This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschoor and Tony Prescott.
This is Paul for sure with the Convergent Science Network podcast and I'm speaking
with Aldo Genovesio who is here as a speaker of our summer school and Aldo you
you study the prefrontal cortex in the monkey so why why what's so interesting
about prefrontal cortex why are looking at this structure.
Yeah, the prefrontal cortex is very interesting for many reasons and my interest
in the prefrontal cortex is now related to all the integrative capacity that
the prefrontal cortex has and,
in the ability of the prefrontal cortex to generate goals based on different
type of computations and I was always interested in the computations that an
area like the prefrontal cortex and the form.
They are very interesting and our information can be combined in the prefrontal
cortex in order to reach a goal.
So the prefrontal cortex in primates,
is in some sense related to complex
behavior plans executive control working memory
attentional selection integration associative learning
rule learning that's quite a list of functions
so are there some underlying underlying dimensions to all these different functions
i think that what we we do in neurophysiology we try to understand first the
role of the upper frontal cortex for each of these functions independently.
And after we tried to understand if we can explain the same data,
for example, this is the case of a working memory.
For a long time, we thought that many tasks, many task activity was related
just to a working memory of a stimulus.
And after we discovered that a lot of this activity could be explained by just an attentional effect.
So we try to understand each of these functions separately,
but also together to understand which functions come to a play in a specific task.
But one common ground could be found in the role of the prefrontal cortex in
the generation of goals.
And this is a difference that the prefrontal cortex can have also compared to
other areas like the parietal cortex that represents pace and time, for example, numbers,
different magnitudes, but doesn't have this capacity that the prefrontal cortex
has to be connected to the premotor cortex in a special way to generate goals.
Okay, so how do you study this ability to generate or maintain goals and strategies in prefrontal cortex?
We designed a task that we called a strategy task in order to try to understand
how the representation of future and previous goals are represented in the brain.
And this task was requiring the monkey to make a choice based on the repetition
or not of the cue from the previous trial.
And the task was organized in a way in which the monkey could maintain in memory
the information about the future goal, and we could study, because there was
a delay before a monkey could plan a movement,
and this way we could study, for example, in that period,
the neural correlate of maintaining in memory a future goal.
So this is an approach to correlate the neural activity with the behavior.
You have a monkey that cannot make a choice. You study the neural activity in
that moment, and you look at how the information is maintained.
We can study the representation of future goals.
If we ask the man in the strategy task to remember also what the monkey did
before in order to perform the current trial,
we can also study what is the representation for a monkey of what the monkey did in the past.
But now, so what you also emphasize there that monkeys already come to the task
pre-equipped, if you want, with some standard strategies that they use in these tasks.
Yes, it was discovered by Betsy Burra in the National Institute of Health,
working with Steve Wise, that
when monkeys learn to associate a set of stimuli to a set of responses.
They do something more than just learning the association.
But because they understand the underlying structure of the task,
they are able to apply two strategies.
One we call the repeat-stay strategy. Every time that the stimulus repeats,
the monkey needs to stay with the same response.
And the other strategy is a change-shift strategy in which the monkey shifts
goal every time that the stimulus is different.
This is because each stimulus is associated only with one response.
So the monkey can apply these two strategies.
They found the lesions of specific areas of the prefrontal cortex is able to
damage the use of this strategy.
It compromises the use of the strategies while the learning is still going on.
The monkeys are able to learn the association, but they cannot use the strategy.
It's something different just learning the association, but it's something of a higher order level.
Right. So, but so now what you found in a sub-region of prefrontal cortex.
In some of the found cells that are responding to these goals,
but also that are responding to these strategies.
So what's exactly the kind of physiology that you encountered there?
Yeah, we found a combination of signal at the level of a single cell.
This is the power of our methodology of behavioral neurophysiology because we
can investigate what a neuron does, if the same neuron combines different information or not.
And we find neurons that are able to combine a very abstract representation
such as a strategy with information about the goal, so about what will be the
goal that the monkey is going to choose or with the previous goal.
So they encode the conjunction of goal and strategy.
And this is also another difference between prefrontal and parietal cortex,
the ability of encoding conjunctions of factors, or in this case a strategy or a goal.
Because in this test, the monkey is trained to, let's say, reach for certain
locations or look at certain locations in order to get a reward,
and that's also the association it learns.
There's a cue, the cue is placed in space somewhere, and then dependent on the
correct response, it will get a juice reward or something of this kind.
So the goal is now a location in this task space.
And what I found interesting is indeed he has these conjunctive cells now.
So these are cells that would respond to, let's say, the cue,
the goal, or maybe the strategy.
But you found different combinations here, right? Because some of the cells
you measured from in this part of prefrontal cortex were specific to location,
to goal. Exactly. While others...
Were more mixed in their response. What's the kind of regularity that you extract from that?
Yes, it's difficult to know how the information combines, not to understand
the way, why the information combines in a way rather than another.
But we found that the higher order information, the strategy was not only encoded
together with the goal, but also with the stimulus feature.
For example, you could have a neuron that was encoding the stimulus A that was
leading to a strategy to a repeat-stay strategy but not to the change-shift strategy.
So both stimuli and goals could be associated to strategy in a very particular way.
But what I was saying today is that I thought this is a common rule.
We have exception and when we have an exception we try to understand why we have an exception.
We found that looking at the data, we found that neurons that were encoding
the future goal, what we are going to do next,
when we looked at the same neuron, we didn't see a signal related to the previous goal.
So these neurons are encoding what you want to do in the future,
but they don't know what you did in the past.
Why did you expect that it might reflect your past goal?
This task is a task that require to the monkey to remember the goal.
So we are in an experimental condition where we require the monkey to know the
goal, to remember the goal.
Because based on the previous goal, the monkey will decide the next goal.
But one thing that I don't understand, because on the one hand it's an association
task, right? You get the cue and now you have to go to certain location. Okay. Yes.
You learn these pairs. You're trained, and as a monkey you're trained for many months on these pairs.
Okay? Yes. So then how does your previous goal figure into this?
In our experiment we had two tasks. We had one learning task where the monkey
were applying also the strategy, and one only strategy task.
The strategy task actually didn't ask the monkey to associate stimuli to responses,
It was just asking to implement a strategy.
So in this case, the monkey just needed to remember the previous goal in order
to choose the next. Okay. Because today…,
I simplified a little bit the task. So there's a dependency then between these goals. Exactly.
If the first goal is left lower corner, then the next one might be right upper
corner. Some regularity of that kind. Exactly.
Depending on the next cue. If the cue is the same, the monkey has to use the
repeat stay, otherwise the change shift. Exactly.
So the cue is informing the monkey about the rule it should follow.
The fact that it is the same or different. Right, exactly. But then,
so now here with this task, We understand the manipulation. We understand the role of memory.
And now you find two things. And why don't we have sort of a bag,
if you want, a collection of cells with variable responses, right?
Some are strategy-specific.
Others are conjunctive. They mix different aspects of the task.
But in some sense, they're not reflecting the memory of the task.
So that would give you a rather incomplete representation. So in this case,
where do you think then the memory of the previous goal resides?
Is it still within the system and you were just unlucky? You didn't see it?
We found it. We found a signal related to the memory of a previous goal,
but that signal is never in the same neuron that represents the signal that
represents the future goal.
So it is there, the representation of a previous goal, or a previous spatial
goal, but in neurons that are different from the neurons that represent the future goal.
You see this as a principle? That's really an organizational principle?
What I think is that, for example, if you go to buy food, for example,
in a grocery store, you have a list of objects or food that you need to buy.
For example, you see a milk, you take the milk, this is your future goal,
and you put your milk with you. You buy it.
If you see the milk again, you don't buy it again because the milk now,
it was a future goal, before, but now it's a previous goal.
So there is probably the need for us to perform what we call output monitoring function.
That is to, in each instance, to know which goal is still pending and which goal is accomplished.
Because if a goal is still pending, we need to accomplish it.
And we can do it even in very simple tasks like buying food in a grocery store.
But what the results say, showing a separation of representation within the
prefrontal cortex between future and previous goal, is that,
Having two separate representations may facilitate an operation of monitoring
on these two representations.
If we want to know if something was done or not, we look where the information is.
I don't know from the computational perspective how this sounds reasonable,
but it looks like that at least in our data we see that we have this distinction
between the two, at least in our task. Right.
But then is this representation of the previous goal only pertaining to the
previous trial or does it have a variable depth?
Is the monkey also representing the goal 10 trials back? Okay.
I actually, I don't think that, I never did a very sophisticated analysis of two trials back.
Maybe there could be an effect, but it was so small to be identified with normal statistic analysis.
Maybe at the population level, there could have been a small effect,
but it was nothing visible, let's say.
So I think that if there is an effect, it's small. Do you have the sense that
behaviorally the monkey keeps a memory depth that's larger than one trial?
In this task, we don't know because of this task.
There are other studies that show that prefrontal cortex can maintain more than one trial in memory,
but maybe this trial was not the trial it was asking the prefrontal cortex to
do. to maintain for two trials.
But now the other thing is that of the cells that you measured,
you have about, let's say, you then try to classify your different cells, right?
So some are conjunctive and some are strategy-related and so on.
But it's interesting that these classifications,
stop at around 30 percent right so you always have a large subset of cells that have no.
Interpretation exactly so in your mind what are those cells doing let's say
that when i say that 30 percent of cell are representing the future goal we
had to think that maybe other 10 percent are representing the previous goal
or another 10 percent can represent the stimulus feature sure.
So I don't know, I cannot tell you now what is the number of cells that don't
represent any variable of a task.
But this is an interesting question, to know of all the variables that we studied,
how many cells were out of a task. So I don't know.
Because there's another aspect to this, right? That in some sense,
we are interpreting the way in which the monkey brain is describing a task, right?
So we say, okay, there are goals, there their cues, their rules.
That's it. That's what we're going to look for.
But it's not impossible that that monkey brain actually is introducing other
aspects to a task description that we're not looking for. Exactly.
So do you have any idea what these additional factors could be that could help
you to interpret these unclassified cells? Okay, yes.
We, by chance, we looked at the activity because in the exploration phase of
the data, We looked at the activity after the delay, after the period of presentation of a stimulus.
It could be one second, one second and a half, and two seconds, for example.
And we saw that there was, I think, 15% of cells that were modulated by the
duration of the previous cue.
So that was an irrelevant information, and we were able to look at,
this because we by by looking at the raster we noticed that there was an incredible
effect in some cells so we said oh what is this and we we now we know that this
is a an encoding of elapsed time that could not be explained by the reaction
time so we did several analysis to um,
factor out for example the reaction time effect and so we we by chance we looked
at this cell so So you never know if a cell doesn't do anything because maybe
you didn't look at the right variable. Right, exactly.
So you're right. So you would say mixed in to these features that are encoded
are also temporal aspects of the task.
But I cannot tell you how many of these cells are encoding because this analysis may not have an end.
So you never know when to stop. Of course. To analyze it, to look at combination.
So at this moment, I don't know if these cells are encoding some other factor. Very nice. Okay.
So now we have an understanding of what this prefrontal cortex does.
It represents parts of the task, but also some linking in time,
like the previous goal, the current goal, or a possible future goal. And now...
You interpret these representations in terms of output monitoring.
Yes. So why do you think output monitoring is a good way to describe these properties?
I think that the separation of goals in the two networks is not output monitoring per se,
but can facilitate output monitoring if we assume
that some other cell will look at this activity to decide if an external observer
that we can think that can be another area or another group of neurons will
decide if a goal was accomplished or not looking simply at the present.
Of activity in one of these two networks. This is a possibility, but it may be also wrong.
But we know that we have failure on monitoring in prefrontal patients,
so it's one of the problems of people with prefrontal damage.
For example, they are not able to accomplish a serious goal,
they get confused, they don't know if they did something or not.
So we know that also lesions of epiphytonal cortis or patients with dementia
have problems in this task.
So it's also related to some disease, psychiatric or neurological disease.
So, but monitoring in this case means something very specific,
such as goal achievement, or is goal achievement an operationalization to measure
something that's broader?
Could be broader. Could be other, we know that we, but I think that we need
to monitor many variables in what we do.
For example, maybe we will talk later about my last studies,
but I can anticipate that we see that goal is important.
And it's not only prefrontal cortex important to encode what we are going to
do, but it's also important to monitor what we are doing, what we are finishing to do.
So golf may be a special relevance not only when we look at the future but also when we look at.
What we did and even when this was
this is not important anymore for what
we are doing now but would you say this frontal area is
sort of imposing an intentionality on on the world because
you could argue look at this frontal area is just filtering everything that
this animal is engaged with with respect to goals and just saying look okay
which of my goals did i achieve where am i vile where am i failing to achieve
a goal so it's really this massive goal filter is that is that a reasonable way to look at it?
Yeah, I think that there is a, gold can be the main function.
And I give you another example, and that we have looking at the correlation between neurons.
So we, okay, we can, we recorded several neurons simultaneously.
And because we recorded for many, many days, and with several electrodes on
time, we can have two conditions, one in which we have two future gold cells
recorded simultaneously, and another in which we have two previous gold cells
recorded simultaneously.
We can look at the correlation between future and future cell and past and past cell.
We see that they're correlated only the neurons that represent the future goal,
but the neurons that represent the previous goal are not correlated.
So, it's true that prefrontal cortex encodes goals, but maybe the future goal
has a special relevance that we can see from the correlated activity that can
be a way of driving the premotor cortex in a choice.
Right, exactly. In the selection of one course of action. So the correlation
you observed is only between these goal cells?
The future goal cells. Only the future goal cells. Yes. Okay.
And when we look at the cells that are encoding what you did before,
and you find by chance the same record in two of these cells,
they are not correlated.
So they don't have anything. So this is still mysterious for us.
We have these results, but we still don't know.
But are you interpreting this like future goals have to be included in monitoring in the future,
so I have to sort of maintain these representations and has to make them part
of a possible task set or something of that kind?
Well, these old goals in some sense don't matter that much anymore.
I can slowly forget about them, so I don't have to maintain them.
Maybe they don't need to activate simultaneously other neurons.
They don't need mechanisms of temporal summation, for example,
to activate neurons in a competitive way.
To generate a behavior in the premotor cortex and the cortex later.
Okay, so that means you see these goal cells as also organizers of neural activity,
for instance neural activity pertaining to cues that you might see in actions and so on.
Yeah, for now we know about the future goal. We don't know if this correlated
activity can apply to other couples or pairs of neurons.
We don't know if the dichotomy is between past and future. Now it looks like that.
Unless we may discover one day there was another reason.
But now it looks like it's past future. So that means that you have in the response,
the neural response in prefrontal cortex.
You find that these future goal cells are tightly coupled in their activity
while past goal cells are not coupled.
And also does it imply that these future goal cells are also strongly coupled to future cues?
Could be, could be, yeah. But there's no data on that. There is no data,
but you're right. Could be, we don't know. Would you predict that?
If I, it's difficult to know. It depends on the task. We need to imagine a task to think that.
No, but look, if it's the same task, wouldn't you suggest, look, I have a future goal.
Then I use that. I use that state to also already predict, ah,
if that's my goal, then I would expect this Q and I expect whatever, this kind of response.
In this task, the goal that the monkey is achieving doesn't predict the future,
the next goal after the future.
So it's difficult to know if, so it stops there.
Right, I understand. So now we have a bit of an idea about the representation
of a task in prefrontal cortex organized around goals, future and past.
And then in some sense, you also
followed this notion of a hierarchical structuring of the frontal lobe.
You moved to the frontal pole, which is sort of really the structure all the
way. Yeah, we moved at the end of the brain. Yeah, exactly. There was nothing else.
Beyond that, that's it, right? There's only skull. So what did you expect to find there?
I think that we're considering that this area is in a special location that
is farther compared to the dorsolateral prefrontal cortex from the premotor cortex.
Context, we were thinking, and also considering that this is a primate innovation,
is an area that should solve new problems maybe in evolution,
I don't know, maybe a trivial idea was to find more complexity and that we thought
to be more integration of information.
Which also would be consistent, I think, with the literature,
there's some hierarchy, it gets more and more abstract, but all the features
are sort of abstracted. Exactly, exactly.
Yes, yes. And so we found that the frontal pole cortex had a completely different function.
It is a monitoring function, so it has just at least, we could study the cells in only a few tasks,
but we found that these cells are active only when the monkey receives the feedback
about the correctness or not of the behavior and it represents the goal that the monkey achieved.
We don't find any other signal of
the signals that I described previously like the representation of a
stimulus or the representation of the past goal or
a prospective representation of a future goal nothing but when I say nothing
really nothing no example so not a few was really impossible to find the example
and we found this very clear signal in 30% of the cell around the monitor but
it's very surprising right because is there anything special about let's say say,
the anatomical organization of this structure compared to other parts of prefrontal cortex?
Yeah, the spatial position is farther from the premotor cortex,
and so it speaks more with other prefrontal area than with the premotor cortex
compared to the dorsolateral prefrontal cortex. Right.
So this is kind of spatial, and so it's more distant from the behavior.
Yeah, but this also means this is very odd, right? Because we have this cortical
circuit, the different parts of the frontal cortex will be tightly interconnected in this.
Earlier area you measured from closer to the motor cortex itself,
you find cells related to future goals, present goals, past goals.
So there's a clear temporal window.
You would believe, given the anatomy,
that this should all percolate up towards the pole. Yeah, I agree.
And it's not there. Yeah. It's interesting because, you know,
these two areas are connected to each other.
They have reciprocal pronation, but the information that maybe comes up to the
frontal pole doesn't activate the frontal pole the way in which we think about.
Yeah, but how could you get this selectivity?
Because, I mean, the prefrontal cortex deals with monitoring and goals.
It operates in a certain temporal window with future and past included.
Now, you go all the way in the front to the frontal pole, and it lives in the
now, right? It's completely just now, completely unlocked to the task.
Yeah, we have this activity even in the dorsolateral prefrontal cortex,
but it's not the only activity that we have.
So this is what I was saying, that it looks like a filtering function is applied
where everything else is gone and it remains.
But actually, we need really a model to understand the function,
why we need cells like these.
But would you say that, does that imply that the brain, all the way in the front,
the most exclusive spot of the cortex, is actually completely preoccupied with
just assessing how well we do with our real-time performance now?
Would you buy that? Maybe this is, as we were discussing today,
this looks not so important in a simple task like this, in a more complex task,
we might understand better the function of these cells.
We might think, okay, without these cells, how could we do?
But in this task, it is more difficult to understand their role.
But what I was thinking is that this signal can help.
We don't know why this signal can do that, but can help the transfer of information.
But is this observation consistent with the clinical literature,
like patients with lesions only to the frontal pole?
The problem is that it's never only the frontal pole.
The lesions are very broad, especially frontal pole is part of a larger area that is damaged.
So it's very difficult to know now. This is why we study also monkey because
neuropsychology in humans is very limitative on that. Right.
If we look at this hierarchy of processing in the frontal lobe,
then what you were saying.
Areas like orbital frontal cortex, so on, might have a good understanding of
the task and the strategies you use, but the only area that actually really
know whether you succeeded, really knows success, is the frontal pole.
In a very unique way, yes. That doesn't do anything else and it has a pure signal
until we find something else. Of course.
But it is that. Orbital frontal cortex, we studied also orbital frontal cortex
with a second strategy task.
More simple and we found that there is an activity related to the goal at the
moment of the feedback, but this activity doesn't depend on the success.
So these neurons are just representing the goal as left or right,
but they don't consider the success of the.
Task. And then you see this, so frontal pole monitors now the task, success,
we did it, and then do you believe it's that information that percolates back
into other areas of the frontal cortex to represent the previous goal?
I may, okay, let's assume that there is already the representation about the
previous and the future goal, about the future goal.
And this activity about the future goal need to move from a group of neuron
to another group of neuron. And let's assume that there is already a connection
between the future goal cells and the previous goal cells.
But this connection is not enough to activate. This is an hypothesis.
It's not enough to activate the previous goal activity.
It may need an additional input. This is a possibility.
It may give an additional input that together with the input from a future goal
cell may activate the previous goal cell. But this is just a scheme just to reason about.
Right, exactly. Because the difficulty of that scheme is that in some cases
you really start to think about it as different modules performing specific operations.
I don't think that there could be a dissociation.
We found future and ghost cells in all the penetrations. So I don't think about
the spatial segregation of these cells.
So this is not my idea. Yeah, but there could be, because these neurons belong
to different population of cells, we know already that there are two different
networks, partially overlapped.
The overlapping is very small. We have a few hybrid cells.
So we have already a segregation in the same area of these neurons.
Right. But would you say there is any critical anatomical difference between
the circuits in the frontal lobe and, or pole, sorry, and let's say premotor?
Yeah, but motor cortex has this connection with the motor cortex,
so it's very close to the… But in terms of the local circuit,
really how the cells are wired up together?
Within each area, it's difficult to say. Right. Yeah.
That's interesting, right? Because even… so you find these rather variable response
patterns in a neural substrate that at the local level is relatively uniform.
Exactly. We don't know. We don't know what is the difference in activity of
cells in different layers.
This is often a limitation of a neurophysiology that we don't know where we're recording.
Often we don't know if it's a pyramidal neuron that's an output outside or an
interneuron. Right, exactly.
We will do that. Probably there are now new electrodes with multi-contacts.
And probably this will be the answer to these questions to understand the microcircuit.
Of course. Of course. So how many cells in the frontal pole could you actually
characterize in this way?
We recorded hundreds of cells.
And how many could you classify? Let's say it was 30%. So significant.
So do you believe that this argues against this very hierarchical view on the
frontal lobes and this hierarchical abstracting?
Of information towards the frontal pole? This very specific signal can be a
result of an abstraction.
It can be something because alpha is a very specific signal that can be the
result of an abstraction from many other signals in a different way from what we are used to think.
So the abstraction is not just integration but it's producing something new that could be a monitor.
Of course, abstraction is also to remove information, right? and to become specific.
Exactly, yes. Okay. So maybe that.
But for sure we need to study more of the frontal pole cortex.
We still have, you know, this is the first study. Sure, of course.
So our speculation in the case of the frontal pole cortex is very limited to our results.
And this is a difference with other areas where we know we can think and compare
the results from different studies and to spend our days comparing.
Exactly. Here we have only this study and some neuroimaging study where we know
that also only a part of the frontal pole cortex can be an homologous area with
the monkey by frontal cortex. Right, exactly.
So we have this additional difficulty with the frontal pole cortex that we don't
know exactly if there is an homology. Right, exactly.
But that's not necessarily bad. That means there's still quite some experiments
to be performed. Yeah, it's still a frontal pole. I feel that we are still studying
the frontal pole with a common origin from both human and man.
The other thing is that in this frontal area, the monitoring area,
goal-oriented if you want, the question is of course also what's the kind of
information it receives from other areas, from other modalities?
If you have a visual cue, what information of the visual cue really enters in
this area? and you could imagine that this again may be symbolic and fairly abstract and for that.
You performed a number of experiments where you start to look at how really
very specific aspects of visual stimuli would engage with these decision-making circuits.
So what was really the idea there? Here we use different modalities.
In the frontal pole, we use different cues like orientation, color, and reward.
Also, we have this connection with the orbital frontal cortex,
so the number of drops of reward could have been an appropriate cue.
Queue, but we didn't find any representation of the queue, either as a color,
an orientation, or a number of reward.
This is very interesting because we found an increase of activity that was selective
for the goal in the monitoring phase.
It was not just selective for the goal, but was just a ramping up of activity.
So, without using as a cue a reward, we might have thought that this was a reward
signal with an appetitive meaning.
But interestingly, when we look at the presentation of a reward as cue,
we don't see any change of activity. The activity remains flat.
So this is very interesting that the reward per se is not able to activate this
neuron. So it is something that has to do with the monitoring of a success independently
of getting something good.
So we were lucky to think about having the reward as a cue.
Of course. Otherwise we would think about doing that experiment now.
At least one experiment less is done.
That's very good. So what I was thinking about is in the next set of experiments
where you sort of backtrack again out of the frontal pole.
Where you start to look really at, okay, this is a rather puzzling question, right?
The responses, so here we have the frontal cortex.
It responds to different aspects
of a task, but these responses seem normalized in some sense, right?
So even though you might have the physical salience of the stimulus vary,
that might be small or big, for instance, or close or far, the neural response
doesn't seem to be really strongly modulated by these differences in just the
visual aspects or the properties of the stimulus.
You're talking about the distance task? Yeah, exactly.
I showed you SELITAR encoding the decision today, so the decision about which
stimulus is farther, which stimulus is longer.
So I focused on this aspect.
But they still encode the feature of the stimulus and the small details of the task.
Yeah, but they don't seem to be strongly modulated.
Look, if a stimulus might be more salient because it's nearby or it's bigger,
the response you find in frontal cortex is not that different as to another
stimulus that might be further away and smaller.
Or do you find modulations there? In this case, in the distance task,
we have stimulants that have a different distance from the center, not from the animal.
Maybe from the animal it could have been different.
And we see actually pretty much the same number of cellular encoding with a
greater activity that the first stimulus is farther than the second or vice versa.
So we I don't know if you meant that we don't see an unbalanced representation of,
Looking at the relative representation of which stimulus is farther.
But what you're looking at is really the physical organization of the display
and how this percolates into the neural response in the frontal cortex.
And it appears that it seems to be normalized. These cells actually don't really
care about, let's say, saccadic distance or anything like this.
It's just saying, no, this is a cue and the cue is in the display and has to
represent it. Yeah, maybe you want to say that this is what I'm presenting is
an abstract representation.
It doesn't depend on the actual position. So the position can be very different,
but the signal is the same. That's right.
So the important thing is that there is a relationship with one distance.
Right, well, you could argue it's like a symbolic representation,
right? Because it's not modulated by the analog properties of the cue.
Yeah, but we still have, also in this case, cells with mixture properties.
For example, a very interesting cell is a cell that is encoding,
for example, that the second stimulus is farther than the first,
but only when the second stimulus is on the top.
So we have cells that are an intermediate level of abstraction.
These are very interesting, these cells, that really it depends not only...
So this looks like an intermediate representation.
Why are you saying that? Because you could also argue it's a rule.
This cell extracted the rule.
The final one that represents just which stimulus is farther independently of the position.
Or if you say a certain cue only when another cue is in a certain position.
So you have a conditional.
I don't think that this has to represent a rule because the rule is always the
same. The rule is choose the second stimulus.
That's your behavioral rule. Yes. But you could also argue that the display
of the stimuli is following certain rule-like regularities.
So, Fred, you just described a cell that gave a specific response to a certain
cue, but conditional on another cue being in a certain position. Yes.
So, I could say that's a rule that defines that little part of the display.
Yeah, you can say that. So, these are the rules. You can also think in this term. Exactly.
So, it's not really feature encoded, but it's rule encoded.
It could be. You could think, yeah, it's conditioned to the appearance of a
stimulus in a special location.
Or you may think it more simply is strictly associated to space and it cannot
generalize more than that.
I don't know. It's difficult to know. But it's an important distinction because
in your interpretation, so you're saying, well, prefrontal is not,
if you want, really encapsulated and segregated from this dirty, noisy outside world.
It's not just the abstract and the beauty. Exactly.
Right? Because it's sort of also the physical properties of the world and of
the display percolate into its representation.
You were saying like, that's where you talked about the mixture.
Yeah. Because we know that neurons in prefrontal cortex care about space,
they have receptive fields, you know, really associated. Sure.
But some neurons go beyond that.
Others, maybe they are representing in a conditional way, as you say,
more in a rule-like, but For me,
I see that activity more related to the physical world, but I don't know.
Yeah, but wouldn't this be… It's difficult to know. As a physiologist,
wouldn't this be, or from a computational point of view, this would be tremendously
annoying because here I have this neuron.
It sits all the way behind a huge hierarchy of processing of,
in this case, visual stimuli.
But where we know we go through areas, like if you're a temporal cortex,
where you have very invariant representations to the world.
And now I'm receiving, I'm in the frontal cortex, I'm receiving this kind of information.
And I can just do my job so you would believe that, of course,
you take the most abstract representation so you reduce noise and so on, right?
So in that sense, in this perspective, you could argue, well,
there's a lot, we lose a lot when we drop the very clear segregation between
more analog representations of the world.
That means they vary with properties of the world. Yeah, with the distance in
this case would be the distance, but the first level of analysis would be the
distance from the center.
Right. But you seem to be much more permissive than I am.
You're more like, well, okay, maybe they vary properties of the world,
but doesn't that mean that your whole concept of how this part of the brain
is organized and relates to other parts of the brain falls apart in some sense?
My idea is that we have a basic representation is similar to the parietal cortex of a metric.
For example, the distance or if the stimulus is on the top or on the bottom.
And after we have a representation it is
very abstract we have something in the middle and unfortunately when I looked
at these cells that we don't know what they are it is very difficult to understand
based on the temporal profile of their activation where they what is their role
so it's I think that also in this case.
We need a model to understand because otherwise very difficult to understand,
especially why we need certain computation instead of others. This is my point.
Why we need, for example, I'm describing cell that are encoding which stimulus
is farther based on the order first or second.
But this is not a requirement of the task because the monkey has just to say
the blue or the red is farther.
So we have also we were talking at the beginning of the interview about this.
Do we have a neuron that represents something that the monkey doesn't need to know?
The order is something like that, because we could avoid having a representation
based on the order, but we still have it.
And so maybe everything needs to be organized in an ordered way.
Right. And this is even when the task doesn't require that.
But do you think there's something to be gained,
to try to reinterpret the responses of
these mixed cells as you call them in a rule in terms of rules would that help
you you think to understand how the system works or I don't know it's a futile
exercise I don't know I don't know if it can help to conceptualize that as a
rule or as half a rule or I don't know it's very difficult to okay but now,
so the neurons that you looked at.
Would you say that in the end, they operate in a similar encoding space?
Like, for instance, they all normalize their responses in some way.
Like, for instance, if I would have, let's say, a stimulus that's further away
or nearby, do I try to normalize all these responses to say,
look, no, the only thing that matters really are my decision variables that
tell me something about evidence.
I don't want to know anything about, let's say, physical organization of the
scene. Or do you really see this mixed?
No, no, I see this mixed. The cells that I showed you are the pure cells,
because otherwise, in a talk, if you start to show a mixed cell, people get confused.
They say, for example, a common idea is that, we had also a problem with some
referee in the past, is that how is it possible this cell is representing this,
if this cell is representing also some other variables.
But we know that a cell can represent more variables at the same time.
But if you present in a talk, for example, a mixture of cells,
many people, you know, we need to explain very well what is going on.
So if we have poor cells and we want to make the point that these neurons make spatial computation.
But now, doesn't it raise another problem? Because here we go.
Now you allow these mixed cells. So that means if I'm reading out a mixed cell
and the mixed cell is active, I have uncertainty because I don't really know
if it's the physical display or is it because there's some important decision information.
So that would mean….
If I have an integration-based model of decision-making, which is a popular
way to think about it, that you say, okay, I just integrate my evidence over
time and whatever reaches threshold first is what I do, then your mixed cells
would mess up that whole scheme.
Exactly. So you need to… So how are you going to solve that? You need to have,
we can only think about a distributed
way of representing an information that is mixed to other information and can
be still extracted because all the other information will be filtered out because
they would be on the opposite side. Right.
But the point is, of course, this is why I was sort of hoping to find some sort of symbolic encoding.
You say, look, I just care about informational aspects that pertain to my task,
and I'm not biased by salience and so on.
But now so that's not what you find no right
and we are already in part of the prefrontal cortex that are
really close to the motor cortex where we're going to
our premotor where we're going to execute our actions so i
have to get to if you want a pure informational representation in my frontal
cortex before i can really make optimal decisions exactly so where is that happening
then we it could happen in prefrontal but at a certain time and after a certain
computation is performed formed, we don't know.
Also it would be interesting again to look at the correlation of these cells
like we were saying, if the cell that are representing a pure signal are more
correlated than the cell.
But have you ever found in any of your experiments a majority of responses being
driven by these, let's call them, pure cells?
No, it's always a minority.
Right. Yeah, it's always a minority. Well, this is interesting,
right, because this might be telling us that also this idea of just pure rate-based
encoding of decision-making might actually not be the reality of the frontal cortex.
Because if you have these mixed encodings with conjunctive cells further modulated
by properties of the display, these responses, these rates of responding,
are not informative on the goals you have to pursue.
Exactly. So we don't know if the information will be extracted from these cells
or if these cells are just an intermediate step for the final computation.
So this is what we don't know. Right.
Is the frontal pole better at that? The frontal pole, for now we know that there is just one signal.
So we- Right, exactly. So also that cannot help you, right? Exactly.
So it's on the opposite extreme,
but also I was saying today that we don't have to expect every signal present
in the prefrontal cortex, for example, in the strategy task in the dorsolateral
prefrontal cortex, the monkey needed to remember the previous stimulus.
We didn't find any evidence in thousands of cells of the representation of the previous stimulus.
While the new task that I was describing, the distance task,
just required a monitoring.
That doesn't require really a monitoring, but we show that the cells show a monitoring activity.
We see that when a stimulus was a goal, it's still represented,
but even if it's not necessary to represent it, but when it was necessary to
represent it, it wasn't.
Right, exactly. In dorsal hyperfrontal cortex, obviously. Yes, this is amazing, right?
So the dominant view on decision-making in the cortex is… The dominant view
is very much integration-based, right, according to this race model of bounded diffusion models.
So your data doesn't really fit that model.
So in your mind therefore these bounded diffusion models are not an accurate
description of this system or is it just something that you haven't looked at sufficiently yet?
No, I don't think that it's in contrast with this model.
It's just a more complex task where we can maybe look at a raised model where
we can study the activity based on a raised model.
Model, but still we may have competing goals, like right and left or different
objects that compete with each other, even in my task.
But the moment in which that happens is very fast, so it's very difficult for us to study.
Unless we maybe study an interpopulation of cells recorded simultaneously,
we may understand more what is going on in the moment of a decision,
but the decision is very fast.
Yeah, but still you talk about what, dozens if not hundreds of milliseconds, right?
Yes. So it's not that fast, is it?
But maybe to understand in our task what a single neuron does,
I think that is difficult.
Because we don't see the representation of competing alternative because immediately,
the monkey can make a decision about one goal compared to the other.
So there is no, maybe the uncertainty that we can have, you can have with a
random dot experiment where you can manipulate the uncertainty.
Here we cannot manipulate the uncertainty.
Okay. So it's difficult without manipulating it. Okay, but, but.
Wouldn't your data suggest that there must be other modes of integrating information
in prefrontal cortex that is more sophisticated than just adding up?
Yeah, information like the movement of the dots.
So I think that the computation that is required here is not too much.
I don't know. I cannot see it in the context of a race model.
Right, exactly. So it's more difficult for me to see it within the paradigm.
Now which is good because i think the race model is is not necessarily it's
only the beginning of a story on decision making and certainly not the end right
um so now in your last set of experiments.
You talked about the encoding of irrelevant information yes this is so why would
that be interesting because in some sense it's like i know these people that
that you know you go shopping you get a shopping list but first they tell you
all the things you do not have to get that, which seems very inefficient.
So why would you worry about encoding irrelevant information?
Yeah, I think it's important to encode irrelevant goal. We found that mainly
we encode relevant goal because we may find better way of reaching a solution.
For example, think about a task in which you need to associate A and B to different
position, but you perform this task and after a while you understand that you
are required only to go right.
So you can avoid taking care of or paying attention to the stimulus.
Without monitoring you would continue to perform the task in a more complex
way. So I think we have a lot of situations like that where we can find a shortcut.
Okay, so it's like an incremental pruning of information in some sense.
Yes, I think so, yeah. Okay.
So what's the mechanism there? How does this play out in prefrontal cortex?
Now my study doesn't allow us to understand the way in which this information is used.
So we don't have a use of this information.
And so it would be nice to design a task where we can see all this information.
But how rapidly does... Because here comes the display. I perform my task, okay?
So it would expect I have all these cells, I have my conjunctive cells,
I have whatever. So all these cells get allocated to describing this task.
But now I'm going to figure out that the subset of these descriptions are irrelevant.
Yes. So then you would expect these cells start to drift again and their response
is in some way... We don't know if these cells were more before.
Because we may be, you know, in learning the paradigm, the cells were double
than the cells that we are having now.
And now we are just looking at the survivals of a mechanism that is broader
than this with much more neurons involved.
And also an interesting point that should be studied is that now we have cells
that are encoding the previous goal when it is irrelevant. But what if we start
to give wrong messages to the monkey and the monkey gets confused?
Will the monkey start to monitor other information different from gold and will be confused?
Dorsolateral prefrontal cortex the right area where to find that
information so i don't know okay so there are many questions open but
you do see you would suggest that there is always a
goal-driven monitoring of the task to sort
of focus on the relevant information there's
a continuous selection process going on there is that what you have in mind
i think that yes but not only that because we have also we have relevant goals
that are represented but But the interesting thing is that this representation
is not so much bigger than the information about the previous goal that we found when the goal,
we might get the requirement to maintain in memory the goal.
That was kind of interesting because we don't find big difference when we need
to remember something from when we don't need at all.
Okay. So that… How do you explain that?
It's very difficult to understand that. Could this be a nonspecific kind of memory response?
Let's say whatever you shoot into this prefrontal cortex, you have a memory field, right?
So it will be sort of automatically maintained, non-specifically with respect to the task.
If it's a goal, yes. Would you buy that interpretation?
The problem now is that what I think is that now we need to be more cautious
when we study, when we say that the signal is associated to a behavior.
For example, if we can find neurons that encode a previous goal,
even when the previous goal was not a requirement.
Now when I have a requirement in a task, I don't know anymore if this is a monitoring
activity or a memory activity.
As a function of reaching a goal.
So now this is my idea that we need to think about this.
So that means that now after all these experiments, one important conclusion
is that to just only interpret activity in frontal cortex in terms of goal monitoring
is not sufficient anymore.
There might also be more non-specific memory effects playing out.
This is what you're saying.
I'm saying that when we know that there is a task requirement and we see a representation,
that we think, okay, we have this representation because there is that task requirement.
Maybe it's not because we have the task requirement. Maybe even without it,
we would have that representation. Exactly right.
But would you have any difference in the physiological signature of these responses?
Like a nonspecific memory response, so that it's not dependent on a specific task requirement.
Requirement might for instance have a different latency a different amplitude
are there any differences there that helps you because we are
comparing different tasks different monkeys just a proportion of
cells that are selective so it's very difficult now to
say i would say that the proportion of cells is not so different i
can and the task is different as different requirements so it's very difficult
to compare latencies would be interesting i think to continue this line of research
with chronic recording and to see and manipulating the experiment and to understand
this is the way to do instead of doing multiple experiments, comparing them.
So now we know we have sort of different pools of cells, at least around sort
of goal monitoring and memory.
Yes. And so now we have an evolving task, right? So I have one trial,
I succeed or not, I go to the next one and so on.
What's the information transfer between these trials?
What do I carry over in information? These last experiments you described,
you try to assess, indeed, look, I have a task configuration.
I have a goal, I have an action, I get a reward or not. And now I get my next trial.
So the question is, okay, of this trial at T is one,
What's the information that I really carry over to the trial at T plus 10?
Yeah. Right? We, yeah, we, I think that we need to design an experiment to see
under which condition we, we have a memory activity that goes over the monitor activity.
It is something more than a monitor. But I thought you were suggesting today
that actually only information on the previous goal really moves over.
So for example, you can introduce random mistakes.
For example, sometimes you don't give a reward to a monkey. and what happens next in the next trial?
Is the monkey starting to represent something more than this because it needs
to reconsider the rule of the task and maybe we start to monitor even the characteristics
of the second stimulus? Right.
Yeah. So this would be my, if I could do 10 experiments at the same time,
this would be one of them.
So now we looked at prefrontal cortex. So, but in terms of territory,
How big a percentage of the neocortex do we call prefrontal cortex?
We have to distinguish granula and agranula prefrontal cortex.
The granula prefrontal cortex, we can study only in primates,
and this is a primate innovation of the granula prefrontal cortex.
Also the rodents of the prefrontal cortex, but it's just a granula.
So it depends on what we consider, and by the granula prefrontal cortex, probably it,
it has a role an important role in
this would be about one third of the cortical sheet in
primates it could be around maybe one third I can't tell exactly it's quite
a chunk of cortical so then the question becomes how many subdivisions of this
should we really consider we can consider a lateral part of the frontal cortex and a more,
orbit orbitofrontal part that sometimes is associated to a ventrolateral.
Or to the more medial dorsomedial prefrontal cortex.
So it depends on the scheme of connection that you consider and so the classification
is still depends on what you consider important if it is the connections to
have a common input a common output or you look at more of the functions or
different functions of the prefrontal cortex.
Is there consensus on this in the field? There is a consensus about macroscopic divisions, yes.
But when we look at each microscopic division, you can have more or less subdivision
based on how much you want to be specific, how much you want to divide.
So in anatomy, we never have a perfect
number of areas because also the
neurophysiology cannot follow because we just
now with neuroimaging with new with a
make with a possibility to do a magnetic
resonance to a monkey we know more about the location but it's still difficult
to target specifically a part of the orbital frontal cortex so when you see
for example as results about orbital frontal cortex they don't distinguish too
much there all the areas that Carmichael and price is divided by orbital frontal cortex.
So sometimes we have the tendency to divide more, to be more precise,
to see differences, but sometimes when it is too much, we cannot use it to understand the neurophysiology.
So it's very difficult to understand which is the right level in making divisions.
So would you say your monkeys do learn this task the same way humans would?
We know that the problem is for a monkey is to understand the rule of the games as usually.
So it's very difficult, you know, to understand, to compare. Okay.
Because they need to understand at the beginning that touching the screen can produce something.
And so it's very difficult for
also for the monkey to eliminate all the potential alternative to a task.
For example, the monkey can focus on the fact that it needs to choose the stimulus
on the top always, and after try the stimulus on the bottom,
and maybe the monkey find the solution and go back again.
So it's an exploration of possibilities, the training. So for your test,
how long do you train these monkeys usually?
Can be, let's say, rarely less than six months, one year. So I can reach two
years. Right, a lot of time.
So Aldo, to finish up our conversation, there are two things.
So you're reworking now on monkey physiology for quite a while.
You have gained also amazing insights in how the system works,
despite all the unclarities, but this is also the research aspect of it.
But now, if we would like to follow your example in the study of the brain,
what would be Aldo's law?
I think that what I tried to do was always to be kind of at the frontier of the field,
like with the frontal pole we were the first,
with the time we were among the first.
So I like to be one of the first to do something. So that was kind of a rule
that is on the other opposite. you may be interested in only one subject to
be a specialist of only one thing.
So I chose the other. Right. No.
Novelty. The novelty. I like the novelty and get bored after a while.
So I like some new challenge. Great. So, so far it was like this.
So Aldo, so five years from now, we're going to come visit you wherever you are. Now you're in Rome.
And I'm going to confront you with the prediction you're going to make today.
So I'm going to ask you, look, Aldo, you predicted X.
Did it happen or not? not. So what's the one prediction you would like to make
today that you're most… I would like to be able to record, let's say,
more neurons and more areas together,
simultaneously to understand really how a circuit made of different areas,
like we were saying, the frontal pole and the dorsolateral upper frontal cortex work together.
So I would like next time to answer more questions, some of the questions that
I was… No, I don't let you get away so easily.
I want a hypothesis on prefrontal cortex. Yeah, now I'm very interested in social
cognition, so I think that the next step will be talking about novelty.
So I'm not continuing studying time, but I'm moving to social interaction,
I want to understand really what all these signals, if all these signals that
I found previously apply to the representation of a different agent that is interacting with that.
So the new challenge is social, I think.
So in five years time, you think you have shown that the prefrontal cortex is
the substrate for social cognition? For some aspects, I hope,
at least for some aspects.
Great. So, Aldo Genovinozzi, thank you very much for this conversation.
Thank you, Paul. Thank you for inviting me.
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