actually engaged. Right.
Of course, at the level of the stratum, you will have easily converging activity
from the amygdala, prefrontal cortex, and thalamus.
So in a way, if there is stratal firing, the correlation to hippocampal activity might be partial.
But you can do this, basically.
But in some simple-minded view, you could say, well, if the source is in hippocampus,
then this is the latency you should see.
Yeah. Yeah. We could certainly try to resolve that at, let's say,
the time scale of, well, the theta cycle is actually around 100 milliseconds.
It could be maybe happening also in the gamma cycles somewhere.
Sure. 20 milliseconds or so. Maybe that works. But would such a post hoc control
now still be worth your while or you think this is really doesn't matter anymore this
story is done now you move on um well
the analysis as we did it now especially jayden jackson was already quite extensive
but more trying to tease out whether um these tech transitions really correlate
to a motivational change or motivational attentional change of the the animal.
So the additional analysis he did were more directed at finding out whether
for instance the chamber entry makes a difference in the state switches.
It's also interesting to see if the animal, I didn't show the data,
but if the animal approaches the reward site,
Then actually the state transitions, the rate of switching decreases in the accumbens.
That might be because the network is converging to a stable state of,
let's say, solid reward prediction.
You're there, you made it, and now you can stop. Right, exactly. Yeah.
Whereas in similar non-rewarded behaviors, where there's an inter-trial interval,
no queue, the L1 makes the same approach, you see a higher switch rate.
As if it also reflects uncertainty or, let's say, ambiguity in the system.
It keeps on flipping back and forth. Right, okay.
So the next part, so now we have a bit of an idea how this mental stratum hippocampal
system might be combining, let's say,
value reward information with information about space and Q, okay?
And in some sense, indeed, what you did in those experiments and look at in
too much detail was really how could these components of the nervous system,
these modules of the nervous system, really exchange information.
So that was sort of the next part of your presentation where you actually emphasized
very much this notion of neural oscillations and spike coherence.
So remember, it's a dynamics-oriented perspective on communication.
Yeah. So what are the main considerations to sort of actually look in that direction
at this sort of intermodule communication and not just at, let's say, rate coding?
Okay, yeah. When we purely compare rate codes of one area to the next.
It's a little bit hard to say that there's actually an influence directly in
the way of a phase relationship or a cross-correlation.
It could be done at spike level, but usually within an area,
let's say a pyramidal cell and an end-neuron can have a very tight cross-correlation.
But between areas, it becomes easily sloppy or not so well-defined, the delays and so on.
So I do think that the oscillations are an interesting way of looking at the
communication mechanisms.
Although, of course, yeah, like I illustrated for gamma, it's certainly not
guaranteed that oscillations per se are important.
The gammas precisely show that probably they have a local function in the network
and are not for this long-range hippocampal to sensory vortex communication. Right.
But you emphasize also this relationship between local field potential and EPSPs
or possible impact on plasticity through spike time dependent learning.
So what other attractive features
do you see in this sort of this synchronization view on communication?
Well, one advantage of synchronization is that if you have strong synchronization,
a network that does that is in a better position to affect a target area or
a common cell, for instance, where the cells converge upon.
If the synchronization at least happens in the gamma range, you're talking about
spike timing differences of in the order of 10 milliseconds or so,
because otherwise you're covering the whole gamma cycle.
And then you get into a range of one spike eliciting an EPSP with at least with
the tail should overlap with the next EPSP 10 milliseconds later.
So that's an interesting range for EPSP starting to summate and generating spikes. spike.
So at the same time, if one cell generates an EPSP and the next one an IPSP,
you only get very short lasting excitations.
And then, yeah, this time scale of gammas correlates quite well with the time
range where you would see spike timing depend plasticity.
There's some nice work by Laurent where he indeed shows that spike timing regulated
in the gamma range more or less does indeed alter synaptic responses.
Right. It is a realistic scenario. But then, so here we have,
let's say, a communication channel between two areas in the brain.
It's organized along some temporal dynamics, some temporal code.
What kind of code do you really have in mind? I mean, how complex would this code be?
Is it really just like I have like a carrier wave and that, let's say,
enslaves all my target neurons to oscillate in a certain frequency and then
I can sort of more efficiently inject EPSPs or create EPSPs there.
How complex is this temporal code then in your mind?
Yeah, well, in my mind, there's also the debate of what this could do.
On the one hand, gamma oscillation or other oscillation could be useful to bring
the notion of iterations into the system.
So we say we make a processing step, all the local neurons interact with each
other and recompute their firing rate at the end of the cycle.
Then there's a stop, maybe also to allow the system to communicate with other
areas and get feedback, which allows the next iteration to happen.
So this could be a functional notion of, why there is also an inhibition between...
Another thing is to partially the information to make ordered sequences.
You want some discretization in the system of place all ordering or sensory
information ordering, not sort of happening
in a jambalaya with every cell overlapping with every other cell.
So ordering and sequencing could be a real function.
Okay. And then what I alluded to was also the notion of phase coding.
So that there is, besides global rates, additional information in when the spikes are fired.
Of course, with the prime example of theta phase precession in the hippocampus,
where you can really decode quite accurately the position of the animal from phasing of the spikes.
But theoretically, I also couple that to employing different modes of coding, actually,
because whereas you might need your rate code for feature coding representing,
I have a cell here, it's a simple cell for orientation and right now that orientation
is very appropriate to code so we drive with the firing rate.
The other thing could be to shift that firing actually and then you create an
additional phase code where that simple cell relates to other cells so it causally
influences the phasing of other cells and then predict backwards.
Okay, but actually, so these are the possible scenarios, right?
But you actually went in there and you measured from quite a number of areas. Okay.
So what was the real setup you built up there? Which areas did you measure from to test these ideas?
And what was the task, the animal had to perform?
Yeah, so the task was to train rats on this discrimination task with the visual
stimuli being a discriminandum,
or at least the positioning of CS plus versus CS minus stimulus would be the
thing to be discriminated by the rats, determining their left or right choices.
With the addition of tactile cues, also tickling the whisker or barrel cortex,
because these are sandpaper cues where the rat would pass by and gain information
about future amounts of record.
So that was the idea. And by recording from both the visual cortex and barrel
cortex, we can get an idea of how they interact.
So, for instance, does the appearance of the visual stimulus affect also barrel
activity, whisking activity?
That would also be in line with a prediction from a hypothesis on multimodal
integration, which I proposed a couple of years ago, and which also relates
to consciousness or how modalities are actually coded. it.
But then the additional areas are the perirhinal and hippocampus to look at this,
let's say, potentially forward propagation of sensory information into the MTL
hippocampus memory system with the additional hypothesis that at some point
when the hippocampus start replaying the sequence might come back out and reach
back to the neocortex again in reverse order.
Okay. But how does this relate to consciousness? Oh, this last part does not.
Oh, no. Okay. Well, indirectly, perhaps, because if we would accept that,
let's say, notions of recognition are also part of your conscious experience,
then things like periorhinal feedback to the neocortex could be very relevant. Mm-hmm.
Okay. But I would also maintain that if you lose the hippocampus,
you're still conscious.
Right. Exactly right. Yeah. So now, the point is that now we have,
you looked at four areas, right?
We have the smetocentric cortex, CA1 in the hippocampus, you have primary visual
cortex, you have perirhinal cortex, okay?
And they're also cleanly organized in an anterior-posterior axis, right?
So I guess you also did it on purpose so you can actually measure from them in a reliable way.
So now then you developed a new measure that helps you to sort of look at these
phase relationships between these different areas which you called,
WPLI the weighted phase locking index which looked very interesting and then what did you find?
Okay, well, yeah, so I confined the story today to gamma rhythms,
which are these high-frequency, roughly 40 to 80 hertz oscillations.
The first point of contention is to what extent the somatosensory cortex generates the gammas.
The visual cortex is less contentious.
We find that there are clear gammas. They're enhanced during active behavior.
This might correspond to active or passive whisking.
And in addition, the gammas are quite local. So at least the coherence of different
field potentials is high within the local area of the somatosensory cortex,
but not, let's say, somatosensory to visual coherence is almost nonexistent, very low.
And the same for perirhinal somatosensory to hippocampal.
Whereas if gamma would be really a central mechanism for communication between
all brain areas sort of in a very global brain-wide fashion,
this would not be expected.
Were you surprised by that outcome? Did that surprise you?
Not really, no. No. Not really because, yeah, on the one hand,
there have been previous findings is an inter-areal gamma coherence in the visual system.
But yeah, not all of those studies corrected for potential volume conduction problems.
A lot of studies did not have the spikes in there to show that the gamma is
really local, local cells are entrained to it.
And so there are all kinds of ways to buy out of this idea of global gamma synchronization.
Our findings do not contradict sort of short-range.
Right, exactly. I think this is the key thing that you observed, right?
That in all these areas you measured from, you found strong local coherence in gamma.
So that means the neurons you're measuring from are all happily firing together
in a gamma range. Right. At some phase relationship to each other. Yeah.
But you do not find a similar coherence between these areas, okay? Right, yeah.
There's a positive control. The areas do have gamma, but not with each other.
Yeah, exactly. So this raises a number of interesting issues.
So how do you then look upon this? Let's first look at the areas individually.
So if you compare, let's say, V1 with somatosensory or perirhinal or hippocampus, CA1...
Is that dynamics in the gamma range really very different between these areas?
Between the somatosensory and visual cortex, not very much.
No, no. They both have similar gamma range. They show good phase locking.
In the hippocampus and perirhinal, the theta becomes very strong.
In the slipstream of theta, you also see beta, which is roughly double the frequency, so 16, 20 hertz.
So there are clearly different things going on
in hippocampus you see more a chopping of the gamma because of this theta rhythm
so yes you see a few spikes in gamma and then the system shuts down for a little
bit and then it comes back again and that choppiness you would not see in V1 or perirhinal.
Perirhinal also has a quite strong theta rhythm together with the hippocampus,
probably the gammas that are locked to the theta cycle so they are not going on all throughout,
the visual cortex tends to have strong gamma
during the visual stimulation but also during
the movement because actually the scene of the rat is totally or
always changing so that means with the measurements
you did we have these two cortical areas that really show their own kind of
gamma dynamics and then we have some more hippocampal related areas where we're
sort of theta starts to dominate the dynamics much more so we have two kinds
of subsystems yeah so how do you then explain this,
gamma dynamics in cortex so how is this generated yeah,
Right. We know from the visual cortex that stimuli can drive the gamma.
And so we presume, but cannot directly prove in this case, that also whisking
movements or other somatosensory stimuli would drive the gamma.
In addition, the gamma could be enhanced, for instance, by attentional processes
or prefrontal feedback to the area, because that's also been shown in monkey studies.
And yeah the factors
driving the strong gamma coherence locally I don't
think can be precisely disentangled here because during the active movement
phase there's lots of things going on visual input reward expectation but wait
I think you can say something about it now because you you have distinguished
the different neural types involved in this and you could distinguish the
inhibitory interneurons from your excitatory pyramidal cells.
And you also found very specific phase relationships between them.
Oh, yeah, in terms of the local circuits, we can make statements.
Yeah, so there were two interneuron classes.
These are the fast sparkers. That's the way you identify them in extracellular recordings.
The broad sparkers are more like pyramidal cells, maybe some stellate cells.
And there the special finding is that there are two classes of interneurons.
One fires early in the gamma cycle, one late, whereas the pyramidal cells fire in between.
So there's one class of interneurons that fire early and they're firing to the
gamma or the entrainment to the gamma can be easily explained by pre-firing of the pyramidal cells.
So that more points to an interneural network gamma mechanism, the ING mechanism.
Whereas the late cells could more be involved in recurrent inhibition driven by the pyramidal cells.
But would you see these early inhibitory cells, as I say, as a separate network
of more like master controllers of the gamma?
Well, they're certainly the earliest cells to fire in the gamma cycle.
So what we think happens is that there's local excitatory input to these interneurons
that excites them, but not from the same local population of pyramidal cells.
They do inhibit each other, but in the rebound of this inhibition,
they can also become excited.
So there's rebound excitation or once the shunting inhibition is lost.
And some of these interneuron classes have gap junctions. So if one spikes,
it can trigger or stimulate firing in the gap junction coupled cell non-synaptically.
So that could explain why there is a class of early firing cells. Right, exactly.
But so they are exclusively coupled with gap junctions that would allow very
rapid transduction with their fellow inhibitory early cells.
Right, yeah. Okay. Yeah. Yeah, so we don't have an identification of what those
cells are, whether those VIP interneurons or somatostatin or basket or chandelier cells,
but there seem to be various classes that could conform to this ING scheme of
interneuron-driven gamma. Exactly.
So that might mean you have, let's say, a very tightly coupled network of interneurons
that are sort of locally initiating then than a pyramidal interneuron-driven gamma oscillation.
Yeah, yeah, yeah, yeah. It could be that the pyramidal cells start firing because
they come out of that inhibition or because they receive additional excitation
from other areas, but then most likely also excite each other locally.
That would suggest that you also should see a spatial parcellation or fragmentation
of this gamma oscillation in the red cortex. Is that true?
Yeah, what people find generally is more gamma superficially in the superficial layers. Yeah. Right.
Here, I have to say, we did record also deep, and there you also see some gamma.
But these were not laminar probes. So we don't have an exact identification of the depth.
So now we have learned a lot about local gamma, which is really cool.
Okay. Okay. Thank you. But it turns out it has nothing to do with your original
question, because you wanted to know about inter-aridal communication,
which you believe will be in gamma, and it's not.
At least a negative say, well, okay, it's not gamma. It should be something
else. That's my question. So what is it? Yeah. Um...
I think there are two possibilities. We're now looking at data and beta ranges for communication.
Those seem to be working, especially for the hippocampus pyramidal system.
But sometimes during some behavioral phases there, we see beta coherence with the sensory cortices.
So maybe we're looking in too high a frequency range.
It could also be the case that the really long range interesting stuff is in
desynchronized assemblies.
So, yeah, because, you know, conscious processing goes on in a largely desynchronized
EEG state, it's not a given that it should happen in an oscillation mode.
No, this is an interesting consequence, right?
Because maybe by looking for synchronized states, they're maybe not as ordered
as a rate code, but they are fairly ordered. and maybe that's still not the way to think about it.
So if you really have to make a bet, do you think you're going to find any kind
of informational coupling at lower frequency ranges? Do you think that's really plausible?
Yeah, in terms of phasing, that could well be.
In a way, we see that in Theta phase precession.
Despite the low frequency of the Theta rhythm, there is distinct information coding in the phase.
Well, but it modulates it still on top of a gamma code, right?
Otherwise, there's nothing there. Yeah, yeah.
The local gamma codes in visual cortex, for instance, also have some phase coding
in the sense that there is stimulus information in the phase. It has been shown also.
But it might also be that most of the information transmission is effective
in a desynchronized mode. You still have synchronous spiking assemblies, but they're sparse.
They're not having a particular special relationship to the mass synaptic potentials
because there are just too few of them. You wouldn't pick them out.
And yeah, by their regular external projections, they reach their targets.
And that's also still a viable scenario. Right, but a bit of a messy one.
Yeah, and you would like to have, you know, gain control over that.
But in your picture on this, would you still believe that at least these pathways
are highly coordinated?
That means, let's say it runs all over the thalamus, so at least there's only one hub doing this.
Well, on the other hand, what we already talked about, the ventral striatum
or hippocampus, you see they have convergent input from many different areas,
right? So would you still think about some anatomical ordering of this very divergent face code?
Or do you also see that as fairly open, like many different anatomical channels
providing these kinds of codes to all parts of the brain?
Yeah, that's a difficult one. Yeah, so like in the visual system,
you do recognize mappings or sometimes retinotopic, sometimes craniotopic, but lots of mappings.
And I think it's actually a key question how or whether neurons on similar locations
of maps in the same framework communicate better with each other.
So that you would kind of use the spatial location of a feature as also a binding
queue to make it belong to some other queue at the same location.
If we don't have a correspondence between spatial mappings or retinotopic mappings,
it seems an intrinsic problem of how you piece things together in space.
No, but it's interesting, right? Because you're looking for order in some sense.
And you also started with this modular view. Like we have modules.
Modules have their local operations.
They're encapsulated informationally. They have to be, although it's just a module.
And then in a very ordered way, they exchange information with each other.
Like hippocampus might send place information to the ventral striatum,
and the ventral striatum does something about the reward prediction.
And then, I mean, you follow a very logical process there. And then you said
like, okay, let's see how then these structures really exchange this information.
And then actually you don't find anything.
So is this whole modular scheme you originally proposed maybe already looking
in the wrong direction? Is the brain really that cleanly modular as you would
like to have it being an experimentalist who actually has to know where to go, right?
Yeah. Well, the position I tried to defend was that it's modular and also not modular.
So there are also modulations like this state switching on top of everything
that indicate that there might be some more global modulation going on.
Where that comes from, we don't know yet. It could be thalamus or cortical or
maybe prefrontal driven.
But yeah, there are certain events in the system that can happen and switch
multiple structures at the same time.
What would such an event be?
Well, in the case of the hippocampus and ventral striatum, where the cue,
the light that switches on,
would signal to the rat like okay you can go now there's a reward to be obtained,
and you could see the way that,
Large parts of the brain and the body have to do with this reward acquisition.
So you better get going and change your system.
It could be that you do need a system that recognizes the relevance of the cue.
This could be a mycolyte prefrontal, maybe earlier on, perirhinal maybe.
And for instance, the prefrontal is in a pretty good position to modulate these systems.
Projected directly into striatum, but also indirectly through the parahippocampus
and perirhinal into the hippocampus.
So at least you have a pretty solid top-down mechanism there to do it,
but speculation weather. Right.
But still you're stable to say, well, it's modular and not modular.
You understand it sounds somewhat paradoxical.
Yeah, of course. Yeah, yeah, yeah. On the one hand, you want to reconcile the evidence for...
Local specialization because there's lesion evidence there's neural
coding evidence on the other hand you say
well hey but yeah there are also these overriding events that have common effects
in both places right so then um so surely you have been marching through this
road of brain for quite a while now and gained an incredible amount of knowledge
about that system at the system level.
So then what should be a surrealist law in our study of the brain?
That's a good one.
A general law? Yeah.
I think that anything meaningful that happens in the brain is a network phenomenon. That would be my law.
Okay, cool. That, you know, single cells that fire don't mean anything.
They don't signify.
You're not conscious of it. It's just, you know. Noise.
At least we, yeah, we have to look at it from at a higher level, I think. Very good.
And then, so five years from now, I'm going to come up to Amsterdam and I'm
going to confront you with a hypothesis you're going to declare to me today.
So what's the key prediction that you see in front of your mind's eye right
now that you know you're going to have confirmed five years from now?
But that would be a very low-level, easy prediction.
It just turns out another way. A prediction that could be confirmed in five years from now.
Something ambitious and impressive. Yeah, not something small,
petty thing like gamma rhythm.
You can do better than that.
Well, what we're working on a lot is multimodal integration these days,
but also perception actually in rats and mice.
So we do have these four area recordings also going on in, let's say,
lower and higher visual areas, including singlet and parietal.
And there I would predict that visual perception, as we also link it to consciousness,
involves the discrete and repeated iterative interactions between lower and
higher areas but not exclusively in a top-down fashion.
Look, you're being rather demanding on my short-term memory here.
So what's the prediction?
Well, basically that visual perception also is a network phenomenon that not
only depends on high to low level feedback,
in this case for the visual system,
but should be more viewed as an ongoing, short-lasting reverberation also involving
the higher systems. Very, very good.
So, Cyril Pennard, thank you very much for this conversation.
Thank you, Paul, as well.
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