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Matthew Diamond on whisker system and decision-making

Episode 13 15.03.2015

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Season 2015

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Can a rat perform the same perceptual decision-making tasks that were once thought to require a primate brain? Neuroscientist Matthew Diamond explains how rats trained on complex vibrotactile comparisons reveal fundamental principles of evidence accumulation, working memory, and sensory coding , and why individual differences between rats rival those between humans. Subscribe for more from the Convergent Science Network podcast series. Matthew Diamond joins Paul Verschure and Tony Prescott at the BCBT summer school to present his laboratory’s work on whisker-mediated decision-making in rats. Using a paradigm in which rats compare two vibrotactile stimuli separated by a delay, Diamond’s team has shown that rats can perform parametric comparisons of stimulus intensity and duration , tasks previously considered beyond rodent capability. The results demonstrate that rats accumulate evidence over time from stochastic stimuli, improving performance with longer stimulus durations, consistent with optimal evidence integration. The discussion distinguishes between two modes of whisker sensing: receptive sensing, where the animal holds its whiskers still to collect an externally delivered vibration, and generative sensing, where the animal actively creates stimulation through its own whisking movements. Diamond argues both are forms of active sensing, since even in the receptive case the animal actively controls whisker state to optimize signal collection. The conversation explores how rats and humans compare on psychometric performance , on average humans outperform rats, but the distributions overlap substantially, with the best rats exceeding the worst human subjects. A key finding is that stimulus intensity and duration combine through summation rather than multiplication, suggesting the brain adds rather than multiplies evidence from these two dimensions. The discussion also addresses why rats show higher lapse rates than humans , possibly reflecting an evolved strategy of continuously exploring whether task contingencies have changed, rather than exploiting a known rule. Diamond explains how these rodent studies complement primate research by revealing how a simpler brain with fewer cortical modules can accomplish similar computations through different circuit architectures. Key topics include parametric versus categorical decision-making, evidence accumulation in stochastic environments, cross-modal comparison between auditory and tactile stimuli, individual variability in rat cognition, and what working memory in rats reveals about prefrontal cortex homology. Part of the Convergent Science Network podcast series from the BCBT Summer School.

Tagged as:
Brain decision making Evidence Accumulation Rats Compare Sensing Stimulus Intensity whisker system

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

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00:00:03 - This is the Convergent Science Network podcast. Leading researchers in the domain
00:00:10 - of neuroscience, brain theory and technology are interviewed by Paul Verschure and Tony Prescott.
00:00:19 - This is Paul Verschure with the Convergent Science Network podcast together
00:00:23 - with my colleague Tony Prescott.
00:00:25 - And we're here with Matthew Diamond who was speaking at our BCBT Summer School 2015 edition.
00:00:31 - Matthew, you were talking about the whisker system in rats,
00:00:36 - but in some sense you give your analysis of the whisker system an expansion, if you want,
00:00:44 - into a domain that is in some sense counterintuitive when you look at people
00:00:49 - who do work on rodents because we've started to really look at pretty complicated
00:00:52 - decision-making tasks.
00:00:53 - Tasks so so how did you actually get
00:00:56 - from the study of the whisker system and its intricacies
00:00:59 - into this domain of of decision making well
00:01:04 - we it it took a number of steps and i should say that one of uh one of my colleagues
00:01:09 - in this field always tells me that we do behaviors that are far too complicated
00:01:13 - why do we do such difficult why do we train rats in such difficult tasks we
00:01:18 - We would do better with a go-no-go whatever,
00:01:22 - and there's some truth to that.
00:01:25 - But we got there because we're in a cognitive neuroscience department,
00:01:29 - and we've always wanted to study cognition, that is, thinking,
00:01:33 - decision-making, perception, all these things together.
00:01:40 - And when I started in neuroscience, my PhD and as a postdoc,
00:01:46 - there were issues that we wanted to study.
00:01:49 - People knew a long time ago in the 1990s,
00:01:53 - People knew what were the interesting properties of the brain,
00:01:56 - some of the most fascinating properties of the brain, the intriguing things
00:01:59 - like decision-making and perception, but there just weren't ways to do it.
00:02:03 - And so all of us would say to each other and to ourselves, what I really want
00:02:09 - to understand is perception and decision-making, but I can't.
00:02:13 - I don't have the methods, they aren't available, so let's anesthetize the rat
00:02:18 - and give a controlled hold stimulus and measure the response to the stimulus,
00:02:22 - but it was always a compromise.
00:02:23 - And then in the last 10 years, there's been a lot of progress in instrumentation and,
00:02:29 - neurophysiological methods and understanding of how to train rats that has allowed
00:02:36 - us actually to begin to explore the things that we knew for a long time we wanted to do,
00:02:41 - but we just couldn't get at them before.
00:02:44 - It's interesting that the training of animals is actually, some of it is a reinvention
00:02:49 - of the wheel, because a long time ago in experimental psychology.
00:02:55 - People did very, very interesting things with animals, going back to 1920s,
00:03:00 - 30s, 40s, and then neuroscience became much more fascinated with neurophysiology.
00:03:08 - Behavioral training was sort of put on the back burner and forgotten,
00:03:14 - and then in the last 10 years, There's been a rebirth of the attempts to study
00:03:20 - interesting behaviors in rats.
00:03:23 - Right. But now for the whisker system, you made this distinction between,
00:03:28 - let's say, active sensing and receptive sensing.
00:03:32 - So why do you think that's an important distinction to make?
00:03:35 - Well, I joined Tony years ago, five, ten years ago, in projects in which we
00:03:43 - focused on what we called active sensing.
00:03:46 - Ehud Aissar was also very important in this way of thinking. And.
00:03:51 - And because active, because the whisker system goes out to explore the world,
00:03:57 - it doesn't wait for things to happen.
00:03:59 - It makes things happen by a very deeply ingrained interaction between sensory
00:04:06 - systems and motor systems. and I bought into that and used that terminology.
00:04:12 - But when one is faced with active sensing, which usually means whisking,
00:04:17 - moving the head, moving the whiskers, and then we say, well,
00:04:22 - what if the animal's not moving the whiskers? What kind of sensing is that?
00:04:26 - And the natural terminology would be passive because it's contrary to active sensing.
00:04:33 - So if the animal is palpating a surface or moving around an arena and feeling
00:04:39 - the walls, and we say that's active sensing, then when it receives a vibration
00:04:43 - without movement, one would be left calling that passive sensing.
00:04:48 - And I didn't think that that was right. My students and postdocs and all of
00:04:52 - us in the laboratory observing the animals did not feel that they were in a passive state.
00:04:58 - We felt that they were actually actively controlling the whiskers in such a
00:05:03 - way as to collect a vibration from an external object.
00:05:06 - And so we refer to that now as receptive sensing.
00:05:10 - And when the animal creates the stimulus by its own movement,
00:05:16 - we call that generative sensing.
00:05:18 - And we believe that both are active.
00:05:21 - So there are two states of the system within the realm of active sensing.
00:05:27 - So there's still passive sensing as another possibility here where there's some
00:05:32 - unexpected stimulus on the whisker.
00:05:34 - Yes, but now from the perspective of the of the whisker system,
00:05:39 - how you use it, aren't you always operating in a mixed mode and isn't there
00:05:43 - to make that clean distinction isn't that only holding in the laboratory and
00:05:48 - it will be more difficult to maintain that under under real world conditions?
00:05:53 - Well, we just don't know enough about real world rats.
00:05:58 - We don't know enough about their natural history, what they do,
00:06:01 - how they do it, how they live, how they move.
00:06:04 - I hope that new research programs will begin on that.
00:06:09 - But as of now, we just don't know what they really do with their sensory systems.
00:06:16 - I think that there may be, as Tony said,
00:06:20 - something that is passive sensing, which means that the animal doesn't know
00:06:24 - what to expect and receives the stimulus that happens without being able to
00:06:31 - prepare the system for what's going to happen because they don't know.
00:06:35 - So for instance, an unexpected arrival of a predator or an unexpected stimulus could be
00:06:44 - processed by a passive sensory system simply because the rat is not able to
00:06:50 - actively set the state of processing.
00:06:55 - So then, let's assume we just sort of, we have the sensor system worked out.
00:07:01 - These rats are really great in using their whisker system for different kinds of discriminations.
00:07:07 - But now you're going to use this modality in actually a pretty intricate task, right?
00:07:14 - Where also you described a number of stages that you want to manipulate,
00:07:18 - which starts controlling, let's say, the attention of the animal to the upcoming stimulus.
00:07:23 - Then there will be a first stimulus, this is a vibration on the whiskers that
00:07:29 - must be encoded. Then there will be a pause.
00:07:31 - So now we are dealing with the working memory task.
00:07:35 - Second stimulus. Then there's a comparison, person, um, a subsequent delay,
00:07:42 - uh, before the action can be executed, then we get the go signal.
00:07:45 - The animal can, can choose left or right to get a reward.
00:07:48 - Yes. So if you just would look at this protocol and you would say,
00:07:53 - look, I'm going to train my rats to do this.
00:07:56 - Uh, I think most people would say, look, look, uh, Matthew, you're mad.
00:08:00 - This is just too complex.
00:08:01 - So, so how much time does it take to get the rat to be really sort of.
00:08:07 - Sufficiently capable of performing such a task.
00:08:13 - Well, there are different questions that you can ask the animal to do in the context of this task.
00:08:21 - But I'd say that for the first level questions, you can get answers with about two months of training.
00:08:29 - If you want to introduce new variations or new variables, it can take as long as three months.
00:08:37 - And it varies from rat to rat. Some are very clever and learn very quickly,
00:08:40 - and some are slower. and we tend to be patient with the slow rats and try to
00:08:46 - train them until the end rather than discarding them. So we see differences between rats.
00:08:52 - But I think it's worthwhile for a number of reasons.
00:08:56 - First of all, just the fact that the rat can do this is informative because,
00:09:01 - as I mentioned during the talk, this is, compared to the primate brain, it's a very small brain.
00:09:08 - It has many fewer modules in the cerebral cortex. So the question is,
00:09:12 - having available a smaller brain and a simpler brain in some way,
00:09:18 - are there things that the rat simply can't do?
00:09:21 - And what can it do?
00:09:24 - And so we're sorting this out. There are a number of laboratories that have
00:09:28 - progressively, in the last five to ten years, introduced into the world of rats
00:09:34 - a series of primate tasks.
00:09:36 - In fact, there was an article in Nature about this three or four years ago,
00:09:40 - I think they called them the rat pack.
00:09:43 - And there was the belief in the field of systems neuroscience,
00:09:50 - cognitive neuroscience, that rats simply couldn't do them.
00:09:53 - But it turns out that it takes a
00:09:56 - lot of training of the research team in order
00:09:59 - to learn how to train the rats so we we train ourselves
00:10:02 - and we learn we learn how to train the
00:10:04 - rats and it's a question of finding the right methods the right the right apparatus
00:10:10 - and once that's done the rats can do very primate like things even the visual
00:10:16 - system we all know that primate visual processing is extremely advanced,
00:10:23 - and we're not saying that rats can have visual perception up to the level of primates.
00:10:30 - But there are some properties of visual processing that five years ago people
00:10:34 - didn't believe that rats have, and it turns out they do have them.
00:10:38 - I'm referring to invariance according to position, according to size,
00:10:44 - according to viewing angle.
00:10:47 - My colleague at CISA, David A. Zocolan has found that rats do have these invariance
00:10:53 - capacities, and people thought they were a property of primates.
00:10:57 - So if you put the time and effort into...
00:11:02 - Finding the right kind of stimuli, the right kind of training apparatus,
00:11:05 - the right training regime.
00:11:06 - Rats can do primate-like things, and this alone tells us something.
00:11:12 - Is this really to use rats as a substitute for primates in brain research,
00:11:19 - or is there some other reason to do this?
00:11:22 - Well, I think that that's one of the outcomes, is to be able to do certain kinds
00:11:28 - of cognitive neuroscience, perceptual neuroscience, in rats and not have to rely on primates.
00:11:34 - That's certainly one benefit of this, but it's not the only reason for doing it.
00:11:39 - Even if there were no constraint on using primates, it's interesting to know
00:11:43 - what rats can do, because they do it with a very different organization of the brain.
00:11:48 - And the fact that a complex task, a primate-like task, can be done with a very
00:11:54 - different brain structure, tells us something about the way the brain works.
00:11:58 - It tells us that the brain, in particular the cerebral cortex,
00:12:03 - can find solutions to doing certain kinds of computations, but in a different way.
00:12:08 - And discovering how the rat brain can accomplish this actually tells us a lot
00:12:14 - about overall brain organization.
00:12:16 - So I'm a bit surprised you say that, because as in part of the argument you'd
00:12:21 - want to make is that though the rat brain is simpler than the primate brain,
00:12:25 - There are some fundamental mechanisms in tasks like decision-making which are probably similar,
00:12:32 - and the neural substrates may even have very interesting similarities.
00:12:37 - Yes, they may. The point is that these mechanisms can be installed in a circuit
00:12:47 - with fewer neurons and with fewer modules,
00:12:49 - and yet somehow the same computation can be accomplished.
00:12:53 - For instance, in what I talked about today, there's a very clear working memory component.
00:13:00 - And if you use the word working memory with systems neuroscientists,
00:13:06 - they immediately think of prefrontal cortex.
00:13:09 - Now, rats may have a prefrontal cortex.
00:13:12 - They probably do, but nobody's absolutely certain what region it is.
00:13:16 - It's very hard to find an analogy or homology between rat prefrontal cortex
00:13:22 - and primate prefrontal cortex.
00:13:24 - Some people believe that the rat homology to prefrontal cortex is a prelimbic
00:13:32 - area along the medial wall of the cortex, but nobody's really sure.
00:13:36 - And yet, with this...
00:13:39 - Possessing or maybe not possessing a prefrontal cortex rats can do uh can carry
00:13:46 - out working memory as well as primates nearly as well as primates and and and and so they're somehow,
00:13:53 - getting these computations done with a different brain structure and that's
00:13:57 - it's interesting to know how they do this but i think your data but maybe we
00:14:04 - should go through that first but i think you're the physiology that you have
00:14:07 - gathered on this task is actually also partially answering that question,
00:14:12 - whether it is that different or whether there are similarities.
00:14:16 - But maybe we can come back to that once we understood the task a little bit better.
00:14:20 - So what now happens is that the animal is exposed to two stimuli.
00:14:29 - These stimuli are a certain velocity of movement of this plate that you manipulate.
00:14:35 - Calculate and the variance of that velocity you control.
00:14:40 - So that means if you want the overall amplitude of the signal you're integrating
00:14:46 - is now controlled, it's different, right?
00:14:49 - But the second parameter you control of your stimulus is now the duration of the stimulus.
00:14:54 - Yes. And the question now is, okay, to what extent can the animal make this
00:15:00 - discrimination and say, oh, this was shorter or longer? Like stimulus two was
00:15:04 - shorter or longer than stimulus one.
00:15:07 - And this informs me whether I should go left or right to get my reward.
00:15:11 - And the reward, there's a liquid reward. Animals are water deprived or it's…
00:15:15 - Yes, it's actually fruit juice.
00:15:17 - Okay. So they're not food or water deprived. They have their water restricted.
00:15:23 - They drink a lot during the task because in a few hundred trials,
00:15:29 - they get almost as much liquid as a rat in its home cage would have in the course of a day.
00:15:36 - Then after the training session, they also have another hour to top up if they feel like it,
00:15:42 - And then they're restricted until the next session.
00:15:46 - So they arrive in the session thirsty, but certainly not in any sense in physiological stress.
00:15:54 - Right. So now we have this parametric control of our stimulus.
00:15:59 - And you made the point that this actually is a qualitatively different task
00:16:03 - than when we have to do some sort of categorical decision making.
00:16:07 - Let's say I show you Tony and then I show you a car and then you can go left or right.
00:16:13 - So what is the principal difference in your mind, certainly from the perspective
00:16:20 - of the red brain, between this categorical decision making and this more parametric decision making?
00:16:27 - Okay. In the parametric decision making, we actually presented two kinds of behavioral paradigms.
00:16:34 - One is in which the rat or the human subjects, we also study humans,
00:16:40 - has to compare this intensity value that you talked about, this variance.
00:16:45 - And in that case, they should measure the physical characteristics of the stimulus,
00:16:50 - but optimally, they should not attend to the duration of the stimulus.
00:16:54 - Then, as a second task, we ask animals and human subjects to judge the duration
00:17:02 - of the stimulus while not attending to the intensity value.
00:17:09 - In both these cases, the comparison is, as you say, a parametric one,
00:17:16 - which means that that every stimulus is distributed along the same dimension
00:17:23 - and vary only in their location along that dimension.
00:17:26 - So we can draw an axis, which we could call variance of the stimulus,
00:17:31 - or intensity or amplitude, we could use different terms, but we can draw an
00:17:35 - axis, and then every stimulus simply has one position along this axis.
00:17:40 - And the object of the brain then is to find the relative position of two stimuli to compare them.
00:17:47 - This is parametric because there's one parameter that defines a stimulus.
00:17:51 - In the case of categorical stimulus comparison.
00:17:55 - Which ecologically is hugely important, but it's a different kind of strategy
00:18:02 - for the experimentalist because the stimuli that have to be compared,
00:18:07 - let's say, living thing in a visual system, in a visual task,
00:18:11 - you could ask the subject,
00:18:12 - do you see a living object or not see a living object?
00:18:15 - And you could show an image for a half second.
00:18:18 - That's an example of a categorical task.
00:18:21 - The stimuli that have to be processed may engage different sets of neurons.
00:18:30 - So the comparison could be between one stimulus that evokes activity in one
00:18:35 - set of neurons, a second stimulus that evokes activity in a second set of neurons,
00:18:41 - and eventually the brain of course
00:18:44 - has to converge the two populations in order to make the comparison.
00:18:47 - But at this stage of our understanding, we don't know in categorical tasks which
00:18:54 - we can't identify exactly the sets of neurons, and we don't know where they converge.
00:18:59 - And so we selected a task in which we could actually study the coding,
00:19:04 - the computations done by neurons in that the exact same set of neurons encodes both stimuli.
00:19:11 - Yeah, but the difference now is, of course, that the neurons that we're going
00:19:15 - to look at, are in some sense forming an analog representation of the stimulus itself.
00:19:20 - Yes. One of the categorical case that would not be the case.
00:19:24 - Well, a categorical representation still has to be built on a preceding parametrically controlled one.
00:19:31 - So is it possible that you are then looking at a lower, let's say,
00:19:37 - processing step in the hierarchy than what would play out when we have categorical decision making?
00:19:43 - Or you think that in this in this part of the rat brain where you're looking.
00:19:48 - That you would also find categorical representations if you knew what to look for? So we don't know.
00:19:55 - I think rats can make, for sure, they can make categorical decisions.
00:20:00 - For example, there's very systematic studies in the olfactory system where rats compare odors.
00:20:08 - These I would consider to be categorical because the odors are different from
00:20:15 - each other. They engage different receptors.
00:20:17 - They create activity in different populations of neurons.
00:20:20 - And then at some stage, perhaps in the olfactory cortex, we're not sure where,
00:20:24 - there's convergence and a decision is made.
00:20:30 - So rats can do this. We've explored in rats and we've had some success.
00:20:37 - We've also done a large set of studies in humans that I didn't present today
00:20:43 - in which the comparison is actually made between modalities.
00:20:46 - So we can have an acoustic stimulus that has some value along a dimension,
00:20:53 - an intensity dimension if you wish, and a tactile stimulus that has another
00:20:58 - value along a dimension,
00:21:01 - and then the subject has to compare the two stimuli in different modalities.
00:21:05 - So the first thing they have to do in order to do this task successfully is
00:21:10 - create some scale whereupon stimuli in different modalities can both be projected.
00:21:18 - So you have to, for instance, if you want to, you could call the scale from 1 to 10.
00:21:23 - So the subject has to learn to put an auditory stimulus along the scale of 1
00:21:27 - to 10, tactile stimulus along the scale of 1 to 10, and then make a comparison.
00:21:32 - Humans can do this. With a lot of work, we got some rats to do this,
00:21:36 - but it's very difficult for them.
00:21:37 - And the rats actually had good days and bad days, depending on how much effort
00:21:45 - they wanted to put into it.
00:21:46 - So it's very hard for rats. It's quite a challenge for humans, but humans can do it.
00:21:52 - So that actually requires, to do the task requires a convergence between the
00:22:00 - auditory system and the tactile system, and a human subject can do this. Right.
00:22:06 - So now, if we look at the stimuli you're using, so these parametric stimuli.
00:22:11 - What does, let's say, the discrimination threshold at which,
00:22:16 - and also the just noticeable difference at which rats operate.
00:22:19 - So how big a difference can they really distinguish between these different stimuli?
00:22:26 - Well, that's a great question. I don't recall that we've actually looked at our stimuli.
00:22:36 - We haven't quantified it exactly in terms of just noticeable difference.
00:22:41 - I think that we could do that computation.
00:22:44 - We haven't. But I think it would require examining
00:22:48 - the psychometric curve and then making
00:22:51 - a model for how far along the psychometric
00:22:55 - curve how different to stimuli would have to be
00:22:57 - to produce a different a measurably different choice in the animal and we have
00:23:06 - the data available we have not made exactly that population okay so now if we
00:23:13 - if we talk about a psychometric curve
00:23:15 - that you defined or extracted from the performance of both rats and humans.
00:23:22 - They're sort of similar, but certainly not identical as far as I got your data.
00:23:28 - So what are the important differences here between the psychometric curve that
00:23:34 - maps the properties of the stimulus to rat performance versus those you find for humans?
00:23:41 - Well, as you saw, the rats can perform the task.
00:23:47 - The average, if we take a set of rats and a set of humans,
00:23:52 - the average value for the human is better than the average value for the rats,
00:23:56 - measured in either as percent correct or as a more derivative measurement is
00:24:03 - the slope of the psychometric curve.
00:24:04 - And these are two standard measures of performance.
00:24:08 - On average, rats are inferior to humans, but there's individual variability
00:24:16 - in both species, in humans and rats.
00:24:21 - And this is one of the many, many interesting things.
00:24:24 - Funny things that we find in rat behavior and rat psychophysics. If you talk to a typical.
00:24:33 - Psychologist, a human psychologist, their impression would be that humans differ
00:24:40 - very much from each other, but a laboratory animal like a rat, they're all the same.
00:24:45 - It turns out that rats vary among the individual variability is as much or more than that in humans.
00:24:52 - So a good rat in our data set, a rat that performs really well,
00:24:57 - is actually better than some of the humans that perform less well.
00:25:02 - So on average the humans are better, but there's very significant overlap in
00:25:07 - the two clouds of performance.
00:25:10 - Wouldn't you expect the rats to have less variability because there's less general
00:25:16 - genetic variability in the population of lab rats?
00:25:19 - That would be our expectation. Our rats are sort of outbred so there is quite a bit of variability.
00:25:26 - And we have completely different behavioral procedures where we confirm a lot
00:25:32 - of variability between rats, but they're not genetically, they're not completely inbred.
00:25:38 - But now in some sense we always make these normative judgments,
00:25:41 - right? We see these psychometric curves, which means we're making assumptions about optimality.
00:25:46 - And it's not not unreasonable to assume that rat brains have been optimized
00:25:51 - for somewhat different, to satisfy somewhat different constraints than human brains.
00:25:55 - So in that sense, the biases and the priors in the rat brain might already be
00:26:00 - different from the human brain and maybe given those priors,
00:26:02 - the rat is behaving optimally.
00:26:04 - So what do you see as the most significant differences in the biases that the
00:26:10 - human brings to bear to the task and that the rat brings to bear on the task?
00:26:14 - Well, the rate of learning is, of course, radically different.
00:26:21 - In the case of human subjects, we have a training session in which they are
00:26:28 - presented with the stimuli in the task, and they make a choice,
00:26:30 - and the computer tells them if they are correct or incorrect.
00:26:34 - We don't give them a verbal instruction to tell them what to extract from the
00:26:39 - data. We don't tell them, this is what you're going to feel.
00:26:42 - This is a parameter of the stimulus that you have to extract and then you have
00:26:46 - to compare this parameter in the first to the second.
00:26:48 - We don't do that because we think it would create lots of biases and lots of
00:26:52 - top-down processes and too much thinking.
00:26:57 - So instead we simply let them feel the stimuli, they receive the stimuli,
00:27:03 - they make a choice and the computer says correct or incorrect.
00:27:07 - So So during the training session, the human subjects are in fact testing possible
00:27:14 - hypotheses about what the rule might be, but we don't tell them the rule.
00:27:18 - Then, trying various rules, we're not sure exactly what the mental rules that
00:27:25 - they test are, but trying various ones,
00:27:29 - they eventually arrive at a choice based on the thing that the computer actually
00:27:37 - employs as the rule, and they start getting it right.
00:27:40 - And so when we see them get about 80% right in blocks of 10,
00:27:46 - then we judge them as having figured out the task and the training is over and
00:27:51 - they can move to testing.
00:27:52 - So in humans, this can take 10, 20, 30 minutes.
00:27:56 - In rats, the training takes, as we said before, one or two months.
00:28:02 - They have to go through multiple stages of training, beginning with simply letting them explore the box.
00:28:12 - Then they have to learn that they have to go into a specific sector of the box
00:28:19 - and put their nose in a specific place.
00:28:21 - We can't tell them to do that the way we can tell human subjects to hold their finger here.
00:28:26 - We just have to train the rats to do that, and each of these takes a week or
00:28:31 - two. So when you get through all the stages, two months have passed.
00:28:35 - So that's a major difference in the training.
00:28:38 - In the performance, one difference you noticed is that.
00:28:42 - On very easy stimuli, stimuli that we know that the sensory system can process correctly.
00:28:48 - Humans can perform at 95% or even 100%. Rats, even for very easy stimuli,
00:28:55 - will make the wrong choice 10 or 15% of the time.
00:28:59 - And this is something that psychologists sometimes call lapse rate,
00:29:02 - which means errors that are not due to sensory processing, because we think
00:29:07 - that the sensory system really can handle those trials.
00:29:10 - So the lapse rate is something that distinguishes rats from humans,
00:29:15 - and there are many theories about why rats make lapses.
00:29:19 - One of the most interesting theories is that when they make this kind of error,
00:29:24 - what they're actually doing is making sure that the rule has not changed.
00:29:29 - That is, if they go to the opposite side to where they know they'll get the
00:29:33 - reward, that if they go the opposite side, they want to confirm that there There
00:29:37 - is, in fact, not a reward there.
00:29:39 - So it means rats might be more tuned to exploring the task space than to keep
00:29:43 - on exploring the task space because they might have evolved for environments
00:29:46 - where the contingencies are less stable as humans have.
00:29:49 - Absolutely. I think humans, if they discover that there's a rule,
00:29:54 - then they are willing to act according to this rule until they have evidence that it doesn't work.
00:30:00 - Whereas rats are much more prone to exploring the environment because perhaps
00:30:07 - they've evolved to believe that environments can change, that rules do change,
00:30:11 - and they want to make sure that something hasn't changed.
00:30:14 - Do you also believe that based on that rats would operate at a different level
00:30:19 - of, let's say, anxiety and certainty than humans?
00:30:24 - Possibly. I mean, they may be more anxious on the basis that they're working
00:30:30 - for their daily water. they get as much as they need.
00:30:36 - If they don't get enough liquid during the testing session, then they can drink
00:30:44 - for a couple of hours afterwards.
00:30:46 - They're not deprived of water in any way, but they know it's something that's
00:30:51 - important to them and they want to get it right.
00:30:54 - And surprisingly, humans also want to get it right, but for different reasons.
00:30:59 - They don't want to disappoint you, I think. Yes, and most of the subjects are
00:31:04 - students, and I think that they want to be better than the other students.
00:31:07 - I think there's some rivalry.
00:31:09 - Right. So we have these psychometric curves now for rats and humans.
00:31:15 - We know rats are slightly worse than humans, but not dramatically so.
00:31:19 - The overall psychometric curve is a bit flatter than in the human case.
00:31:24 - Also, the extremes are a bit more compressed as far as I could tell.
00:31:28 - Yes. So, that basically means we know now the rat can perform the task.
00:31:33 - Yes. So, now we want to know, okay...
00:31:35 - How do then these two properties of the stimulus that you're manipulating,
00:31:40 - which is intensity and duration, affect task performance, right?
00:31:44 - So what can you say about that relationship?
00:31:47 - Well, the parameter that we first explored was the perception of the value of
00:31:53 - intensity, the parameter of intensity.
00:31:55 - But we arranged the experiment in such a way that intensity is not a value that
00:32:04 - emerges instantaneously from the stimulus.
00:32:06 - It's a stochastic stimulus. It's a vibration, but a vibration which is created
00:32:12 - by sampling a normal distribution, so you could call it modulated noise in a way.
00:32:18 - So the effect of this is that at any given instant, there's not really a complete
00:32:26 - signal available to the sensory system in order to make a choice.
00:32:29 - Instead, the information necessary to make a choice, to make the judgment, accumulates over time.
00:32:37 - That's the nature of a stochastic stimulus.
00:32:40 - Stochastic stimulus is defined by probabilities,
00:32:45 - by statistical distribution, and with an increasing number of samples,
00:32:50 - the statistical structure becomes more evident to the brain,
00:32:55 - to whoever sampling the stimuli.
00:32:57 - So from this, our prediction is that if the brain is is actually accumulating evidence.
00:33:05 - And using the history of events during the stimulus, then the performance would
00:33:11 - be better with a longer duration.
00:33:13 - And this is what we found in both in rats and in humans.
00:33:18 - You said that there was some evidence that maybe rats wouldn't be good at accumulation in that way.
00:33:26 - Yes, there have been other perceptual tasks that have been explored in rats,
00:33:32 - uh change detection kinds of tasks in
00:33:35 - which the there's been evidence that under some conditions rats do not use a
00:33:41 - long history of of tactile stimulation in order to make the choice but use only
00:33:47 - very recent history and apparently this depends on the uh the way that the animal,
00:33:54 - what it decides to optimize in the task so is it possible that that your results
00:34:00 - are consistent with those other findings because your task emphasizes you can't
00:34:05 - solve your task without evidence accumulation.
00:34:08 - Yes, it's true that the task was actually set up in such a way as to reward
00:34:16 - evidence accumulation.
00:34:18 - It was set up with a statistical structure such that performance is better if
00:34:24 - evidence is accumulated.
00:34:26 - But that doesn't necessarily impose on an organism to do it.
00:34:32 - An organism can do the task without evidence accumulation. stimulation,
00:34:37 - it would simply have not good performance.
00:34:40 - But then what you showed, which was very surprising, is that this integration
00:34:46 - process of the information, the evidence provided to the animal,
00:34:49 - is following a summation rule and not so much an integration process, like a multiplication.
00:34:55 - And this had to do with an interaction effect between intensity and the duration of the stimulus.
00:35:02 - Yes. So how did that exactly play out?
00:35:06 - Well, the task involves comparison of two stimuli, and the two parameters that
00:35:11 - we can control are the intensity value, which was the variance in the vibration,
00:35:18 - the higher variance is perceived as being more intense, and the duration of the stimulus.
00:35:23 - Of course, there are many variables in the experiment that we didn't talk about today.
00:35:28 - For instance, the delay between the first and second stimulus is extremely interesting
00:35:32 - because a longer delay puts a heavier load on working memory.
00:35:37 - Today, instead, we focused on the parameters of intensity and duration.
00:35:42 - And our first question was whether performance in humans and rats or in rats
00:35:51 - would vary according to the duration of the stimulus.
00:35:54 - And as we were saying, the statistical structure is such that an ideal observer,
00:36:00 - a machine doing the task with all available data, would in fact do better with a longer stimulus.
00:36:07 - And we found that that is the case under the conditions in which the first and
00:36:14 - second stimulus were of equal duration.
00:36:18 - We then varied the duration, but included trials trials in which the first and
00:36:26 - second stimulus were not of equal duration, which we call the unbalanced condition.
00:36:31 - And in the unbalanced condition, we found, to our surprise, that the subjects,
00:36:38 - both humans and rats, did not measure intensity as an average over time,
00:36:44 - but instead with some form of summation,
00:36:47 - as we saw, not a linear summation,
00:36:50 - but with some form of summation that caused a longer stimulus to be perceived as stronger.
00:36:56 - So it was not purely an averaging, but some form of summation.
00:37:03 - But do you see this now as, let's say, I have to estimate, you could say I can
00:37:06 - estimate these two parameters of duration and intensity.
00:37:11 - But now if I would integrate over these parameters separately,
00:37:15 - then duration might give me, let's say, a false sense of intensity.
00:37:21 - Because imagine I just have, let's say, a clock and I'm just integrating from
00:37:25 - this clock longer, so my signal gets higher. and that might then start to have
00:37:28 - a crosstalk, adding noise, if you want, to my intensity estimate.
00:37:34 - So do you see it in those terms, like a crosstalk between informational channels?
00:37:39 - Or do you see it more as an intrinsic bias that the system has? It's an intrinsic bias.
00:37:47 - It's not that longer stimulus adds noise. In fact, the longer stimulus reduces
00:37:50 - noise in the sense that subjects perform better if the two stimuli to be compared
00:37:57 - last 600 milliseconds each,
00:38:00 - as opposed to if the two stimuli last 100 milliseconds each.
00:38:06 - So the longer stimulus doesn't add noise, it actually adds precision to the
00:38:11 - judgment of the statistical properties.
00:38:15 - The problem that I think the the perceptual system has, which it is not able
00:38:22 - to overcome, is that it doesn't have an absolute clock.
00:38:27 - If the brain had an absolute clock, then it could measure the total activity
00:38:32 - occurring over stimulus and then divide by this absolute value.
00:38:36 - But it's not available in the brain. And for some reason it's it's not there
00:38:43 - or it's not used and and so um and so not knowing exactly how much,
00:38:49 - to divide the the the accumulated value
00:38:53 - by there's a bias such that the longer stimulus
00:38:56 - feels stronger and this this effect you
00:38:59 - see as a linear effect so if i double the time then this subjective sense of
00:39:04 - intensity is also doubled or it follows an exponential curve as you should say
00:39:08 - grammatically or could you imagine that at the level of neural substrate it
00:39:13 - might be let's say more discretized or it might have different sensitivities
00:39:16 - because it has to write for instance on oscillatory activity,
00:39:20 - yeah we don't know in exactly that level of detail we built a model that simulates behavior.
00:39:29 - Correctly and accurately when the summation was done not linearly but exponentially
00:39:35 - with an exponentially decreasing weighting function so that the onset of the
00:39:40 - stimulus contributes to the perceived value with the highest weight,
00:39:45 - and then that weight decreases exponentially with a time constant of about 150 to 200 milliseconds.
00:39:53 - And then we compared that to the expected results if the,
00:40:02 - if the weight of the stimulus increased over time so that the end of the stimulus
00:40:07 - had more weight, and we found that the results were consistent with an exponentially
00:40:13 - decreasing weighting function, not increasing.
00:40:17 - Right. So in some sense, you put now the process that would account for this
00:40:23 - bias at really the sensor processing end of the pipeline.
00:40:26 - Well, in between, we still have our working memory buffer. We have another integration
00:40:30 - stage that has to come to the decision.
00:40:34 - So why do you put the full burden of that bias at the perceptual end?
00:40:39 - It might also be, let's say, some sort of capacity issue in your working memory, for instance, right?
00:40:46 - So we don't know where this waiting function occurs,
00:40:51 - but we've seen that if we look at the activity of sensory cortex,
00:40:55 - primary sensory cortex, which in rats is for the whisker area,
00:40:59 - this is called a barrel cortex,
00:41:02 - the activity of neurons there does not reflect a weighting function.
00:41:08 - So if we try to decode the choice of the rat based on the activity in the sensory
00:41:16 - cortex, the choices will not reflect the duration of the stimuli.
00:41:24 - So the effect of the duration must occur after sensory cortex.
00:41:29 - Another region that we're looking at is premotor cortex, which is in front of sensory cortex.
00:41:36 - And there we find many neurons that have activity during the time between the
00:41:44 - first and second stimulus, where the activity has a very clear relation to the first stimulus.
00:41:49 - So at first glance, one interprets these as working memory neurons.
00:41:54 - Many laboratories have found neurons like these.
00:41:57 - The ROMA laboratory in Mexico City has found many neurons like this in exploration of primate cortex.
00:42:04 - And the interpretation is that these are neurons involved in working memory.
00:42:12 - Now, when we look at how in rats the working memory neurons encode the first
00:42:17 - stimulus, we find that the firing rate has a stronger,
00:42:23 - better statistical correlation with the first stimulus if we consider the first
00:42:31 - stimulus as the perceived value of that stimulus rather than the actual physical
00:42:38 - property of the stimulus.
00:42:39 - In other words, we find that the activity of a neuron that varies according
00:42:44 - to first stimulus intensity.
00:42:47 - Will fire more if the first stimulus lasts longer, even if the intensity during
00:42:55 - that stimulus is constant.
00:42:57 - So as the stimulus continues over time.
00:43:03 - We know from a lot of behavioral work that as the stimulus continues over time,
00:43:08 - the perceived intensity increases.
00:43:11 - This came out from the psychometric curves.
00:43:14 - At the same time, we find that neurons in premotor cortex, if the firing is
00:43:19 - positively correlated with the stimulus intensity, then those same neurons fire
00:43:25 - more when the stimulus lasts longer.
00:43:27 - So the firing rate actually reflects the stimulus duration. so the firing rate
00:43:34 - in premotor cortex reflects the perceived value of the stimulus rather than
00:43:39 - the average value of the stimulus.
00:43:41 - Do you do a control where the intensity is not varying randomly within a given
00:43:50 - range but is fixed and so you get a continuous stimulus,
00:43:55 - at that right target frequency?
00:44:01 - So two stimuli are being compared and… So rather than getting a noisy stimulus
00:44:08 - with an average intensity,
00:44:09 - you would just have the stimulus continuously at the target value.
00:44:15 - That would be an easier task.
00:44:16 - Something like a sinusoid, for instance.
00:44:19 - No, we have not used sinusoidal stimuli in this stage of the experiment.
00:44:25 - We did in some very preliminary pilot studies and found that rats had a hard
00:44:33 - time maintaining attention for pulse trains and for sinusoids.
00:44:39 - That is, it was more difficult to convince the rat to remain in the nose poke,
00:44:46 - to remain immobile bowel with its whiskers in contact with the plate.
00:44:50 - For the first stimulus, the interstimulus delay, and the second stimulus,
00:44:53 - to wait for the go cue when the stimuli were sinusoidal.
00:44:58 - They tended to abort the trial, that is to leave early.
00:45:01 - So to put it in layman's terms.
00:45:06 - Periodic or regular or constant stimuli are simply not interesting.
00:45:12 - And for some reason, we're not sure why.
00:45:15 - And the noisy stimuli are interesting. The rat is very willing to wait for the entire stimulus.
00:45:21 - We can make the stimulus, if we want, even one second long, two seconds long, and they wait.
00:45:27 - That's interesting. So one thing that occurred to me is that you have a task
00:45:31 - in which the stimulus is varying along two parameters, intensity and duration.
00:45:36 - Um and but success in
00:45:40 - the task requires that you just attend to intensity
00:45:43 - and yes you could you ignore duration yes it's uh
00:45:46 - because it's is put there as a kind of confound
00:45:49 - yes um and your model
00:45:52 - is assuming that they are more or less successful in attending to the intensity
00:45:59 - but presumably i mean neither the rat nor the human knows that this rule is
00:46:04 - just about intensity So they might be working on all sorts of theories about
00:46:08 - mixtures of intensity and duration.
00:46:11 - And if you give either duration or intensity on their own, then they can solve those problems too.
00:46:18 - So I'm just wondering, in the model space that you've explored,
00:46:22 - have you explored all the potential variants of rules that the animals could
00:46:29 - be using to try and maximize performance on this task?
00:46:32 - Are there rules where you might do reasonably well by taking the duration into account in some way?
00:46:45 - Well, we put a reward rule into the computer, and that's applied to the rat
00:46:54 - or to the human during the experiment.
00:46:56 - And the reward rule, if we do an intensity experiment, the reward rule is intensity.
00:47:02 - That's what has to be compared. Or we can, in different routes or different human subjects,
00:47:08 - we can change the rule such that the subject is rewarded according to duration
00:47:14 - and to perform perfectly should ignore intensity.
00:47:20 - Now, if we consider the first case when the rule is intensity,
00:47:25 - there are many, many trials in which the two stimuli have the same duration
00:47:32 - and vary only by intensity.
00:47:35 - So an example would be a 400 millisecond stimulus followed by 400, or 600, 600, 200, 200.
00:47:42 - In all those cases, no choice could be made based on duration.
00:47:49 - In fact, the rats and the humans perform very well according to the intensity rule.
00:47:55 - So we have no reason to think that when they're actually doing the intensity
00:48:01 - task, that they think that they might be doing a time task.
00:48:07 - They know that when they're doing intensity, they're doing intensity.
00:48:11 - The problem that they have is that when they try to accomplish the intensity
00:48:16 - task, ask the sensory system.
00:48:20 - Is confounded by the duration. Well, that's your reading of what they're doing.
00:48:25 - I mean, is your assumption there that intensity is more salient to them than duration?
00:48:31 - No, because we can train rats and we can train humans to do duration.
00:48:36 - The question is the reward rule. In fact, there's actually no requirement that
00:48:45 - we use a different stimulus set.
00:48:47 - If we have two parameters to be varied, the intensity and the time duration,
00:48:55 - we can have stimulus pairs that differ in one or the other, and the subject
00:49:02 - is simply rewarded for making a choice based on one parameter and not the other,
00:49:05 - or the second parameter and not the first.
00:49:08 - So we don't even have to change the stimuli. We just change the rule and we
00:49:13 - find that a subject trained according to one rule makes its choice based on that rule,
00:49:18 - and a subject trained on the other rule makes its choice based on that rule.
00:49:25 - You still have some kind of interaction effect because with the intensity the
00:49:28 - longer stimuli are easier to classify.
00:49:32 - So this makes your task rather different from a lot of more standard judgment tasks of that kind.
00:49:40 - Right. So for the longer stimulus, there is more signal available for making
00:49:46 - the discrimination, and we found
00:49:47 - that in fact the discriminations are made better both by humans and rats.
00:49:51 - And this confirms work done by the ROMA laboratory with vibrations applied to
00:49:57 - the finger of monkeys, keys, and also in visual system, detecting the direction of noisy moving dots,
00:50:05 - dots which move in a coherent or incoherent way, performance is better for longer stimuli.
00:50:11 - So we've simply confirmed that for stimuli in which.
00:50:17 - More information, more signal is available in a longer time,
00:50:22 - even to the ideal observer.
00:50:24 - There's simply more information available that in fact rats and humans perform better.
00:50:29 - So that's a confirmation of many, many different studies.
00:50:33 - And the confound between time and intensity was something that emerged from our experiments.
00:50:43 - Then we went back in literature and looked for this and found that there have
00:50:49 - been reports consistent with this.
00:50:51 - And are you able to look at the sort of learning history and infer from that
00:50:57 - anything about perhaps the strategy, certainly in humans, you could imagine
00:51:04 - you have a strategy of testing various hypotheses.
00:51:07 - But even in the rat, you might imagine that there was some pattern of of changes
00:51:13 - in the trials where at one stage they have no idea and perhaps another stage
00:51:18 - they're testing that it's about intensity,
00:51:21 - these kinds of things. Is there anything to see from that?
00:51:24 - Yes. During training, rats, of course, have very low performance as they're
00:51:30 - learning, but they make a choice.
00:51:33 - And they might very well have some rule in mind,
00:51:39 - some algorithm, algorithm that they're by which they're acting and it's it's
00:51:43 - not working or it may be by chance works 60 or 65 percent of the time and they
00:51:48 - think that that's fine so so we have to simply.
00:51:55 - Simply train them day after day and if they if they had in mind a rule that
00:52:03 - was not the the one that the computer is set up to use,
00:52:07 - then sooner or later they'll discover that it's not reliably giving them the reward.
00:52:13 - So you made a model to explain the performance or the mapping of the stimulus
00:52:18 - properties to the performance.
00:52:20 - And in there, you see that you have this very fixed time constant at which you
00:52:24 - are ramping the sensory evidence that comes in.
00:52:26 - You just mentioned 150 milliseconds or something like that.
00:52:30 - Do you see this really as an invariant that is sort of wired into the human and the red brain?
00:52:35 - Or this is also in turn dependent on task properties?
00:52:40 - That's a really interesting question. And we've been able to,
00:52:46 - from the simulation, the model, we've been able to extract this time constant
00:52:50 - tau for a number of rats, a number of humans.
00:52:53 - And to our surprise, and I think probably it would be surprising to many colleagues,
00:53:01 - the tau, the time constant, was actually slightly longer in rats than in humans.
00:53:06 - About in the order of 150 to 175 milliseconds for rats and about 100 to 150
00:53:12 - milliseconds for humans.
00:53:15 - So while many of us would have predicted that humans accumulate evidence over
00:53:21 - a longer history, it turns out that rats accumulate evidence over a longer history.
00:53:25 - That was a big surprise to us.
00:53:30 - Under the same conditions. Now, the question is whether this tau is something intrinsic to the brain.
00:53:37 - Might it be the same for different kinds of stimuli, maybe even for different modalities?
00:53:42 - We don't know, but our guess is that tau does depend on the stimulus conditions.
00:53:57 - From a mathematical point of view if the stimulus is stochastic it has some,
00:54:06 - frequency properties what we can think of as a correlation time that is how
00:54:11 - much time has to pass for the stimulus to be uncorrelated with what it was in
00:54:16 - the past how much time has to pass before the stimulus becomes completely unpredictable,
00:54:23 - completely independent And in the conditions we've used, with a filter of about
00:54:28 - 150 milliseconds, the correlation time is in the order of 10 milliseconds,
00:54:33 - plus or minus a few milliseconds.
00:54:36 - So that means that in 150 milliseconds,
00:54:42 - the rat could sample, the sensory system could sample approximately 10 to 20,
00:54:49 - roughly 10 to 20 independent samples.
00:54:54 - If the correlation time were longer, that is, if it took the stimulus longer
00:54:59 - to achieve a value unrelated to the previous value, for example,
00:55:04 - making the filter a lower pass,
00:55:08 - then it would take longer in order to accumulate the same number of independent samples.
00:55:14 - Therefore, to achieve the same performance, the sensory system would have to
00:55:18 - actually sample for longer. So one would expect that tau may adapt to that by
00:55:24 - increasing the integration time.
00:55:28 - This is a bit mathematically complex, but I hope it comes through.
00:55:31 - But then, so the point would be that either I have some sort of attenuation
00:55:35 - factor of the evidence I accumulate,
00:55:37 - or I might be sitting in some oscillatory dynamic that is basically dictating
00:55:44 - to me that, okay, the early samples will have a higher impact on the information
00:55:49 - to grade than later samples.
00:55:50 - So how do you see it more as a continuous attenuation factor or do you see it
00:55:55 - more as sort of an oscillatory sampling process.
00:56:00 - So until we have evidence to
00:56:03 - the contrary our guess is that this is a continuous a continuous a weighting
00:56:11 - function that changes continuously over time we haven't really looked for and
00:56:15 - therefore haven't seen any signs that it might be oscillatory maybe at a gamma
00:56:20 - frequency or something like that
00:56:22 - could have some role, but we simply haven't looked at that.
00:56:25 - But then it is a weighing function that in turn might be task-dependent.
00:56:29 - So that means for different tasks and different, let's say, stages of learning,
00:56:32 - the weighing function will change. Yeah, no, for sure it does.
00:56:36 - I mean, let me try to pull out an example that might make sense intuitively,
00:56:42 - although I'm not working through this mathematically,
00:56:46 - but if if i ask you is
00:56:49 - the climate of the earth changing you would
00:56:52 - collect samples you'd go back in history and use ice core or whatever might
00:56:58 - be available and collect samples uh over a long time to find out if if uh if
00:57:03 - the climate of the earth is changing and so so so the the the.
00:57:11 - Period of time over which you'd have to collect samples in order to give us
00:57:15 - an answer would be many hundreds or thousands of years.
00:57:19 - If I ask you, is something that changes in the order of seconds,
00:57:26 - you would need many fewer samples.
00:57:29 - So clearly, in any kind of task, in order to estimate the properties of some time series of data.
00:57:44 - You will need a different number of samples in order to make the estimate.
00:57:49 - So from the model, this now follows logically, you also predicted that if you
00:57:56 - would compare now a primacy or a recency effect,
00:57:58 - like more evidence in the beginning or in the end, And that actually for both
00:58:01 - the rats and the humans, you would see primacy. And that's also what you observe.
00:58:05 - Yes. So this was actually a really nice prediction that came out of the model.
00:58:09 - But then the next step is now that you have the model in your hands,
00:58:12 - okay, what are the neurons really doing? Okay.
00:58:14 - So with that, you went to a frontal area in the rat brain.
00:58:19 - And you start to look at the neural responses in this task. ask.
00:58:23 - So what were the features of the neural responses that stood out for you in
00:58:29 - these populations of neurons that you measured from?
00:58:32 - So the data that I presented today came from two regions of the cerebral cortex.
00:58:37 - The primary sensory cortex, which as we said in rats, it's called barrel cortex,
00:58:43 - the cortex that receives input from the whiskers.
00:58:45 - And then we looked at a more frontal region, which is usually called premotor
00:58:50 - cortex or whisker motor cortex or as it's also being called by Carlos Brody
00:58:56 - frontal orienting field FOF.
00:58:58 - So different people have different names for it and so I showed some evidence
00:59:04 - from from that area as well.
00:59:05 - In the sensory cortex we found that.
00:59:10 - Around half the neurons have a very clear relationship in their firing to the stimulus properties,
00:59:17 - but what the neurons report in their firing is only the most recent events of
00:59:25 - the stimulus, the last 10 or 20 milliseconds. seconds.
00:59:27 - So these are what we call local coding neurons.
00:59:30 - They encode what happened immediately, just a few milliseconds ago.
00:59:37 - What happened 100 milliseconds ago or 200 milliseconds ago has very little impact
00:59:43 - on the likelihood of a spike at any given time.
00:59:46 - So at time t equals zero, Euro, the stimulus value at T minus 250 has no impact,
00:59:56 - but what happened at T minus 10 or 15 milliseconds has a large impact.
01:00:01 - So the neurons report the most instantaneous, most recent value of the vibration.
01:00:08 - So this is an essential element for the brain to be able to reconstruct the
01:00:16 - stochastic stimulus, this noisy vibration.
01:00:18 - The noisy vibration is made up of a sequence of local events,
01:00:23 - but any given local event does not define the stimulus.
01:00:27 - And so there has to be integration done after the sensory cortex in order for
01:00:32 - the brain to appreciate the overall statistical structure of the neuron, of the stimulus, sorry.
01:00:39 - So the sensory cortex neurons are.
01:00:43 - Are local coders, and the integration has to occur elsewhere.
01:00:47 - In the frontal region that we refer to as premotor cortex, the activity of neurons
01:00:54 - did in fact reflect the overall statistical structure of the stimulus,
01:00:59 - but not the local history.
01:01:03 - So for instance, from looking at the firing of a neuron in prefrontal cortex,
01:01:07 - we cannot say what happened in the whisker vibration 10 milliseconds ago or
01:01:13 - 15 milliseconds ago, but we can say what has happened over the course of the
01:01:19 - last 100 or 200 milliseconds.
01:01:21 - So those neurons already reflect integration, but we're not sure if they're
01:01:26 - doing the integration or are receiving a neuronal signal that's already processed.
01:01:33 - Right, but now, would you see these primary sensory neurons as wavelets,
01:01:38 - which is like neural wavelets?
01:01:40 - That are now encoding a time series.
01:01:43 - Yeah, that's one way to look at it. In fact, our colleague Rodrigo Quiroga is
01:01:49 - looking at spike trains through wavelet analysis, and it's actually quite a promising approach.
01:01:56 - Right. So now we have the prefrontal or the premotor area neurons looking at
01:02:03 - the signal at a longer time window. Yes.
01:02:05 - But can you correlate the neural response directly with the parametric control
01:02:10 - of your stimulus, like duration and the width of the vibration distribution?
01:02:16 - Yeah, the neurons in the premotor cortex,
01:02:20 - most of them, those that do have firing that's related to the stimulus,
01:02:27 - are better correlated with what we would call the perceived value of the stimulus
01:02:32 - than the physical value of the stimulus.
01:02:35 - That is, each stimulus is characterized by the intensity, which we call sigma, and by the duration.
01:02:42 - The neurons in premotor cortex,
01:02:45 - when they encode the memory of the stimulus or they encode the choice made by the animal,
01:02:53 - their activity is better related
01:02:54 - to the value of sigma after we take into account the effect of time.
01:03:05 - And that's one side of the PFC or the frontal neurons. And then there are others
01:03:11 - that encode the working memory element, or is that what you're talking about, the working memory?
01:03:17 - So we've seen in premotor cortex, as Renufo Romo, who is present today,
01:03:25 - he noted that he's seen the same thing in primate premotor cortex,
01:03:30 - that there's really a mixed soup of neurons there.
01:03:34 - It's incorrect in our data
01:03:38 - and his data it's incorrect to believe that one module
01:03:41 - one region of cortex is working in a
01:03:44 - homogeneous way one sees in the same trainings
01:03:48 - in the same test session even recorded adjacent electrodes in other words neurons
01:03:54 - sitting side by side do very different things in premotor cortex we see a mixture
01:04:00 - of neurons that include those that encode the stimulus as the stimulus occurs,
01:04:05 - but not the local history of the neuron, but rather the time-integrated value of the neuron.
01:04:12 - During the time-integrated value of the stimulus, of course.
01:04:18 - During the stimulus presentation.
01:04:20 - So those are online as the stimulus occurs, those neurons encode that stimulus.
01:04:26 - Other neurons encode the the preceding stimulus during the delay interval between the two stimuli.
01:04:34 - So those neurons seem to participate in working memory, and then still other neurons.
01:04:40 - That encode during the second stimulus the value of that stimulus,
01:04:44 - the parameter of that stimulus, and others the choice that the rat has to make
01:04:49 - based on the comparison between the first and the second stimulus.
01:04:53 - And to make matters even more complicated, some neurons encode combinations
01:05:00 - of these different features.
01:05:02 - That is, it's not unusual for a neuron to encode the stimulus as as it occurs,
01:05:06 - and the memory of the stimulus, whereas another neuron may encode the memory
01:05:11 - but not the stimulus as it occurs.
01:05:13 - So there's really no single label that you can give to the full population of neurons.
01:05:19 - So you can see why there might need to be different neuron types in order to
01:05:24 - solve this problem, but if there's no topography, as you suggest,
01:05:28 - then we have a real challenge for reading out what is the right answer here.
01:05:33 - So it's not just a case of looking at average firing in say the comparison neurons
01:05:38 - because they're intermixed with the working memory neurons and so on so what
01:05:43 - do you think is have you got any idea of how that readout might happen?
01:05:48 - Well I think that there's two issues I would say two ways of looking at this
01:05:56 - problem one is the computation that's being done physiologically by the cortex
01:06:02 - cortex, and the other is the readout.
01:06:04 - So about the computation, I would say we're close to the starting point.
01:06:11 - We just don't know what it is. We don't know, for instance, how a memory is
01:06:15 - stored for one or two seconds.
01:06:18 - We just don't know. We don't know whether firing rate is, which is what we've
01:06:23 - looked at and most laboratories look at, we don't know if firing rate is actually
01:06:26 - the memory or if there's something beyond firing rate.
01:06:30 - There could be population codes, there could be trajectories through multi-dimensional
01:06:34 - population space, there could be latent synaptic weights during the delay interval.
01:06:39 - There are many ways, many theories for how memory is stored,
01:06:45 - and we really can't confirm or exclude any of these.
01:06:49 - So about this we really don't know much, and we don't know the transformation
01:06:55 - from the feeling of an ongoing stimulus to a memory of the the feeling.
01:07:00 - This is completely unknown to us. The second point that you raise, decoding.
01:07:07 - Well, we can quite easily make networks that take our activity and decode it,
01:07:15 - and that's not difficult to do.
01:07:19 - From the same population of neurons, we can,
01:07:23 - We can look at the population from, say, two different angles,
01:07:29 - and by one angle we see a memory, and from another angle we see a choice.
01:07:34 - This has been shown, for instance, in a paper recently.
01:07:43 - Valerio Monte, I think, if I'm not mistaken, is the first author of the paper,
01:07:48 - from neurons in primate prefrontal cortex,
01:07:53 - where there were two features of a stimulus, I believe color and motion.
01:07:59 - And the monkeys were trained to do either a color task or a motion task.
01:08:04 - And he found that the same neurons encoded both features and could be decoded
01:08:12 - from the the activity of those neurons, either the color or the motion could be decoded.
01:08:17 - So it's not that difficult using sort of offspring of principal component kinds
01:08:25 - of analyses to find dimensions whereby a certain property can be decoded.
01:08:31 - We can do that, but what we don't know is what makes the neurons fire that way.
01:08:37 - So from the population response, you can decompose it into different components,
01:08:43 - one of which gives you a good indication of the animal's behavior, its actual response.
01:08:50 - Yes. And so we're assuming some downstream system is able to do that decomposition. Yes.
01:08:57 - But now the standard model of decision-making are drift-diffusion models where
01:09:02 - we just integrate over firing rates.
01:09:04 - And in some sense, if you look at your model or your physiology,
01:09:07 - your data, then you also see some very marked or task-specific modulation of firing rates.
01:09:14 - So is the minimum model that we could apply to this then exactly that,
01:09:20 - the drift diffusion model?
01:09:22 - Yeah, this has something in common with drift diffusion and it's the kind of
01:09:27 - stimulus statistically that has been explored for drift diffusion.
01:09:33 - I think that one of the main differences is that the behavioral arrangement
01:09:40 - in this task does not ask the rat or the human observer to reach some threshold and make a choice.
01:09:48 - We don't give them the option of collecting as much evidence as they want and then taking the action.
01:09:57 - In those cases, it's been argued that the animal or the human collects evidence
01:10:06 - until they reach, and so they drift to a threshold, and then they make the choice.
01:10:11 - In our case, we determine the stimulus duration, and the animal and the rat
01:10:17 - and the human are required to wait for the entire stimulus.
01:10:21 - So that may take them beyond the threshold or they may not be allowed to reach the threshold.
01:10:28 - That's determined by the stimulus duration. Okay, but that would still mean
01:10:32 - that the decision variable could also be reflected just in the firing rate and
01:10:37 - then it's a matter of reading out that firing rate.
01:10:39 - It's not necessarily my favorite model, but you have market correlation if you
01:10:46 - want between the performance and your firing of these neurons.
01:10:50 - Of course, these are good examples, but in all the visual that you have seen,
01:10:57 - in the end, if we're close to the decision moment, you can actually predict
01:11:01 - from the firing rate of the neurons whether we go left or right.
01:11:05 - Yeah, and we've also looked at error trials.
01:11:10 - And on error trials, those neurons which have on correct trials a correlate
01:11:20 - in their neuronal firing rate to the choice of the animal have that same correlate on error trials.
01:11:25 - In other words, if a neuron fires at a high rate when the rat is going to turn
01:11:30 - right on correct trials and a low rate when he turns left on correct trials, then on error trials.
01:11:39 - The neuron will fire for turning right and not fire for turning left even though it's an error.
01:11:45 - So many of the premotor neurons that are choice selective are choice selective
01:11:52 - whether the choice is correct or incorrect. Correct.
01:11:54 - Right. So this is really amazing, right?
01:11:57 - Because you're really now going from a signal-dependent response in the nervous
01:12:02 - system to, let's say, a subjective state-dependent response.
01:12:07 - And with that, actually, you're really close to explaining this task.
01:12:12 - And then at the end of your talk, you also showed how actually it can have some
01:12:16 - diagnostic value if we look at humans, Which was a very surprising result where
01:12:21 - you showed that, well, actually, in performing another variation of this task,
01:12:26 - humans that are at risk of schizophrenia actually show a very different kind of performance.
01:12:33 - Yes. So what is the salient feature there in your mind? Right.
01:12:39 - So we have just a few subjects who are completely healthy,
01:12:45 - yet the family history suggests that they're in the category of what's called
01:12:50 - at-risk for schizophrenia, meaning that there's something in the family genome
01:12:55 - that may give a predisposition.
01:12:58 - Yet at the time of testing, they're absolutely healthy and symptom-free.
01:13:04 - So the task that we gave to the low-risk subjects and the healthy at-risk subjects
01:13:15 - was a time-duration comparison.
01:13:18 - So they received two vibrations sequentially and had to judge whether the first
01:13:25 - or second duration was greater.
01:13:28 - And the confounding factor was the intensity.
01:13:34 - And so this is motivated by the fact that the previous work had found that judgment
01:13:39 - of intensity was confounded by duration.
01:13:41 - We wondered whether perception of duration is confounded by intensity.
01:13:46 - And we found that in both groups of subjects, in fact, the perceived duration
01:13:52 - was confounded by intensity.
01:13:56 - In the sense that the stronger stimulus,
01:14:02 - the higher variance, the higher intensity stimulus was on average perceived
01:14:07 - as with a bias towards a longer duration.
01:14:12 - So to put it in very short and in rhyming words, longer feels stronger.
01:14:18 - Shorter duration feels weaker.
01:14:21 - And that this is the interpretation of the psychometric curves.
01:14:23 - So subjects were not able to purely exclude the intensity of the stimulus from the duration,
01:14:34 - even though the task would require that for perfect performance.
01:14:38 - In the subjects that were healthy but might be at risk due to family history.
01:14:45 - The effect of intensity was much more pronounced.
01:14:48 - In other words, they were less able to exclude the irrelevant stimulus feature from the computation.
01:14:56 - To do the task perfectly, the brain should discard the intensity information,
01:15:04 - not use it in the computation, and do the computation based purely on time.
01:15:09 - That was what the rule was in the task, is measure the time.
01:15:15 - And these subjects showed a much stronger effective intensity.
01:15:20 - So essentially, they were less able to discard the irrelevant feature.
01:15:27 - So we made a long tour now from the whisker system into the clinic.
01:15:32 - And you've been sort of getting us through that every step of the way.
01:15:39 - And so in that sense, you have been really championing this whole more system
01:15:44 - level perspective on the brain and how it relates to behavior.
01:15:47 - So if you would like to follow in that tradition that you represent,
01:15:51 - what should be Matthew's law that we have to adhere to?
01:15:55 - So I don't have any laws.
01:15:58 - I don't have any wisdom to pass on to the younger generation.
01:16:05 - Or even to us, you know? Even to us, I have nothing useful to say.
01:16:10 - The only thing I could add is that the approach that we use in the laboratory,
01:16:16 - and the approach, nothing works unless you have very, very good students.
01:16:21 - I have excellent students, very bright and motivated and hardworking,
01:16:26 - and that's what allows our approach to work.
01:16:31 - And the approach I outlined at the very beginning of the talk,
01:16:35 - which is we construct experiments that have three elements,
01:16:40 - a controlled sensory stimulus, a behavioral output, and neuronal activity,
01:16:46 - measurements of neuronal activity.
01:16:47 - And these three elements we can view as being a triangle and that gives us three
01:16:53 - possible connections the three sides of the triangle the connection between
01:16:57 - the stimulus and the behavior.
01:17:01 - Gives us some insight into how the brain uh how the brain experiences a sensory
01:17:06 - stimulus and this is quantified by psychophysics and we we try to study how
01:17:12 - a sensory stimulus produces a a a neuronal firing pattern within the sensory system,
01:17:20 - which we can call coding or sensory coding or encoding,
01:17:24 - whereby the sensory receptors and then the successive stages of processing put
01:17:30 - the physical properties of stimulus into the language of the brain action potentials.
01:17:35 - Then we want to look at,
01:17:38 - how a sensory representation can lead to a decision, which we can refer to as
01:17:43 - decision-making or decoding or any number of things.
01:17:47 - So I think that if you have the right members of your research team,
01:17:55 - then putting these three elements into an experiment can provide some insights.
01:18:00 - Now, we want to put this law on a T-shirt, and we don't want to make the print too small. Yeah.
01:18:04 - So what kind of, what's Matthew's law we can actually print on a t-shirt now?
01:18:10 - I think the law is, again, I don't want to call it a law.
01:18:14 - I'd say something that we have fun doing. Let's call it what we have fun doing instead of the law.
01:18:21 - It's Matthew's triangle. It's the golden triangle, which I would say is a systems approach.
01:18:29 - I'd say the approach is that it's more interesting when you actually see the
01:18:36 - organism doing something.
01:18:38 - So it's important to have behavior, it's important to know the input,
01:18:44 - and it's important to know the neuronal basis of this.
01:18:47 - So to put it in a word, I'd say the golden triangle of cognitive neuroscience.
01:18:54 - Wouldn't it be better to find a fourth point and call it a diamond?
01:18:59 - Matthew's diamond!
01:19:03 - So I can't be the one that does that. We'll do it for you. Don't you worry.
01:19:08 - But the last thing is, look, Tony likes traveling.
01:19:10 - And so he wants to definitely come and visit you in Trieste five years from
01:19:14 - now because he's not so quick booking these kinds of things.
01:19:16 - So, but five years from now it's going to be at your lab to test whether a prediction
01:19:23 - you made today was actually confirmed or rejected.
01:19:26 - So what's the key hypothesis that you want to see tested in that timeframe?
01:19:31 - Well, goodness gracious, that's a tough one. But I think that what I think would
01:19:36 - be fun, really fun to be able to do within five years is to have rats.
01:19:43 - We know we could do it in humans. It's not difficult, but to have our animals
01:19:49 - able to apply two different decision rules to sensory inputs.
01:19:56 - So in the work that I talked about today, we have a comparison task where the
01:20:03 - stimulus is characterized by intensity and duration,
01:20:06 - and we can train rats to do an
01:20:08 - intensity comparison, or other rats we can train to do a time comparison.
01:20:13 - The stimulus set is the same, so wouldn't it be fun and interesting to be able
01:20:19 - to train rats in one session or one group of trials to receive the stimuli and make one judgment,
01:20:25 - and then in the next group of trials to make the other judgment,
01:20:28 - and to see where in the brain the processing diverges according to what the animal is doing with it.
01:20:38 - The sensory input would be the same in both groups of trials,
01:20:42 - but what the brain has to extract to make its choice and its action is different,
01:20:48 - and it would it would be intriguing to see where the task rule takes its effect
01:20:56 - on neuronal processing.
01:20:58 - Right. Great. Matthew Diamond, thank you very much for this conversation. Thank you.
01:21:04 - The CSN Podcast was produced by the Convergent Science Network of Biometrics
01:21:09 - and Biohybrid Systems, a project funded by the European 7th Research Framework Program.
01:21:17 - For more interviews, recorded lectures, or upcoming conferences in the field
01:21:22 - of biometrics and bio-hybrid systems, go to csnnetwork.eu.
01:21:30 - Music.

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Matthew Diamond on whisker system and decision-making

Season 2015

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Stephen Noctor on cortical development and precursor cells

Ranulfo Romo on decision-making and somatosensory cortex

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