David Bau: And so, yeah, so we're lucky to welcome here today, Winnie young
David Bau: who is a well, you're you're a neuroscience. Phd. Student. But she also does work in interpretability and large language models. I'm not actually sure which topic she's going to talk about today. She's welcome to talk about either one. But I've been really interested in her. You know. Her neuroscience work actually caught my eye with. She's done a bunch of work in memory formation neuroscience. And I thought it was quite interesting. So if she doesn't talk about it, then you can ask her about that stuff as well.
David Bau: and so so. But you're from Nyu right? Yes. Is that right? So up for the day, and so so so grab a time to chat with her this afternoon before she takes off. I don't know how long you're here for. I will be here the whole afternoon. Yeah, that's great. Yeah. Yeah.
David Bau: Yeah, that's that's that's that's good. So welcome, Winnie. And and you should go ahead and project your yeah, I'll I'll set this up.
David Bau: are you projecting already. Yes, I can. So to project. Let's see.
David Bau: let's see, are you okay? Okay, yeah. So go ahead and share your screen.
David Bau: and then we will, we'll get you going.
David Bau: The company
David Bau: excellent!
David Bau: Okay, no present review.
David Bau: Oh, you switch screens, or something like that, or
David Bau: Look at that! Let me try
David Bau: see if I can share. But with all the screen.
David Bau: okay, maybe not.
David Bau: See something back here.
David Bau: That's not what I want. I can close this. It won't put zoom that could.
David Bau: We're all set
David Bau: Hello again, everyone. So my name is Winnie and luckily I will talk about my work in neuroscience. But it's about learning and memory in the brain. And I met Professor Bot neurobs. And I think it will be very, very interesting to to start this conversation about how
David Bau: learning and memory works in the brain, and and, you know, have fun discussions about connection with how that works in Llm. So
David Bau: I hope we'll have fun today.
David Bau: Right? As
David Bau: David already introduced, I'm currently a Phd student 5th year. So almost graduating in neuroscience and study, learning and memory in the brain and also started to do interpretability work in large language models. Recently.
David Bau: okay, so
David Bau: this, you know, when when you guys look into memories and stuff in large language models. You guys are very fortunate where you have this, even though it's a black box but so easily accessible. Just wanted to
David Bau: give you some sense of what the challenges that we're facing as neuroscientists. So basically, when we wanted to record. You know, how? The the neuron active. When when to perform certain tasks, we need to
David Bau: do perform surgeries and insert electrode into the brain, and so that what that was that has been a big chunk of my Phd, you know, practicing open the skulls of
David Bau: the animals, insert wires into their brain trying to target. You know those wires very thin. They're about the size of like a single strain of hair, and we wanted to put them in a very precise way hopefully into a brain area of interest. In this case, hippocampus.
David Bau: and you know, we wanted to record the brain activity while animal do something interesting. So we have to build up those mazes like physically constructing them ourselves, going to workshops, designing like things. And and then, you know, building our circuits.
David Bau: For example, this one.
David Bau: you know, design. I designed a task where you know when certain lights goes on, the animal need to learn. Okay, Green light associate with going to the right and if it successfully goes to light, right, it will get reward. So now he's waiting here. There's a waiting period now. Green lights goes on.
David Bau: Has to go to the right side. If he successfully go to the right side, he get rewarded here, and it's hard to say here, but the the water will be automatically delivered, and there are doors on this maze, so that you know we can, you know, restrict his behavior and just learn this task
David Bau: just to give a taste of what's the lab life like? Okay. And there's another example, this is a rat much bigger than the little mouse we saw from the video above. And this mouse is running on a very simple maze. It's just a linear maze, and the animals are
David Bau: water restricted. So they're very motivated to just run back and forth and gather reward at this water port. This is a very simple.
David Bau: not like complicated, but it's get us to
David Bau: started some very foundational property of the hippocampus, as I will share later. You know this is the wires that connect to their brains that we can collect their brain signals as when they perform tasks.
David Bau: And you know, you have fun with your animals. Well, you're not doing it. Experiments. I built this like luxurious hotel for for my animal. A lot of toys for them enrich them.
David Bau: while they're not doing tasks. And apparently this enrichment really helps them to become faster at learning tasks.
David Bau: okay, so that's the fun. Lab life. Yeah. What was the reward? You said? Sorry. What was the reward? What was the reward? Yes. So in our cases we'll just give water. The reason why is that they are water restricted? Before the task. So they're
David Bau: very thirsty, essentially. So they're motivated to get this reward. Can they smell? We don't add, there will be cases in some experimental setups. People add some other to, you know, different flavors, or sometimes food palate as reward. But
David Bau: oh.
David Bau: when animals chew food, palate will create electrical noises that interfere with electrical signals from the brain that we want to record. So want to avoid that. That's why we choose water. Some nitty, gritty details.
David Bau: okay.
David Bau: all right. So we put electrode in. And that's the brain region. We want to study the hippocampus. It's right located above your ear, centered in the brain. And this brain region. Really, if you dissect it out. It looks like a seahorse, and hence the name hippocampus from Greek.
David Bau: So the hippocampus has been really heavily studied by neuroscientists, and it's a very, very interesting brain region, and
David Bau: that has been you know, hypothesized and involving in many interesting cognitive functions like
David Bau: planning and mental simulation, memory, consolidation memory, retrieval as as well as form a representation of the space in the external world, and and which lead to this theory called cognitive map theory, you know. Okay, we will kind of I will go through some of my studies that touch upon
David Bau: some aspects of hippocampal function.
David Bau: And then we'll broadly. I will divide like my work into 2 part. One part is the presentation sorry representation part. So how things are represented in the brain.
David Bau: the other part is what I call operations, like memory, retrieval, memory, consolidation, planning, mental simulation of certain operations. Okay, let's start with representation in the brain. Sorry this is the overview. We'll start with the representation. We'll go to memory processing.
David Bau: And there's the part 3, which you know. I think I'm too ambitious. Put everything in, but I don't think we will. Maybe we will not have time to go into it. It's about kind of a trying to some fun discussions about unifying this mental travel in the past, which is memory, retrieval, and this mental travel in the future with this imagination.
David Bau: and what the memory is really constructive rather than a static trace, and
David Bau: the relationship of that to false memory and hallucination. Probably that would generate interesting discussions
David Bau: related to large language models. So maybe I don't have time to go to part 3. But during one-on-one meetings with people will be fun discussion points. Okay, representation in the hippocampus. So to talk about representation in the hippocampus, we really have to start from this really seminal findings about place cells in hippocampus. How many of you guys heard of place cells?
David Bau: Okay, okay, some of you. Okay, that's good. So that means I have something to say, all right. So so this discovery of place on the hippocampus together with another kind of discovery, called place cells, won the Nobel Prize in 2014.
David Bau: So basically, John O'keefe recorded from the Rat's brain while he put animals in a box. And he observed this very interesting phenomena. Where, when? When the rats?
David Bau: you know, navigate to a specific location in the brain, a particular cell would fire, and that cell would fire very specifically. Only when animal go to a specific corner of the space and become silent when animal go to any other quarter. So he called those cells that are tuned to specific spatial location in space place cells.
David Bau: And you know, okay, this is a video showing you in real time on red navigate in this complicated, I mean in this long track.
David Bau: And here are the cells. So each.is a spike when a cell fires and each color represents a different cell.
David Bau: and you know, as you can see when animal is at here, this corner, this red cell is firing right. And previously, when he was at this corner the blue cell was firing, and so on and so forth. When animal goes to different parts of the maze, different cells would activate.
David Bau: So that play that is the concept of play cell and and
David Bau: you know, if each individual cell represent a specific location in the space. We take all the cells together. That's what John O'keefe think that you will have a cognitive map of the external world internally represented in the brain, and he hypothesized that hippocampus is this
David Bau: plays a role in this construction of cognitive lab
David Bau: in the brain. Your question, here, how do you make sure that the the cells which are activating is a function of space and not time.
David Bau: That's a very good question. So you can test that right? You can
David Bau: when animal is at the other places. Other corners Well, if
David Bau: if animal revisit the same location at different time, the spell will buy in the same way. So it's invariant of the time, but variance of in terms of space. But that's a very good point.
David Bau: Later. You're thinking very much forward. Later we'll talk about how hippocampus also represent time.
David Bau: So if these are dependent on the specific locations of the coordinates, I am thinking of a scenario where there will be no turns, but we'll have.
David Bau: or red on a circle.
David Bau: So at that place there will be right on a circle. At that point there will be right on a circle. So instead of having turns, you'll have those as circles, and I'm wondering if it is on a circle at that point will it always be blue? Or, if it is like.
David Bau: I know, 2D. 2D. And then it's just a circle at that location instead of having a turn. It's moving on a circles along this circle. I will have like different circles. For example, there are different turns, and I will have circles on the turns.
David Bau: and I will just like measure his activity on the circle, and I will start a new experiment on another place.
David Bau: and it will also have a circle. So at every place, instead of having the currents, I will have circles. I see what you're going where you're going. You're saying that you have certain in a circular maze, but you have a different circular maze. Do you? You know the same cell that fire at like a specific corner of maze. One does it
David Bau: fire course to fire at the other mess at the corresponding corner or location? It's not the case. So this is.
David Bau: you're thinking, step ahead as well. So you know, if you put animal in different environments, there's this phenomenon called remap.
David Bau: So basically, the please say, I would
David Bau: globally remap to fire at different locations when it's when the animal is in a different environment. So because hippocampal memory is very context dependent. When when you're talking about a different context, you have a different kind of code for that environment.
David Bau: But
David Bau: lately I will go go talk about. There are some signals in the hippocampus that are preserved across environments. And and and we'll talk about that. But for the place cells it will undergo remap.
David Bau: So do you have a baseline?
David Bau: Baseline activity. How would these colors change if I measured at 6 random cells?
David Bau: Instead of place sales? If I put my electron somewhere. Right? So about 30% of you know about about
David Bau: 30% of the cells in the hippocampus are place cells so meaning that they have very specific. You know we have, like criterias, both where when they meet the criteria of a place cell. But there are other cell types in hippocampus. Maybe some are tracking time, some are tracking the order of the events. Are you asking about? I just wonder like how this picture would change if I measured different cells.
David Bau: Right? They they will have different properties. Like.
David Bau: it's the colors are kind of localized, but it still looks quite noisy. Right? Yes, so that's a very good point. That's you know. This is the noisiness of biology, and also the noisiness of the single cell. And later we'll talk about how, if you
David Bau: rather than build your cognitive map from single cells. But look at populations of cells, and that would be a more robust way to look at things. So very good point, you know, this is, this is the biology looks like. This is the data of biology very noisy.
David Bau: Hmm, do these representations get more crisp and less noisy over time?
David Bau: That's a very good question. Yes, it does. over. You know, if you put the animal into a totally normal environment. Never been there. You place fields are much more noisy comparing to if the animals get used to that environment.
David Bau: you know. but it gets stabilized. Over time. Okay, thanks for the fascinating questions.
David Bau: So, okay, so this cognitive theory,
David Bau: was kind of it's exciting. This discovery of place cells. kind of.
David Bau: you know, provide a new basis for the cognitive theory, but the cognitive theory was not proposed by John O'keefe. It was proposed by this guy called Edward Tolman back in 1948, so he did some a series of very interesting experiments. Kind of
David Bau: at that time. You know, the background is that at that time of point all the neuroscientists are basically behaviorists. They believe in
David Bau: stimulate response associations. But Tolman doesn't think that way. So many think that like learning is more than about stimulate response association. To learn. You have mental, you have mental constructions, you have a belief. And so you predict, what
David Bau: predict what you do based on those mental constructions, and you know the
David Bau: for example, he designed this very clever experiments where? At 1st the mouse would go through this channel and go to this spot. G. To get food, and animals are trained very, very well, very familiar with tasks. They always go this way to get rewarded.
David Bau: and later he then constructed this more complicated maze and add more arms into this maze and block the original pathway where the animal would get reward.
David Bau: and then now the animal is very, very hungry. Where should the animal go? And he find out? The animal would go to the path like this path that is closest to where it was rewarded before the reconstruction. So that is his idea that you know
David Bau: it's not animals, not just remember this ritual of going into this specific path, but rather construct a map in the brain, such that it's flexible. When things in environments are changing.
David Bau: So that was back in his time. He can only look at behaviors without having the tools to tap into. The internal activity.
David Bau: Okay,
David Bau: So here comes to the part of my study. So, instead of doing single cell recording, what I did was I recorded from large populations of cells in the hippocampus, and animals are running on this what we call the figure 8 mace, so similar to the video that I showed you. You know, animal has to wait from here and then go
David Bau: turn left or right at this junction, and it's a memory task, because then has to remember what is the previous turn, and it has to alternate in order to get rewarded.
David Bau: So it's a simple task. But we can record when it will perform this memory task. And
David Bau: when, after I got population activity, what I did was dimensionality reductions that I can look at. What's the population representation look like. And after doing dimensionality reduction. That's what I see in B, okay, this is
David Bau: the population activity into the.
David Bau: And you can see this really good correspondence between this manifold
David Bau: in the hippocampus and this physical environment in outside world. So it's kind of I guess a
David Bau: another way to show this cognitive map. Yes, so a couple of questions. So first, st how many neurons were you recording from? And then, second, I don't think I fully understand, like exactly what those colors correspond to in in terms of like which neurons are firing when so? Yes, so the the colors are color coded by the spatial locations in this space. So you know.
David Bau: this is the actual maze. And I just superimposed animals behavior trajectory on this maze. And you can see that at the beginning. It's this blue and at the reward pod is green and it's red. So it's just color coded by, okay, so just 0 to one is progression through the maze.
David Bau: Yes, okay, yes. 0 to one is progression to the maze. Yeah.
David Bau: And then and then and then the number of neurons. And then, how did how did you? What were you doing? Dimensionality reduction on to make that right? So basically, what you have is so I recorded in this particular session we recorded from 500 neurons simultaneously, which is a lot for electrophysiology.
David Bau: and then basically, what you will have after recording is two-dimensional matrix of
David Bau: each row is a cell. Activity of cell, and each column will be a time point. So will be a matrix of each cell's activity over time. And then you do dimensionality reduction on that matrix.
David Bau: In this case, yes, it's a long, it's it's with you. Map and and
David Bau: and then I plot the 1st 2 components. And the matrix is the matrix going to be binary. So it's either going to be the neurons firing or not? Or is the frequency at that time? It's the firing which is continuous, not
David Bau: yeah. How do you decide those 500 cells to
David Bau: to probe. Yes. So those are the 500 cells are all the cells that we get from the hippocampus. So we're interested in hippocampus. So we
David Bau: can I check the coordinate where the hippocampus is, and what targets?
David Bau: So so is it like all the cells in the hippocampus? No, no, the hippocampus. Yeah, millions of cells. And I just take a very small samples from the whole hippocampus population. But in child it's enough to help to reconstruct this cognitive map.
David Bau: But how did you know that those 500 s. Are the one?
David Bau: Well, so it's distributed. So if you just if you know roughly in this hippocampus. Where does you know there are pioneers who have done the work of mapping out? Okay? So we know roughly where in specific sub region, in the hippocampus you will find place cells, and we just stick our electrode there and then you empirically test. If you you get place cells or not by
David Bau: before. You don't know if you can get it. But but in a in a, in a sub region of the hippocampus, where space is represented, it's really distributed. So you can
David Bau: stick your electrode. It's a relative large area that you can stick your electrode in, and
David Bau: it will always get placed out in that to do this very great, his questions were great.
David Bau: So the your result is that this manifold that you visualize
David Bau: kind of has these 2 loops?
David Bau: Or that's the recite right?
David Bau: What do you mean?
David Bau: The result that you were presenting to us is that like
David Bau: the one for the one, the result? Oh, the result? Oh, the result!
David Bau: Is that like this manifort that you obtain via dimensionality reduction?
David Bau: has 2 loops like the real maze. Exactly. Yes, yes, I didn't get that. Oh, sorry, yes, yes. So that's the point. Yes, the the neuro manifolds. It's amazing that you have internal representation in your brain that really mimic the topology of the external world and kind of having basically demonstrating that you have cognitive map of the outside world in the brain.
David Bau: So it's very interesting that you have 2 loops, and you chose like with your coloring scheme. The both like left and right, are both like symmetrically colored. But then, in your manifold, it could also be that there's just like a single loop, and like both left and right, are
David Bau: encoded in the same way. Yeah. I'm wondering whether, if you were like, do different coloring for left and right, would you still see 2 loops? Is it like encoding left and right differently or not. No, it is if you yeah, if I code it left and right, and then it will be very nice correspondence. One loop is the left one. The other loop is the right, completely distinct. That's interesting. Yeah, yeah.
David Bau: I have a question.
David Bau: So
David Bau: I'm not from biology. But I do like neural network probing and intervention. So whatever I'm seeing right here, I think this is probing right, Professor, I'm not wrong. Can you also intervene like you know what I mean. Yes, definitely, I know exactly what you're you mean. So there is technology to do so, even though it's not perfect. But there's a technology called optogenetics.
David Bau: Long story short, you
David Bau: through virus injection, you inject some viral construct that have ion channels on it so such that those ion channel response to light. So when you turn on light of a specific frequency, that ion channel would open that would force the cells to fire.
David Bau: or, if you have another light frequency, will force the cell, the ion channel to close and the cell will be silent. And that way you can kind of activate versus suppressing the activity of the cell in a very specific manner. But you know its biology is very challenging. You have to deliver the virus you have to make sure it's like so many details unlike you guys can
David Bau: can do patching path patching and activation patching very easily.
David Bau: And yeah, but there are ways on
David Bau: And you're right. This is just recording without manipulation. Yet.
David Bau: Okay, okay. So so
David Bau: so hippocampus. You know, since this Nobel prize discovery is very interesting. Okay, we have this GPS in our brain that represents this. But
David Bau: you know, it's not just about space. We also represent time, and we also represent events. And and it turned out that hippocampus also represents these other things. And I just want to talk one particular example of it, which is a study that I
David Bau: participating when I was undergrad student, where, you know, we're using a different technique instead of stick electrode into the brain. We implant a little glands so that we can imaging the firing of the cells of the brain. And
David Bau: You know, this is the little camera. That implants on top of the hippocampus. Ca, one is a specific area of the hippocampus, where there are a lot of places I'm using.
David Bau: Insert a small piece of glass on top of it, so that we can, in imaging the activity of the cells where, when the animal perform this task, where animal run around a maze in a structured way. So every 4 laps
David Bau: the animal will get rewarded. So that's the structure that we imposed onto the task. And then we look at, okay, what is activity look like in the brain. So what we find out is that the cells are not just very specially specific. So like, for example, this cell, the heat map, the color, represents the firing rate of the cell. The cell is very specifically only fired
David Bau: when the animal get to this corner of the space.
David Bau: But not just this. If you look at the activity across
David Bau: labs. So this is the activity during Lab one. This is Lab 2, this is Lab 3, and this is Lab 4. This cell only selectively fired at lab one. So we call this a lab, one cell
David Bau: like orange you a little bit. So the X-axis is the location like you linearized trajectory of this maze and and each row is a trial, and the heat maps color representing the firing rate of the neuron. So you know, this cell
David Bau: higher at specific location, but also specific trial.
David Bau: Yes, so sorry for by lab, do you mean? Oh, okay, I heard lab, and I was like, sorry. Yes, so every 4 lap, it will get rewarded. And that's how you get this, the temporal structure. And then we can, as a reference. Then we can look at okay, does. The cells are sensitive. Other cells sensitive to this temporal structure.
David Bau: Okay? And just to make sure, I understand, one lap would be, the animal starts goes all the way around the maze once, and then that completes the 1st lap, and then it goes around again right away. That's the second one
David Bau: and also like these, are different trials with different rats, or the same rat, the same animal, the same animal in the same session.
David Bau: I just want to say that this work? I was stumped by
David Bau: The 1st author of this work is Chen, who is also a neuroscientist like me. But now started to work in large language models, and he's now deployed. He is my partner in science and life. Okay?
David Bau: So what is interesting about this event. Identity is that the
David Bau: if you look across days. So the cells that are firing at Lab one will be also firing and day 2. So this kind of identity event identities of the circle across days. And what is even more interesting is that when you put animal into a totally different maze in a maze here. So from a square box to a circle box.
David Bau: what you will observe in terms of the spatial map, this gets to your question a little bit. What you will observe in terms of spatial map is this global remapping? So if you look here, focus on here you can see that this cell, this is the same cell it fires at this corner when it's the square maze. But when you change this local configuration, you know, to a circle environment. It's remapped to fire at a different location. So
David Bau: so the space signal remap. But very, very interesting this
David Bau: time this event identity preserved. So if you also let the animal do wrong, every 4 laps get a reward in this different environment. This
David Bau: sequence, like the other structure preserved across different environment. So
David Bau: it's interesting how some of the signals are invariant. Some are invariant.
David Bau: Okay,
David Bau: yeah. Okay. So I've already talked about this. You know, we record this is now back to my recent work.
David Bau: put animal into this figure. It means you get the 2D matrix of spiking data into dimensionality reduction.
David Bau: and what I'm looking at now is that you've seen. This is the spatial map from the population point of view. But there is also this very, very interesting thing that this
David Bau: event, sequence the order of the events, are also represented in the population level in the hippocampus. So you know.
David Bau: in the bottom panels, as I, you know, colored this manifold in a different way instead of color. With respect to the spatial location. I color with respect to the progress in the session. So the trial number. And you can see this very nice organization from early to late trials, very elderly, represented in the manifold as well. So hippocampus also keep track of the time.
David Bau: Not just the space. Small question, does this timer reset sometimes.
David Bau: Yes, that's a very good question. So you know.
David Bau: looking at the previous study, the single cell study about the trial. Like every 4 trials, you get the reward right? That's kind of a reset. So every 4
David Bau: times when it it resets. And and so it's
David Bau: the the short answer is, there are those different signals. There are ones that are reset, maybe looking at similarities across time. You know, when it's left one again, it's finding this similarity and reset. And there are times where it's just continuously drifting away. With the time. And and that's the picture that we're seeing here.
David Bau: So there are cells that are tracking different aspects
David Bau: and so that can reset themselves, that continuously drifting away. Basically.
David Bau: So there are some cells that will continue to drift throughout the throughout the life of the animal. Right? Right?
David Bau: Right?
David Bau: See?
David Bau: I'm sorry. What is time here? Is it like 70 trials or 70 trials? Okay.
David Bau: is there? If you did umap in like 3 dimensions instead, would it like stratify the number of trials more clearly? Yeah, if you yeah. 2D. Is already enough to represent this, but if you see 3D. You can see the structure better, but nothing fundamentally changed. If you go to 3D,
David Bau: does it look continuous in 3D,
David Bau: what do you mean? Continuous work?
David Bau: So over here? You see that? Yeah, right? Yeah, yeah, yeah, you can. It's continuous. You can. It's continuous. Yeah, I hope I have. Yeah, had that animation that it's rotating. 3D. It's not here.
David Bau: I will send to you the offline, you will see what it's like in 3D.
David Bau: Another question. Maybe I didn't. I missed something. But if you're looking at different populations. Right? It's like what? Some time cells and some space cells.
David Bau: okay. Good question.
David Bau: it's not like time. Cells will always be time, cells and place cells will be always be place cells. So in a different environment, it could in a different environment this time cell. Here can be a place cell there, and also the picture get more complicated when people now start to, you know.
David Bau: reformalize what his hippocampus is doing. It's not just space and time, it can be any continuous task, relevant, variable. So, for example, this. Now, this space is not the physical space. It's the ascend space, for example, frequency of the tone.
David Bau: so the animal would be trained to listen to. You know different frequency of the tone, and press the lever at certain frequency. Then here the the you know, variable that the animal has to track is the sound space, and if you look at it, the brain and the hippocampus, there are sound frequency cells. So basically tracking this continuous progression in this this other space, you know.
David Bau: So basically hippocampus, it's just it's also a memory machine, right? It's recording your your experience. So whatever cognitive, variable that you're engaging with the hippocampus will keep track of it. And it's very flexible. So
David Bau: the sun frequency cell when it's tracking the space is the place cell.
David Bau: I have a very stupid question or remark, so if we think of it like this is, this is the set of amount of mouse or red. But if we put
David Bau: VR on those rats and mouse, how did it change? Very good question. And people have done that, you know.
David Bau: People have had fixed the animal with some you know, construct and then put them onto a ball where they're just running on this rotating ball. And there's the VR screen from the animal in the record. And you see, please same thing.
David Bau: Just one quick question. Use GPS, then
David Bau: it's the GPS in this virtual world. So I think it's similar to John's question. So in this particular task. Did you see.
David Bau: like distinction between the place cells and the time cells, or where the same set of cells acted both as a place cells and a time cell. Yeah, good question.
David Bau: let me just
David Bau: I fucking short question is
David Bau: Sometimes there are. There are cells are just purely place cell, so it will always fire at a specific location, regardless of which trial it is meaning. It will fire at exactly same like approximately the same frequency firing rate. But there are cells. There are place cells, so they fire at a specific location. But the firing rate will be different. According to the the time.
David Bau: So it's conjunctive, right? It's both representing the space and time.
David Bau: So that's actually most of the case. Most of the cells that I see are conjunctive place and times.
David Bau: Okay, so when when you're analyzing these, all the cells look like independent rows of this matrix.
David Bau: are there physical relationships between cells. Yeah, there, there is no relationships. There is no mapping between the physical space, like the physical proximity and the proximity of the cell in the brain. So there are brain areas like work that way. For example, there's retinal mapping.
David Bau: You know, the physical world, the pixels that are close to together in a physical world, are represented by cells in your retina that are close to each other and similar your somatosensory system.
David Bau: 2 fingers are represented by cells that are. There's there's a humanglers in the brain, but space doesn't work that way. It's like salt and pepper. It's distributed.
David Bau: So your umap shape is the same for the space and the time experiment right?
David Bau: Does that mean that all the individual neurons that you look at have the same frequencies.
David Bau: Sorry? Sorry. In the previous slide in the previous slide. I was not this this?
David Bau: Oh, yeah, you had like space and time, and it was like 2 different colorings. Yes. Oh, that was the next slide, absolutely. And it's like neural manifold for space and time has the same shape.
David Bau: it's the same, it's the same manifold. I just colored them the different way.
David Bau: which means that they they have the same frequency.
David Bau: No, she's just use. She's just using the same. You map.
David Bau: It's the same data, same transformation, same manifolds. It's the same data. The coloring is post hoc.
David Bau: one question. I'm sorry to ask a lot. So when you, when you, when you measure this activation or like firing, do they have the same threshold, or this threshold is different for different neurons.
David Bau: Okay, so the so this, the the thresholds for firing is the same for every neuron.
David Bau: there's membrane potential. You can measure. And across a certain threshold there will be a action potential. That threshold is the same for every neuron. But across that, you know
David Bau: threshold the cell would fire. But fire at what? Frequency? That will be different.
David Bau: Okay, I have one question. I actually got lost. Why are you saying that these neurons are mapping time or representing time, because in the second figure it is kind of like individual individual. Yes, individual columns of your matrix that the umap was calculated on projected on the umap. Right?
David Bau: The whole matrix.
David Bau: the whole matrix gets dimensionality reduction. Yes, but when you map it with time dimension on the second row. If you just go back one slide on the on the matrix.
David Bau: Oh.
David Bau: yeah. Once. Yeah, yeah. Yeah. So this is the matrix. The umap was calculated on right right and
David Bau: on the second row of images in next slide. They are kind of like.
David Bau: if if I'm understanding properly individual columns of the matrix projected on the and that's how you color it. Right? What do you do there?
David Bau: So each each. represents a point in time.
David Bau: Oh, that's what you mean. So each you're saying that each dot corresponding to. So each.in this 2 dimensional space corresponding to one time, which is one column. In this high dimensional space. Time and trial are not same thing here. Right. Is that the same thing? Well, time and trial is not the exact same, but
David Bau: instead of color with time, I call, you know, it's correlated right in this particular case.
David Bau: Okay, so
David Bau: sorry. Wait. Am I understanding? So there's there's a number of dots equal to the number of columns. Yes, okay. So your dimensionality reduction was along the rows.
David Bau: You went from N rows, where N is the number of neurons to dimensionality reduction is done on number of neurons. Basically.
David Bau: So yeah, the number of dimension corresponding to number of neurons.
David Bau: you know, side kind of side question. But do you know, Pascal Wallace?
David Bau: Because I know he's at. He's at Nyu, and he. He's like the world expert in like dealing with spike trains like he's like, really good at, like he has a whole bunch of statistics on, like, how to do spike train stuff. Really? Well, interesting. I I seriously don't know. I think he's like in the center for neural data science there, I see. Yeah.
David Bau: Oh, cool.
David Bau: Alright, if you
David Bau: you know, do this kind of analysis in different spatial kind of configurations. Here is a linear maze. So you saw this earlier when I showed the videos, animals go back and forth on the linear track. You do this, you will see kind of similar phenomenon. If you move on to a more complicated maze, here's a radio arm maze where there are 8 arms on the maze.
David Bau: and if you look at the manifold, it looks like this. It's kind of mimicking this spatial configuration as well. And if you look at the neuro manifold colored in trials. There's this nice
David Bau: Your presentation of the trial event as well. So
David Bau: Just to say that this phenomenon is observed in different tasks and different rodent species. In the linear myth we recorded from rats the radio recorded from mouse.
David Bau: okay, so that was the part about the representation in the brain. Now we move on to dynamic processing.
David Bau: Wow! Time.
David Bau: It's all right. We can go over a little bit. Okay. Where? When should I stop just to get a sense of how. So we we actually, we have we have this room for quite a while, and so you could shoot for as late as noon.
David Bau: Yeah. And then, and maybe we could take
David Bau: a little a little break and then have a
David Bau: do. We have a second talk that's scheduled it at noon. So we could. Yeah, okay, yeah.
David Bau: Okay, I will try to go quickly, and then we can always talk after
David Bau: so here's the part about memory. And you know.
David Bau: yeah, burn is very different from larger models in the in one. At least, this aspect of that burn is a continual learning machine like it's there's no pre training. Post training is continuously learning and all updating its weight. with a new experience. And there's this classical problem of catastrophe getting in your networks where.
David Bau: when you learn new information, this new information would catastrophically interfere with the old information. So it's a very hard problem to solve in your networks. But the brain solve this problem right? We continuously learn new information, not at the expense of destroying what we have learned before. So what is the brain's solution for this catastrophic, forgetting problem?
David Bau: Well, the theory goes back from 1989. So my mentor, Yuri Buzaki, in the nineties so proposed this two-stage model of memory formation. So he hypothesis that in order to
David Bau: solve this catastrophe, forgetting problem, you have to form your memory in 2 stages. The 1st stage is this memory encoding stage which hippocampus play a crucial role because hippocampus has those very very
David Bau: rapid synaptic changes. So it's very plastic. It can rapidly learn new things and encode it on the fly. When you experience new, very fast,
David Bau: change or update weights on the flight while you're learning information. Right? You know, this is good in terms of it can rapidly change but
David Bau: this rapid change can bring problems. You know we learn in new information. Are you sure this, if this new information is contradictory or in conflict with your world model? Are you sure you always wanted to rapidly update your world model just based on the single experience? Right? The world is very noisy. So the solution of the brain is to have this 2 separate system, the hippocampus fast.
David Bau: Record everything. But later.
David Bau: when you wanted to simulate and integrate this new learning into the existing knowledge framework. There is the second stage called Consolidation Phase, where new experience during offline period, when you're not having this online demand for encoding new information. This
David Bau: learning episodic experience, are gradually transferred to new cortical networks for long-term storage, where the memory will transform from episodic memory to more semantic like memory, and and because there is those, there is no demand of fast encoding.
David Bau: the there, there is time to resolve conflict and slowly incorporate new information without damaging the old.
David Bau: Oh, memory!
David Bau: So that was Yuri's theory, right? This is totally theory, with no evidence at that point, and and we
David Bau: he not only like proposed to this theory, but he hypothesized a neural mechanism, for how this could happen, so, what his
David Bau: bread and butter in the lab is, he recorded from hippocampus, and he sees very interesting activity. You know, you stick a wire into the brain. You can see a population activity in the brain, and that's we call local feud potential. It's basically population activity.
David Bau: and if you would, and and what you observe is, there are those very strange events that, you know. Every now and then the brain would fire in very, very high frequency about 100 to 200 Hertz really fast bursting of activity. And what underlying those very bursty activity, those bursty activity, are pushed up with ripples and underlying those events are
David Bau: essentially population first, st so cells fire together in a very synchronized way.
David Bau: and you know those short oracles. It occurs when animals, not actively paying attention in encoding new information, not during learning or navigating, but during what you call offline period, when animals are quiet, awake, or during sleep, you will observe those events.
David Bau: and and Yuri hypothesized that these sharp with loopholes can be a neurophysiological mechanism that support memory transfer. So during those events
David Bau: cells firing in a reputed synchronizely in a by high frequencies. So experience will be
David Bau: repetitively reactivated during those events and and potentially transferred to the cortex.
David Bau: Yeah, so many of the things are jargons to me. But
David Bau: what I wanted to know is, do we actually forget anything?
David Bau: Yes, and that's you're thinking, one step ahead. And that's why I want to talk about that next.
David Bau: Okay, but just to you know.
David Bau: give you a sense of this shop with ripple. So I said, it happens during flight awake. So whenever the animal stops.
David Bau: okay, so so this is plotting. You know.
David Bau: because you have this spatial map of the brain. Then if you, if you have the recording of the hippocampus cells, then you can decode
David Bau: where the animal is, and this is showing the decoded location from hippocampal activity. But
David Bau: to make the story simple.
David Bau: when animals are quite awake. You know, not really running and actively sample new information. If you decode activity in the brain. You will see that
David Bau: the the cells are decoded to represent, not the current location of the animal, but some other part of the brain some some other part of the maze. So
David Bau: so so this you know, during those quiet awaken shop with ripples.
David Bau: people have. We are. What we find is that
David Bau: you know, when animals actively sampling the environment, you will see a specific sequence of cell that is firing right? The place cells firing in sequence. But when animal go to sleep, or when it's quite awake, what we observe is that specific sequence of cell reactive
David Bau: in exact same order as when the animal is experienced the world. So this is the word memory replay
David Bau: right, as if that this memory has been reactivated again.
David Bau: So
David Bau: You know.
David Bau: Yuri hypothesized at that time that ripples would replay the the memory. But at that time there is no evidence because we couldn't record but
David Bau: in the nineties nineties that's the 1st time people could record from enough cells in the brain and basically demonstrate that indeed, during those sharp foot ripple events, cells that you will see those reactivation of the activity of the neurons
David Bau: such that you, they reactive in a way that the sequence really mimic the sequence when the animal is is experienced, the external world.
David Bau: So that's memory replay during shuffle repose.
David Bau: so you asked. Does the brain forget? And
David Bau: yes, we definitely don't remember everything in our life. What we experience. so
David Bau: And so memories are selectively consolidated during sharp wood ripples. During sleep. But how?
David Bau: Some of the memories are forgotten. So how does the brain select what to remember and what to forget? That is indeed a important question. And actually, that was what my recent work was about.
David Bau: I will. Just without going to the details. Just
David Bau: very, very high level. Tell you about this kind of mechanism that we find about memory selection.
David Bau: Okay, like. Recall that. You know, when animal is learning on this maze, we can
David Bau: have a manifold that's
David Bau: represent the space. And this manifold also give us information of the events, the specific events.
David Bau: So that is very good, because if we have, if we have this handle on the events. Then we can look at how those different events are selectively reactivated or consolidated during sleep.
David Bau: But we can, you know, we we can differentiate those different events. Then we can look at them during offline period. How they are. Consolidated.
David Bau: So
David Bau: so the way we did it. Is, you know, this is an example. Event. Cells are firing. We can decode not only which part of the maze of this event is replaying about, but we can also decode which individual event this replay event is replaying
David Bau: and we can do that when animal is quiet, awake on the maze during shop whip ripples.
David Bau: and we can do that when animal go to sleep afterwards, during non-rem sleep.
David Bau: And then, if we look at the relationship, between what is
David Bau: replayed during a wake ripple, and what is replayed during sleep ripple, is this very interesting correlation that you know some of the events are
David Bau: consolidated much more frequently during sleep than others.
David Bau: And and the the sleep replay frequency distribution is is plotted in purple, and the red is the maze replay. So what you're seeing seeing here is that you know some of the events, for example, those early trials, the proportion of those events get replayed is very low. So they're not very. They're not being reactivated during sleep. But
David Bau: the interesting part is that those events not get activated during sleep are also the events that are not get activated during awake replay.
David Bau: And so our hypothesis is that
David Bau: you know, there's there's this very high correlation between what is replayed when animal is on the maze, and what is replayed when animal go to sleep later. So our hypothesis is this.
David Bau: awake reports act as a memory tag, such that the things.
David Bau: the events that wanted to be remembered and stored are tagged by those replay ripples, such that when animal go to sleep later. Only the events that has this memory tag would be, you know, rehearsaled and repetitively reactivated and consolidated or potentially transferred into the neocortex. And those events that doesn't have this memory tag are
David Bau: being forgotten. They're not reactivated rehearsals, and they're forgotten. So it's on the same day. Right? So it's on the same day. Yes. So if you give it like 7 different environments, how would it react like, how would this change over time? So if you
David Bau: what do you mean? How would it change? So you have one setting on Friday, another setting on Saturday you give like new task every single day like new environment. And I'm I'm trying to understand, like, how the distribution of this memory tags change like after if you record it after 7 days. So how the long term memory over 7 days sum up versus what happened on Friday night?
David Bau: I I understand your setup where you wanted to record across days when there are diversity of experience. So here, you know, the events are in the same same day. But you're curious about events that are across different days. And what about? So what I wanted to understand is like, if it is like very short term like, it only cares about what what happened the last day.
David Bau: Comparing to it's a long term memory. Yeah, over 7 days, like, so when I'm like dreaming about it. Right? So, yes, yes, okay, you're you're thinking about whether the recent experience get replayed more or the remote experience get replayed. More, I mean, I don't have an answer for this, and I don't think there is a short answer for this. This is really depending on a lot of factors, for example, how
David Bau: salient this experience is, you know, even if it's a remote experience, if if it's very novel to the animal. So we know this like novelty is very important, so novel experience will get disproportionately replayed much more than
David Bau: a like normal experience. So recency is a factor like everything. Constant. Recent experience would get reactive bit more than remote experience. But novelty would override this. So many factors. Interplay. But I guess the message here is whatever the the experience that get really get activated a lot during sleep during this, you know, offline periods
David Bau: those experience you can, you can predict which experience by looking at the awake repro activity.
David Bau: Yes.
David Bau: go ahead. I just. I think there's so many interesting connections to Llms like, I I don't know. Is there any research on like memory consolidation over like a sequence of tokens, because, like memory tags.
David Bau: I like. I wonder if there's a way? Maybe if there are memory tags, I have no idea if the research is out there and like, maybe you could also detect memory tags by like introducing like novel tokens of some kind or like, maybe there's a there's a signal there, I don't know. There's just this is really cool. So thank you so much.
David Bau: Yeah. Well, but the brain is also very different from Llm. Like, what is even memory tag for the for the for Llm. Right? Also. Llm. Doesn't have this offline period, at least. Now. So
David Bau: you know.
David Bau: there's all in this forward pass when we interact with it. So that's corresponding to this encoding base. It doesn't. It wouldn't engage into this offline period. But actually, what I wanted to say is that maybe that's something that we need to introduce to large language models to have this offline period where it goes through the things it has learned.
David Bau: find. Commonalities do comparisons across experience and abstract out
David Bau: commonality, abstract abstract rules and beautiful world models.
David Bau: But those are not present. Now.
David Bau: offline period be considered like something where you know you're giving like in context, like some things. So it's like looking at. The model is looking at things in context, and then at a certain point, it like looks back into its internal knowledge and gives output based on that. You're saying that
David Bau: even during in context, learning. In order to, you know, solve this in context, learning task, it's retrieving knowledge from the weights. So some sort of memory. Right? So there can be this
David Bau: maybe implicit memory retrieval process that's going on during learning
David Bau: But I guess the difference there is, then you don't, you do. You don't have the chance to reorganize what happened in the past? You only simply retrieve
David Bau: what's in the past, because you, if you do in context, you don't do English. At the same time, at least, the current
David Bau: you either do in context or in wait versus us is always a mix.
David Bau: Would you say that? Yeah, I mean.
David Bau: I I think Jojo made a good point that this is closer to this retrieval part. Rather than this. With the system consolidation, where, you know, you can really look at experiences across contacts, windows not just in contacts, but looking at experience across contacts point regularities and organizes
David Bau: so I I still don't think we have a correspondence in in martial English models now.
David Bau: and and I'm very interested in like, not like novel ways. To. I I think large language model need a hippocampus for for the continued learning problem.
David Bau: Yeah.
David Bau: When I look at this. It reminds me much more of like
David Bau: right offline. Yes, yes, yes, yes, yes, yes, this is the thing that I don't have time to to talk about. But
David Bau: there's this very rich literature and kind of this virtuous circle of exchange of idea between this replay literature and and the neuroscience, as well as this offline replay literature in Rl, so you know, Dennis has this before he
David Bau: find it deep. When he was a cognitive neuroscientist, he specifically was interested in memory, and he did a lot of Fmi studies looking into how human memory works. So he's very interested in this concept of mental traveling. In the past, mental traveling in the future.
David Bau: and and in the early days they have papers about, you know, drawing this inside of replay and and and and use that for improving. Rl, so, for example, the the
David Bau: deepq learning the deepq network is actually they have this replay buffer. That you know, Deepq network really improved atari games to like human level. And one of very important component of this replay buffer. And it's actually inspired by how memory will work in the brain. If you read their blog post. At least that's
David Bau: That's what they say. Their inspiration comes from. Still, the engineer who designs the learning algorithm in this way and
David Bau: print model right? And then it's frozen
David Bau: and and can play the video games.
David Bau: So neural, the neurosense that you show is very interesting because
David Bau: the learning just happens at the same. The agent just learns right? Right?
David Bau: Roll it up.
David Bau: That's a big difference.
David Bau: Yeah, very big difference. It's continuously updating its weight.
David Bau: Yeah, after in context, learning the insight you get from in context, learning in large language model will be gone right wherever the chain of thought get the conclusion you get from this very computationally heavy chain of thought, reasoning. If there's important lesson there in another session it's gone, and so temporary it would not get updated. But the brain.
David Bau: the human engineers will lock all of it, and they will throw it into the next training round.
David Bau: Yes, that's I. Yes, I mean, have you guys noticed that you like
David Bau: well, has memory for across session like it's remember your personal preference and traits. But I guess, doesn't it do that discreetly like it'll like, sometimes decide that like something that you tell. Yeah. So I think people started to, you know, trying to build in this kind of long term memory aspect, or semantic memory, like
David Bau: aspect. But I think it's pretty premature, though. Pretty, very premature, very different, very preliminary. Yeah, yeah. Yeah. And I feel like there's a lot to do to to improve
David Bau: feel like the closest we have is like fine tuning a bunch of Laura's and doing it like very frequently. But that's not
David Bau: still not really the same. Yeah. And I'm curious, if you guys know, like, if you do this fine tuning in a continuous learning setup like you, continuously fine tune like different tasks. Does this catastrophic forgetting for a problem like, I know, it's a big problem for neural networks. But for this very large models in the current regime. Has anyone done studies on this like whether
David Bau: this catastrophic acting is still a big problem.
David Bau: Eventually, that is kind of like bound to happen?
David Bau: I mean, in in memory, we, we went up to 10,000 edits. But eventually the model performance does decrease degrade by a lot
David Bau: yeah.
David Bau: yeah, I mean, I guess there are decay of human memory as well over time. But I guess maybe there's suboptimal ways when this can happen. If some
David Bau: you know, humans can learn, learn some abstract rules from experience, and the specificities
David Bau: can be gone. But the abstract rules can remain. Things like that, like the system, consolidation, aspect of it, if it can be incorporated.
David Bau: would be very helpful.
David Bau: Yeah, alright. I think that's all I have, for now we've gotten to noon.
David Bau: so let me stop the recording here.