- Welcome to the Huberman Lab Podcast, where we discuss science and science-based tools for everyday life. I'm Andrew Huberman, and I'm a professor of neurobiology and ophthalmology at Stanford School of Medicine. My guest today is Dr. Terry Signowski. Dr. Terry Signowski is a professor at the Salk Institute for Biological Studies, where he directs the Computational Neurobiology Laboratory.

And as his title suggests, he is a computational neuroscientist. That is, he uses math as well as artificial intelligence and computing methods to understand this overarching, ultra-important question of how the brain works. Now, I realize that when people hear terms like computational neuroscience, algorithms, large language models, and AI, that it can be a bit overwhelming and even intimidating.

But I assure you that the purpose of Dr. Signowski's work, and indeed, today's discussion, is all about using those methods to clarify how the brain works, and indeed, to simplify the answer to that question. So for instance, today you will learn that regardless of who you are, regardless of your experience, that all your motivation in all domains of life is governed by a simple algorithm or equation.

Dr. Signowski explains how a single rule, a single learning rule, drives all of our motivation-related behaviors. And it, of course, relates to the neuromodulator dopamine. And if you're familiar with dopamine as a term, today you will really understand how dopamine works to drive your levels of motivation, or in some cases, lack of motivation, and how to overcome that lack of motivation.

Today, we also discuss how best to learn. Dr. Signowski shares not just information about how the brain works, but also practical tools that he and colleagues have developed, including a zero-cost online portal that teaches you how to learn better based on your particular learning style, the way that you in particular forge for information and implement that information.

Dr. Signowski also explains how he himself uses physical exercise of a particular type in order to enhance his cognition, that is his brain's ability to learn information and to come up with new ideas. Today, we also discuss both the healthy brain and the diseased brain in conditions like Parkinson's and Alzheimer's, and how particular tools that relate to mitochondrial function can perhaps be used in order to treat various diseases, including Alzheimer's dementia.

I'm certain that by the end of today's episode, you will have learned a tremendous amount of new knowledge about how your brain works and practical tools that you can implement in your daily life. Before we begin, I'd like to emphasize that this podcast is separate from my teaching and research roles at Stanford.

It is, however, part of my desire and effort to bring zero-cost to consumer information about science and science-related tools to the general public. In keeping with that theme, I'd like to thank the sponsors of today's podcast. Our first sponsor is BetterHelp. BetterHelp offers professional therapy with a licensed therapist carried out completely online.

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Again, that's helixsleep.com/huberman to get up to 25% off and two free pillows. And now for my discussion with Dr. Terry Signowski. Dr. Terry Signowski, welcome. - Great to be here. - We go way back. And I'm a huge, huge fan of your work because you've worked on a great many different things in the field of neuroscience.

You're considered by many a computational neuroscience, so you bring mathematical models to an understanding of the brain and neural networks. And we're also going to talk about AI today, and we're going to make it accessible for everybody, biologist or no, math background or no. To kick things off, I want to understand something.

I understand a bit about the parts list of the brain, and most listeners of this podcast will understand a little bit of the parts list of the brain, even if they've never heard an episode of this podcast before because they understand there are cells, those cells are neurons, those neurons connect to one another in very specific ways that allow us to see, to hear, to think, et cetera.

But I've come to the belief that even if we know the parts list, it doesn't really inform us how the brain works, right? This is the big question. How does the brain work? What is consciousness? All of this stuff. So where and how does an understanding of how neurons talk to one another start to give us a real understanding about like how the brain works?

Like what is this piece of meat in our heads? Because it can't just be, okay, the hippocampus remembers stuff, and the visual cortex perceives stuff. When you sit back and you remove the math from the mental conversation, if that's possible for you, how do you think about quote unquote, how the brain works?

Like at a very basic level, what is this piece of meat in our heads really trying to accomplish from, let's just say the time when we first wake up in the morning and we're a little groggy till we make it to that first cup of coffee or water, or maybe even just to urinate first thing in the morning, what is going on in there?

- What a great question. And you know, I have a, Pat Churchland and I wrote a book, "Computational Brain," and in it, there's this levels diagram. And it levels of investigation at different spatial scales from the molecular at the very bottom to synapses and neurons, circuits, neural circuits, how they're connected with each other, and then brain areas in the cortex, and then the whole central nervous system span 10 orders of magnitude, 10th to the 10th in spatial scale.

So, you know, where is consciousness in all of that? So there are two approaches that neuroscientists have taken. I shouldn't say neuroscientists, I should say that scientists have taken. And the one you described, which is, you know, let's look at all the parts, that's the bottom-up approach. You know, take it apart into a reductionist approach.

And you make a lot of progress. You can figure out, you know, how things are connected and understand how development works, how neurons connect. But it's very difficult to really make progress because quickly you get lost in the forest. Now, the other approach, which has been successful, but at the end, unsatisfying, is the top-down approach.

And this is the approach that psychologists have taken, looking at behavior and trying to understand, you know, the laws of behavior. This is the behaviorists. But, you know, even people in AI were trying to do a top-down, to write programs that could replicate human behavior, intelligent behavior. And I have to say that both of those approaches, you know, bottom-up or top-down, have really not gotten to the core of answering any of those questions, the big questions.

But there's a whole new approach now that is emerging in both neuroscience and AI at exactly the same time. And at this moment in history, it's really quite remarkable. So there's an intermediate level between the implementation level at the bottom, how you implement some particular mechanism and the actual behavior of the whole system.

It's called the algorithmic level. It's in between. So algorithms are like recipes. They're like, you know, when you bake a cake, you have to have ingredients and you have to say the order in which they're put together and how long. And, you know, if you get it wrong, you know, it doesn't work, you know, it's just a mess.

Now, it turns out that we're discovering algorithms. We've made a lot of progress with understanding the algorithms that are used in neural circuits. And this speaks to the computational level of how to understand, you know, the function of the neural circuit. But I'm gonna give you one example of an algorithm, which is one we worked on back in the 1990s when Peter Dayan and Reid Montague were postdocs in the lab.

And it had to do with a part of the brain below the cortex called the basal ganglia, which is responsible for learning sequences of actions in order to achieve some goal. For example, if you wanna play tennis, you know, you have to be able to coordinate many muscles and a whole sequence of actions has to be made if you wanna be able to serve accurately.

And you have to practice, practice, practice. Well, what's going on there is that the basal ganglia basically is taking over from the cortex and producing actions that get better and better and better and better. And that's true not just of the muscles, but it's also true of thinking. If you wanna become good in any area, if you wanna become a good financier, if you wanna become a good doctor or a neuroscientist, right, you have to be practicing, practicing, practicing in terms of understanding what's the details of the profession and what works, what doesn't work and so forth.

And it turns out that this basal ganglia interacts with the cortex, not just in the back, which is the action part, but also with the prefrontal cortex, which is the thinking part. - Can I ask you a question about this briefly? The basal ganglia, as I understand, are involved in the organization of two major types of behaviors.

Go, meaning to actually perform a behavior, but the basal ganglia also instruct no-go. Don't engage in that behavior. And learning an expert golf swing or even a basic golf swing or a tennis racket swing involves both of those things, go and no-go. Given what you just said, which is that the basal ganglia are also involved in generating thoughts of particular kinds.

I wonder therefore if it's also involved in suppression of thoughts of particular kinds. I mean, you don't want your surgeon cutting into a particular region and just thinking about their motor behaviors, what to do and what not to do. They presumably need to think about what to think about, but also what to not think about.

You don't want that surgeon thinking about how their kid was a brat that morning and they're frustrated because the two things interact. So is there go, no-go in terms of action and learning? And is there go, no-go in terms of thinking? - Well, I mentioned the prefrontal cortex and that part, the loop with the basal ganglia, that is one of the last to mature in early adulthood.

And the problem is that for adolescents, it's not the no-go part for planning and actions isn't quite there yet. And so often it doesn't kick in to prevent you from doing things that are not in your best interest. So yes, absolutely right. But one of the things though is that learning is involved.

And this is really a problem that we cracked first theoretically in the '90s and then experimentally later by recording from neurons and also brain imaging in humans. So it turns out we know the algorithm that is used in the brain for how to learn sequences of actions to achieve a goal.

And it's the simplest possible algorithm you can imagine. It's simply to predict the next reward you're gonna get. If I do an action, will it give me something of value? And you learn every time you try something, whether you got the amount of reward you expected or less, you use that to update the synapses, synaptic plasticity, so that the next time you'll have a better chance of getting a better reward and you build up what's called a value function.

And so the cortex now over your lifetime is building up a lot of knowledge about things that are good for you, things that are bad for you. Like you go to a restaurant, you order something, how do you know what's good for you, right? You've had lots of meals in a lot of places and now that is part of your value function.

This is the same algorithm that was used by AlphaGo. This is the program that DeepMind built. This is an AI program that beat the world Go champion. And Go is the most complex game that humans have ever played on a regular basis. - Far more complex than chess, as I understand.

- Yeah, that's right. So Go is to chess, which chess is to something like checkers. In other words, the level of difficulty is another way off above it because you have to think in terms of battles going on all over the place at the same time and the order in which you put the pieces down are gonna affect what's gonna happen in the future.

- So this value function is super interesting and I wonder whether, and I think you answered this, but I wonder whether this value function is implemented over long periods of time. So you talked about the value function in terms of learning a motor skill. Let's say swinging a tennis racket to do a perfect tennis serve or even just a decent tennis serve.

When somebody goes back to the court, let's say on the weekend, once a month over the course of years, are they able to tap into that same value function every time they go back, even though there's been a lot of intervening time and learning? That's question number one. And then the other question is, do you think that this value function is also being played out in more complex scenarios, not just motor learning, such as let's say a domain of life that for many people involves some trial and error, it would be like human relationships.

We learn how to be friends with people. We learn how to be a good sibling. We learn how to be a good romantic partner. We get some things right, we get some things wrong. So is the same value function being implemented, we're paying attention to what was rewarding, but what I didn't hear you say also was what was punishing.

So are we only paying attention to what is rewarding or we're also integrating punishment? We don't get an electric shock when we get the serve wrong, but we can be frustrated. - What you identified is a very important feature, which is that rewards, by the way, every time you do something, you're updating this value function every time and it accumulates.

And the answer to your first question, the answer is that it's always gonna be there. It doesn't matter. It's a very permanent part of your experience and who you are. And interestingly, and behaviorists knew this back in the 1950s, that you can get there two ways of trial and error.

Small rewards are good because you're constantly coming closer and closer to getting what you're seeking, better tennis player or being able to make a friend. But the negative punishment is much more effective. One trial learning. You don't need to have 100 trials, which you need when you're training a rat to do some tasks with small food rewards.

But if you just shock the rat, boy, that rat doesn't forget that. - Yeah, one really bad relationship will have you learning certain things forever. - And this is also PTSD. Post-traumatic stress disorder is another good example of that. That can screw you up for the rest of your life.

But the other thing, and you pointed out something really important, which is that a large part of the prefrontal cortex is devoted to social interactions. And this is how humans, when you come into the world, you don't know what language you're gonna be speaking. You don't know what the cultural values are that you're going to have to be able to become a member of this society and as things that are expected of you.

All of that has to become through experience, through building this value function. And this is something we discovered in the 20th century. And now it's going into AI. It's called reinforcement learning in AI. It's a form of procedural learning, as opposed to the cognitive level where you think and you do things.

Cognitive thinking is much less efficient because you have to go step-by-step. With procedural learning, it's automatic. - Can you give me an example of procedural learning in the context of a comparison to cognitive learning? Like, is there an example of perhaps like how to make a decent cup of coffee using purely knowledge-based learning versus procedural learning?

- Oh, okay, okay. - Where procedural learning wins. And I can imagine one, but you're the true expert here. - Well, you know a lot of examples, but just since we've been talking about tennis, can you imagine learning how to play tennis through a book, reading a book? - That's so funny.

On the plane back from Nashville yesterday, the guy sitting across the aisle from me was reading a book about, maybe he was working on his pilot's license or something. And I looked over and couldn't help, but notice these diagrams of the plane flying. And I thought, I'm just so glad that this guy is a passenger and not a pilot.

And then I thought about how the pilots learned. And presumably it was a combination of practical learning and textbook learning. I mean, when you scuba dive, this is true. I'm scuba dive certified. And when you get your certification, you learn your dive tables and you learn why you have to wait between dives, et cetera, and gas exchange, and a number of things.

But there's really no way to simulate what it is to take your mask off underwater, put it back on, and then blow the water out of your mask. Like that, you just have to do that in a pool. And you actually have to do it when you need to for it to really get drilled in.

- Yes, it's really essential for things that have to be executed quickly and expertly to get that really down pat so you don't have to think. And this happens in school, right? In other words, you have classroom lessons where you're given explicit instruction, but then you go do homework.

That's procedural learning. You do problems, you solve problems. And I'm a PhD physicist, so I went through all of the classes in theoretical physics. And it was really the problems that really were the core of becoming a good physicist. You know, you can memorize the equations, but that doesn't mean you understand how to use the equations.

- I think it's worth highlighting something. A lot of times on this podcast, we talk about what I call protocols. It would be, you know, like get some morning sunlight in your eyes to stimulate your suprachiasmatic nucleus by way of your retinal ganglion cells. Audiences of this podcast will recognize those terms.

It's basically get sunlight in your eyes in the morning and set your circadian clock. - That's right. - And I can hear that a trillion times, but I do believe that there's some value to both knowing what the protocol is, the underlying mechanisms. There are these things in your eye that, you know, encode the sunrise qualities of light, et cetera, and then send them to your brain, et cetera, et cetera.

But then once we link knowledge, pure knowledge, to a practice, I do believe that the two things merge someplace in a way that, let's say, reinforces both the knowledge and the practice. So these things are not necessarily separate, they bridge. In other words, doing your theoretical physics problem sets reinforces the examples that you learned in lecture and in your textbooks, and vice versa.

- So this is a battle that's going on right now in schools. You know, what you've just said is absolutely right. You need both. We have two major learning systems. We have a cognitive learning system, which is cortical. We have a procedural learning system, which is subcortical, basal ganglia.

And the two go hand in hand. If you want to become good at anything, the two are going to help each other. And what's going on right now in schools, in California at least, is that they're trying to get rid of the procedural. - That's ridiculous. - They don't want students to practice because it's going to be, you know, you're stressing them.

You don't want them to be, to feel that, you know, that they're having difficulty. So, but we can, but it can do everything. - For those listening, I'm covering my eyes because I mean, this would be like saying, goodness, there's so many examples. Like here's a textbook on swimming, and then you're going to go out to the ocean someday and you will have never actually swum.

- Right. - And now you're expected to be able to survive, let alone swim well. - It's crazy, it's crazy. And I'll tell you, Barbara Oakley has, and I have a MOOC, Massive Open Online Course, on learning how to learn. And it helps students. We aimed it at students, but it actually has been taken by 4 million people in 200 countries, ages 10 to 90.

- What is this called? - Learning how to learn. - Is it, is there a paywall? - No, it's free, completely free. - Amazing. - And, you know, I get incredible feedback, you know, fan letters almost every day. - Well, you're about to get a few more. - Okay, well.

- I did an episode on learning how to learn, and my understanding of the research is that we need to test ourselves on the material. That testing is not just a form of evaluation. It is a form of identifying the errors that help us then compensate for the errors.

- Exactly. - But it's very procedural. It's not about just listening and regurgitating. You're, you know, you've put your finger on it, which is that, and this is what we teach the students, is that you have to, the way the brain works, right, is not, it doesn't memorize things like a computer, but you have to, it has to be active learning.

You have to actively engage. In fact, when you're trying to solve a problem on your own, right, this is where you're really learning by trial and error, and that's the procedural system. But if someone tells you what the right answer is, you know, you know, that's just something that is a fact that gets stored away somewhere, but it's not gonna automatically come up if you actually are faced with something that's not exactly the same problem but is similar.

And by the way, this is the key to AI, completely essential for the recent success of these large language models, you know, that the public now is beginning to use, is that they're not parrots. They just, they're not, they just don't memorize what the data that they've taken in.

They have to generalize. That means to be able to do well on new things that come in that are similar to the old things that you've seen, but allow you to solve new problems. That's the key to the brain. The brain is really, really good at generalizing. In fact, in many cases, you only need one example to generalize.

Like going to a restaurant for the first time, there are a number of new interactions, right? There might be a host or a hostess. You sit down at these tables you've never sat at. Somebody asks you questions, you read it. Okay, maybe it's a QR code these days, but forever after you understand the process of going into a restaurant, doesn't matter what the genre of food happens to be or what city, sitting inside or outside, you can pretty much work it out.

Sit at the counter, sit outside, sit at the table. It's, there are a number of key action steps that I think pretty much translate to everywhere. Unless you go to some super high-end thing or some super low-end thing where it's a buffet or whatever, you can start to fill in the blanks here.

If I understand correctly, there's an action function that's learned from the knowledge and the experience. - Exactly. - And then where is that action function stored? Is it in one location in the brain or is it kind of an emergent property of multiple brain areas? - So that, you're right at the cusp here of where we are in neuroscience right now.

We don't know the answer to that question. In the past, it had been thought that, you know, the cortex had, were like countries that each of which, each part of the cortex was dedicated to one function, right? You know, there's, and interestingly, you record for the neurons and it certainly looks that way, right?

In other words, there's a visual cortex in the back and there's a whole series of areas and then there's the auditory cortex here in the middle and then the prefrontal cortex for social interaction. And so it looked really clear cut that it's modular and now what we're facing is we have a new way to record from neurons.

Optically, we can record from tens of thousands, from dozens of areas simultaneously and what we're discovering is that if you want to do any task, you're engaging not just the area that you might think, you know, has the input coming in, say the visual system, but the visual system is getting input from the motor system, right?

In fact, you know, there's more input coming from the motor system than from the eye. - Really? - Yes, Ann Churchill at UCLA has shown that in the mouse. So now we're looking at global interactions between all these areas and that's where real complex cognitive behaviors emerge. It's from those interactions and now we have the tools for the first time to actually be able to see them in real time and we're doing that now first on mice and monkeys, but we now can do this in humans.

So I've been collaborating with a group at Mass General Hospital to record from people with epilepsy and they have to have an operation for people who are drug resistant, to be able to take out, find out where it starts in the cortex, you know, and where it is initiated, where the seizure starts and then to go in, you have to go in and record simultaneously from a lot of parts of the cortex for weeks until you find out where it is and then you go in and you try to take it out and often that helps.

Very, very invasive, but for two weeks we have access to all those neurons in that cortex that are being, you know, recorded from constantly. And so I've used, I started out because I was interested in sleep and I wanted to understand what happens in the cortex of a human during sleep, but then we realized that, you know, you can also figure, you know, people who have these debilitating problems with seizures, you know, they're there for two weeks and they have nothing to do.

So they just love the fact that scientists are interested in helping them and, you know, teaching them things and finding out where in the cortex things are happening when they learn something. This is a goldmine, it's unbelievable. And I've learned things from humans that I could have never gotten from any other species.

- Amazing. - Obviously language is one of them, but there are other things in sleep that we've discovered. Having to do with traveling waves, there are circular traveling waves that go on during sleep, which is astonishing. Nobody ever really saw that before, but now- - If you were to ascribe one or two major functions to these traveling waves, what do you think they are accomplishing for us in sleep?

And by the way, are they associated with deep sleep, slow wave sleep, or with rapid eye movement sleep, or both? - This is non-REM sleep, this is a jargon, this is during intermediate- - Transition states. - Yeah, transition state. - Okay, our audience will probably keep up. They've heard a lot about slow wave sleep from me and Matt Walker from Rapid Eye Movement Sleep.

- Light, slow wave sleep, yeah. - And so what do these traveling waves accomplish for us? - Oh, okay, so in the case of the, they're called sleep spindles. They last, the waves last for about a second or two, and they travel, like I say, in a circle around the cortex.

And it's known that these spindles are important for consolidating experiences you've had during the day into your long-term memory storage. So it's a very important function, and if you take out, see, it's the hippocampus that is replaying the experiences. It's a part of the brain, it's very important for long-term memory.

If you don't have a hippocampus, you can't learn new things. That is to say, you can't remember what you did yesterday, or for that matter, even an hour earlier. But the hippocampus plays back your experiences, causes the sleep spindles now to knead that into the cortex. And it's important you do that right, 'cause you don't want to overwrite the existing knowledge you have.

You just want to basically incorporate the new experience into your existing knowledge base in an efficient way that doesn't interfere with what you already know. So that's an example of a very important function that these traveling ways have. - I'd like to take a quick break and acknowledge our sponsor, AG1.

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If you would like to try David, you can go to davidprotein.com/huberman. Again, the link is davidprotein.com/huberman. As I recall, there are one or two things that one can do in order to ensure that one gets sufficient sleep spindles at night and thereby incorporate this new knowledge. This was from the episode that we did with Gina Poe from UCLA, I believe, and others, including Matt Walker.

My recollection is that the number one thing is to make sure you get enough sleep at night so you experience enough of these spindles. And we're all familiar with the cognitive challenges, including memory challenges and learning challenges associated with lack of sleep, insufficient sleep. But the other was that there was some interesting relationship between daytime exercise and nighttime prevalence of sleep spindles.

Are you familiar with that literature? - Yes, oh yes. No, this is a fascinating literature, and it's all pointing the same direction, which is that we always neglect to appreciate the importance of sleep. I mean, obviously, you're refreshed when you wake up, but there's a lot of things happen.

It's not that your brain turns off. It's that it goes into a completely different state, and memory consolidation is just one of those things that happens when you fall asleep. And of course, there's dreams and so forth. We don't fully appreciate or understand exactly how all the different sleep stages work together.

But exercise is a particularly important part of getting the motor system tuned up. And it's thought that the REM, rapid eye movement sleep, may be involved in that. So that's yet another part of the sleep stages. You go through, you go back and forth between dream sleep and the slow-wave sleep, back and forth, back and forth during the night.

And then when you wake up, you're in the REM stage, more and more REM, more and more REM. But that's all observation. But as a scientist, what you want to do is perturb the system and see if you can maybe, if you had more sleep spindles, maybe you'd be able to remember things better.

So it turns out Sarah Mednick, who's at UC Irvine, did this fantastic experiment. So it turns out there's a drug called zolpidem, which goes by the name Ambien. You may have some experience with that. - I've never taken it, but I'm aware of what it is. People use it as a sleep aid.

- That's right. A lot of people take it in order to sleep, okay. Well, it turns out that it causes more sleep spindles. - Really? - It doubles the number of sleep spindles. If you take the drug, you take the drug after you've done the learning, right? You do the learning at night and then you take the drug and you have twice as many spindles.

You wake up in the morning, you can remember twice as much from what you learned. - And the memories are stable over time? - Yes. - It's in there. - Yes, yeah, no, it consolidates it. I mean, that's the point. - What's the downside of Ambien? - Okay, here's the downside.

Okay, so people who take the drug, say if you're going to Europe and you take it and then you sleep really soundly, but often you find yourself in the hotel room and you completely have no clue, you have no memory of how you got there. - I've had that experience without Ambien or any other drugs where I am very badly jet lagged.

- Yes. - And I wake up and for a few seconds, but what feels like eternity, I have no idea where I am. It's terrifying. - Well, that's another problem that you have with jet lag. Jet lag really screws things up. But this is something where it could be an hour.

You know, you took the train or you took a taxi or something and you're... So here, now this seems crazy. How could it be a way to improve learning and recall on one hand and then forgetfulness on the other hand? Well, it turns out what's important is (laughs) that when you take the drug, right?

In other words, it helps consolidate experiences you've had in the past before you took the drug, but it'll wipe out experiences you have in the future after you take the drug, right? (laughs) - Sorry, I'm not laughing. It must be a terrifying experience, but I'm laughing because, you know, there's some beautiful pharmacology and indeed some wonderfully useful pharmaceuticals out there.

You know, some people may cringe to hear me say that, but there are some very useful drugs out there that save lives and help people deal with symptoms, et cetera. Side effects are always a concern, but this particular drug profile, Ambien, that is, seems to reveal something perhaps even more important than the discussion about spindles or Ambien or even sleep, which is that you got to pay the piper somehow, as they say.

- That's right. - That you tweak one thing in the brain, something else, something else goes. You don't get anything for free. - That's a true, I think that this is something that is true, not just of drugs for the brain, but steroids for the body. - Sure. Yeah, I mean, steroids, even low-dose testosterone therapy, which is very popular nowadays, will give people more vigor, et cetera, but it is introducing a sort of second puberty, and puberty is perhaps the most rapid phase of aging in the entire lifespan.

Same thing with people who take growth hormone would be probably a better example, because certainly those therapies can be beneficial to people, but growth hormone gives people more vigor, but it accelerates aging. Look at the quality of skin that people have when they take growth hormone. It looks more aged.

They physically change, and I'm not for or against these things. It's highly individual, but I completely agree with you. I would also venture that with the growing interest in so-called nootropics, and people taking things like modafinil, not just for narcolepsy, daytime sleepiness, but also to enhance cognitive function, okay, maybe they can get away with doing that every once in a while for a deadline task or something, but my experience is that people who obsess over the use of pharmacology to achieve certain brain states pay in some other way.

- Absolutely. - Whether or not stimulants or sedatives or sleep drugs, and that behaviors will always prevail. Behaviors will always prevail as tools. - Yep, and one of the things about the way the body evolved is that it really has to balance a lot of things, and so with drugs, you're basically unbalancing it somehow, and the consequence is, as you point out, is that in order to make one part better, one part of your body, you sacrifice something else somewhere else.

- As long as we're talking about brain states and connectivity across areas, I want to ask a particular question, then I want to return to this issue about how best to learn, especially in kids, but also in adulthood. I've become very interested in and spent a lot of time with the literature and some guests on the topic of psychedelics.

Let's leave the discussion about LSD aside, because do you know why there aren't many studies of LSD? This is kind of a fun one. No one is expected to know the answer. - Well, it's against the law, I think. - Oh, but so is psilocybin or MDMA, and there are lots of studies going on about this.

- Oh, there are now, yeah, it's changed, but when I was growing up, as you know, it was against the law. - Right, so what I learned is that there are far fewer clinical trials exploring the use of LSD as a therapeutic, because with the exception of Switzerland, none of the researchers are willing to stay in the laboratory as long as it takes for the subject to get through an LSD journey, whereas psilocybin tends to be a shorter experience.

Okay, let's talk about psilocybin for a moment. My read of the data on psilocybin is that it's still open to question, but that some of the clinical trials show pretty significant recovery from major depression. It's pretty impressive, but if we just set that aside and say, okay, more needs to be worked out for safety, what is very clear from the brain imaging studies, the sort of before and after, resting state, task-related, et cetera, is that you get more resting state global connectivity, more areas talking to more areas than was the case prior to the use of the psychedelic.

And given the similarity of the psychedelic journey, and here specifically talking about psilocybin, to things like rapid eye movement, sleep, and things of that sort, I have a very simple question. Do you think that there's any real benefit to increasing brain-wide connectivity? To me, it seems a little bit haphazard, and yet the clinical data are promising, if nothing else, promising.

And so is what we're seeking in life as we acquire new knowledge, as we learn tennis or golf, or take up singing or what have you, as we go from childhood into the late stages of our life, that whole transition is what we're doing, increasing connectivity and communication between different brain areas?

Is that what the human experience is really about? Or is it that we're getting more modular? We're getting more segregated in terms of this area, talking to this area in this particular way. Feel free to explore this in any way that feels meaningful, or to say pass if it's not a good question.

- No, it's a great question. I mean, you have all these great questions, and we don't have complete answers yet, but specifically with regard to connectivity, if you look at what happens in an infant's brain during the first two years, there's a tremendous amount of new synapses being formed.

This is your area, by the way. You know more about this than I do. - That's true. - But then you prune them, right? Then the second phase is that you overabundant synapses, and now what you wanna do is to prune them. Why would you wanna do that? Well, you know, synapses are expensive.

It takes a lot of energy to activate all of the neurons, and the synapses especially, 'cause there's the turnover of the neurotransmitter. And so what you wanna do is to reduce the amount of energy and only use those synapses that have been proven to be the most important, right?

Now, unfortunately, as you get older, the pruning slows down, but doesn't go away. And so the cortex thins and so forth. So I think it goes in the opposite direction. I think that as you get older, you're losing connectivity. But interestingly, you retain the old memories. The old memories are really rock solid, 'cause they were put in when you were young.

- Yeah, the foundation. - The foundation upon which everything else is built. But it's not totally one way, in the sense that even as an adult, as you know, you can learn new things, maybe not as quickly. By the way, this is one of the things that surprised me.

So Barbara and I have looked at the people who really were the benefit of the most. It turns out that the peak of the demographic is 25 to 35. - Barbara? - Oakley, Oakley. Yeah, she's really the mastermind. She's a fabulous educator and background in engineering. But what's going on?

So it turns out, we aimed our MOOC at kids in high school and college, because that's their business. They go every day and they go into work. They have to learn, right? That's their business. But in fact, very few of the students are actually, you know, they weren't taking the course.

Why should they? They spent all day in the class, right? Why do they want to take another class? - So this is the learning to learn class. - Learning how to learn. - Okay, so you did this with Barbara. - So I did with Barbara, and now 25 to 35, we have this huge peak, huge.

So what's going on? Here's what's going on. It's very interesting. So you're 25, you've gone to college. Half the people, by the way, who take the course went to college, right? So it's not like, you know, filling in for college. This is like topping it off. But you're in a workforce.

You have to learn new skill. Maybe you have mortgage. Maybe you have children, right? You can't afford to go off and take a course or get another degree. So you take a MOOC and you discover, you know, I'm not quite as agile as I used to be in terms of learning, but it turns out with our course, you can boost your learning.

And so that even though you're not as, your brain isn't learning as quickly, you can do it more efficiently. - This is amazing. I want to take this course. I will take this course. What sort of time commitment is the course? You already pointed out that it's zero cost, which is amazing.

- Okay, so it's bite-sized videos lasting about 10 minutes each. And there's about 50 or 60 over a course of one month. - And are you tested? Are you self-tested? - Yeah, there are tests. There are quizzes. There are tests at the end. And there are forums where you can go and talk to other students.

You have questions. We have TAs. No, it's- - And anyone can do this? - Anyone in the world. In fact, we have people in India, housewives, who say, "Thank you, thank you, thank you," because I could have never learned about how to be a better learner. And I wish I had known this when I was going to school.

- Why do more people not know about this learning to learn course? Although, as people know, if I get really excited about it or about anything, I'm never going to shut up about it. But I'm going to take the course first because I want to understand the guts of it.

- You'll enjoy it. We have like 98% approval, which is phenomenal. It's sticky. Is it math, vocabulary? - No, no math. We're not teaching anything specific. We're not trying to give you knowledge. We're trying to tell you how to acquire knowledge and how to do that, how to deal with exam anxiety, for example, or how to, you know, we all procrastinate, right?

We put things off. - Nah, no, I'm kidding. We all procrastinate. - How to avoid that. We teach you how to avoid that. - Fantastic. Okay, I'm going to skip back a little bit now with the intention of double-clicking on this learning to learn thing. You pointed out that in particular in California, but elsewhere as well, there isn't as much procedural practice-based learning anymore.

I'm going to play devil's advocate here. And I'm going to point out that this is not what I actually believe. But, you know, when I was growing up, you had to do your times tables and your division, and, you know, and then your fractions and your exponents, and, you know, and they build on one another.

And then at some point, you know, you take courses where you might need it like a graphing calculator. To some people, they can be like, what is this? But the point being that there were a number of things that you had to learn to implement functions and you learn by doing, you learn by doing.

Likewise, in physics class, we, you know, we were attaching things to strings and for macromechanics and learning that stuff. Okay, and learning from the chalkboard lectures. I can see the value of both, certainly. And you explained that the brain needs both to really understand knowledge and how to implement and back and forth.

But nowadays, you know, you'll hear the argument, well, why should somebody learn how to read a paper map unless it's the only thing available because you have Google Maps? Or if they want to do a calculation, they just put it into the top bar function on the internet and boom, out comes the answer.

So there is a world where certain skills are no longer required. And one could argue that the brain space and activity and time and energy in particular could be devoted to learning new forms of knowledge that are going to be more practical in the school and workforce going forward.

- So how do we reconcile these things? I mean, I'm of the belief that the brain is doing math and you and I agree it's electrical signals and chemical signals and it's doing math and it's running algorithms. I think you convinced us of that, certainly. But how are we to discern what we need to learn versus what we don't need to learn in terms of building a brain that's capable of learning the maximum number of things or even enough things so that we can go into this very uncertain future?

Because as far as you know, and I know neither of us have a crystal ball. So what is essential to learn? And for those of us that didn't learn certain things in our formal education, what should we learn how to learn? - Well, this is generational. Okay. So technologies provide us with tools.

You mentioned the calculator, right? Well, a calculator didn't eliminate the education you need to get in math, but it made certain things easier. It made it possible for you to do more things and more accurately. However, interestingly, students in my class often come up with answers that are off by eight orders of magnitude.

And that's a huge amount, right? It's clear that they didn't key in the calculator properly, but they didn't recognize that it was a very far, it was a completely way off the beam because they didn't have a good feeling for the numbers. They don't have a good sense of exactly how big it should have been, order of magnitude, basic understanding.

So it's kind of a, the benefit is that you can do things faster, better, but then you also lose some of your intuition if you don't have the procedural system in place. - And think about a kid that wants to be a musician who uses AI to write a song about a bad breakup that then is kind of recovered when they find new love.

And I'm guessing that you could do this today and get a pretty good song out of AI, but would you call that kid a songwriter or a musician? On the face of it, yeah, the AI is helping. And then you'd say, well, that's not the same as sitting down with a guitar and trying out different chords and feeling the intonation in their voice.

But I'm guessing that for people that were on the electric guitar, they were criticizing people on the acoustic guitar. You know, so we have this generational thing where we look back and say, that's not the real thing. You need to get the, so what are the key fundamentals is really a critical question.

- Okay, so I'm going to come back to that because this is how, the way you put it at the beginning had to do with whether your, how your brain is allocating resources, okay? So when you're younger, you can take in things. Your brain is more malleable. For example, how good are you on social media?

- Well, I do all my own Instagram and Twitter and those accounts have grown in proportion to the amount of time I've been doing it. So yeah, I would say pretty good. I mean, I'm not the biggest account on social media, but for a science health account, we're doing okay.

I'm thanks to the audience. - Well, this speaks well for the fact that you've managed to break, you know, to go beyond the generation gap because- - I can type with my thumbs, Terry. - Okay, there you go. That's a manual skill that you've learned. - That's a new phenomenon in human evolution.

- I couldn't believe it. I saw people doing that and now I can do it too. But the thing is that if you learn how to do that early in life, you're much more good at it. You can move your thumbs much more quickly. Also, you can have many more, you know, tweets going and we're not, what are they called now?

They're not called tweets. - So on X, I think they still call them tweets because you can't, it's hard to verb the letter X. Elon didn't think of that one. I like X 'cause it's cool. It's kind of punk and it's got a black kind of format and it fits with kind of the, you know, the engineer, like black X, you know, and this kind of thing.

But yeah, we'll still call them tweets. - Okay, we'll call them tweets. Okay, that's good. But you know, I walk across campus and I see everybody, like half the people are tweeting or, you know, they're doing something with their cell phone. They're, I mean, it's unbelievable. - And you have beautiful sunsets at the Salk Institute.

We'll put a link to one of them. I mean, it is truly spectacular, awe-inspiring to see a sunset at the Salk Institute. - Every day is different. - And everyone's on their phones these days, sad. - And you know, they're looking down at their phone and they're walking along, even people who are skateboarding, unbelievable.

I mean, you know, it's amazing what a human being can do, you know, when they learn, get into something. But what happens is the younger generation picks up whatever technology it is and the brain gets really good at it. And you can pick it up later, but you're not quite as agile, not quite as maybe obsessive.

- It fatigues me, I will point this out, that doing anything on my phone feels fatiguing in a way that reading a paper book or even just writing on a laptop or a desktop computer is fundamentally different. I can do that for many hours. If I'm on social media for more than a few minutes, I can literally feel the energy draining out of my body.

- Interesting. - I would, I could do sprints or deadlifts for hours and not feel the kind of fatigue that I feel from doing social media. - So, you know, this is fascinating. I'd like to know what's going on in your brain. Why is it, and also I'd like to know from younger people whether they have the same.

I think not. I think my guess is that they don't feel fatigued because they got into this early enough. And this is actually a very, very, I think that it has a lot to do with the foundation you put into your brain. In other words, things that you learn when you're really young are foundational and they make things easier, some things easier.

- Yeah, I spent a lot of time in my room as a kid, either playing with Legos or action figures or building fish tanks or reading about fish. I tended to read about things and then do a lot of procedural-based activities. You know, I would read skateboard magazines and skateboard.

I was never one to really just watch a sport and not play it. So, you know, bridging across these things. So social media, to me, feels like an energy sink. But of course, I love the opportunity to be able to teach to people and learn from people at such scale.

But at an energetic level, I feel like I don't have a foundation for it. It's like, I'm trying to like jerry-rig my cognition into doing something that it wasn't designed to do. - Well, there you go. And it's because you don't have the foundation. You didn't do it when you were younger.

And now you have to sort of use the cognitive powers to do a lot of what was being done now in a younger person procedurally. - I'd like to take a quick break and thank one of our sponsors, Element. Element is an electrolyte drink that has everything you need and nothing you don't.

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My book, "Chad G.D.P. and the Future of AI," I went through and I looked at other people's experiences with Chad G.D.P. I just wanted to know what people were thinking, and I came across, it was an article, I think it was "The New York Times," of a technical writer who decided she would spend one month using it to help her write things, her articles.

And she said that when she started out, you know, at the end of the day, she was drained, completely drained. And it was like, you know, working on a machine, you know, like a tractor or something. You know, you're struggling, struggling, struggling to get it to work. And then she started, said, "Well, wait a second.

"You know, what if I treat it like a human being? "What if I'm polite instead of, you know, being curt?" So she said, "Suddenly, I started getting better answers "by being polite and, you know, back and forth "the way you with a human, you know." - So saying, "Could you please give me information "about so-and-so?" - Yeah, "Please, "I'm really having trouble." No, you know, that answer you gave me was fabulous, is exactly what I was looking for.

And, you know, now I need you to go on to the next part and help me with that, too. In other words, the way you talk to a human, right, if you're an assistant, that you're-- - Or is it that she was talking to the AI to chat GPT, it sounds like in this case, in the way that her brain was familiar with asking questions to a human?

In other words, so is the AI learning her and therefore giving her the sorts of answers that are more facile for her to integrate with? - I think it's both. First of all, the chat GDP is mirroring your, the way you treat it, it will mirror that back. You treat it like a machine, it will treat you like a machine, okay?

'Cause that's what it's good at. But here's the surprise. Surprise is, she said, "Once I started treating it "like a human, at the end of the day, "I wasn't fatigued anymore." Why? Well, it turns out that all your life, you interact with humans in a certain way, and your brain is wired to do that, and it doesn't take any effort.

And so by treating the chat GDP as if it were a human, you're taking advantage of all the brain circuits in your brain. - This is incredible, and I'll tell you why, because I think many people, not just me, but many people really enjoy social media, learn from it.

I mean, yesterday I learned a few things that I thought were just fascinating about how we perceive our own identity, according to whether or not we're filtering it through the responses of others, or whether or not we take a couple of minutes and really just sit and think about how we actually feel about ourselves.

Very interesting ideas about locus of self-perception and things like that. I also looked at a really cool video of a baby raccoon popping bubbles while standing on its hind limbs. And that was really cool, and social media could provide me both those things within a series of minutes. And I was thinking to myself, this is crazy, right?

The raccoon is kind of trivial, but it delighted me, and that's not trivial. - There you go, yeah. - So, but here's the question. Could it be that one of the detrimental aspects of social media is that if we're complimenting one another, or if we are giving hearts, or we're giving thumbs down, or we're in an argument with somebody, or we're doing a clap back, or they're clapping back on us, or dunking, as it's called on X, that it isn't necessarily the way that we learned to argue.

It's not necessarily the way that we learned to engage in healthy dispute. And so, as a consequence, it feels like, and this is my experience, that certain online interactions feel really good, and others feel like they kind of grate on me, like because there's almost like an action step that isn't allowed, like you can't fully explain yourself, or understand the other person.

And I am somebody who believes in the power of real face-to-face dialogue, or at least on the phone dialogue. And I feel the same way about text messaging. I hate text messaging. When text messaging first came out, I remember thinking, I was not a kid that passed notes in class.

This feels like passing notes in class. In fact, this whole text messaging thing is beneath me. That's how I felt. And over the years, of course, I became a text messenger. And it's very useful for certain things, be there in five minutes, running a few minutes late. In my case, that's a common one.

But I think this notion of what grates on us, and as it relates to whether or not it matches our childhood developed template of how our brain works, is really key, because it touches on something that I definitely wanna talk about today that I know you've worked on quite a bit, which is this concept of energy.

What we're talking about here is energy, not woo, biology, woo, science, wellness, energy. We're talking about, we only have a finite amount of energy. And years ago, the great Ben Barris sadly passed away. Our former colleague and my postdoc advisor came to me one day in the hallway and he stopped me and he said, he called me Andy, like you do.

And he said, Andy, how come we get such a rundown of energy as we get older? Why am I more tired today than I was 10 years ago? I was like, I don't know, how are you sleeping? He's like, I'm sleeping fine. Ben never slept much in the first place, but he had a ton of energy.

And I thought to myself, I don't know. Like, what is this energy thing that we're talking about? I wanna make sure that we close the hatch on this notion of a template neural system that then you either find experiences invigorating or depleting. I wanna make sure we close the hatch on that, but I wanna make sure that we relate it at some point to this idea of energy.

And why is it that with each passing year of our life, we seem to have less of it? - You know, you ask these great questions. I wish that I had great answers. - Well, so far you really do have great answers. They're certainly novel to me in the sense that I've not heard answers of this sort.

So there's a tremendous amount of learning for me today and I know for the audience. - But let's say somebody is 20 years old versus 50 years old versus what should they do? I mean, we need to integrate with the modern world. We also need to relate across generations.

- Oh yeah, no, this is true, this is true. - People aren't retiring as much, they're living longer. Birth rates are down, but we have to get all get along as they say. - So, you know, it is interesting. I think it's true that we all, as we get older, have less of the, you know, the vigor, if I could use a somewhat different word from energy, we'll come back to that.

But I think there are some who manage to keep an active life. And here's something that, again, in our MOOC, we really emphasize. - Could you explain a MOOC? I think most people won't know what a MOOC is, just for their sake. - Okay, this is, they've been around for about, actually started at Stanford, Andrew Ng and Daphna Koller.

So they have a company called Coursera. And what happens is that you get professors, and in fact, anybody who has knowledge or, you know, professional expertise, to give lectures that are available to anybody in the world who have access to the internet. And, you know, it could, this is like probably tens of thousands now.

Any specialty, history, science, music, you know, you name it, there's somebody who's done, you know, who's an expert on that and wants to tell you, because they're excited about what they're doing. Okay, so, you know, what we wanted to do was to help people with learning. And so part of the problem is that it gets more difficult.

It takes more effort as you get older. - It depletes your vigor more, if we're gonna stay with this language of energy and vigor. - Yeah, yeah, that's right. So let's actually use the word energy. As you know, in the cell, there is a physical power plant called the mitochondrion, which is supplying us with ATP, which is the coin of the realm for the cell to be able to operate all of its machinery, right?

So, and so one of the things that happens when you get older is that your mitochondrial run down. - You have fewer of them and they're less efficient. - That's right, they're less efficient. And actually drugs can do that to you too. They can harm mitochondria. - Or recreational drugs.

- No, the drugs you take for illness. I'm not sure about recreational drugs, but I know it's the case that there are a lot of drugs that people take 'cause they have to, but the other thing, and this is something, that's the bad news, here's the good news. The good news is that you can replenish your energy by exercise.

That exercise is the best drug you could ever take. It's the cheapest drug you could ever take. That can help every organ in your body. It helps obviously your heart. It helps your brain, it rejuvenates your brain. It helps your immune system. Every single organ system in the body benefits from a regular exercise.

I run on the beach every day at the Salk Institute. I can, and I also, it's on a mesa, 340 foot above. So I go down every day and then I climb up the cliff. - Yeah, those steps down to Black's Beach, they're a good workout. - They are, they are, and so this is something that has kept me active, and I do hiking.

I went hiking in the Alps last fall. So this is, in September, so this is, I think, something that people really ought to realize is that it's like putting away reserves of energy for when you get older. The more you put away, the better off you are. Here's something else.

Okay, now this is jumping now to Alzheimer's. So a study that was done in China many, many years ago, when I first came to La Jolla, San Diego, I heard this from the, it was the head of the Alzheimer's program. He had done a study in China on onset, and they went and they had three populations.

They had peasants who had almost no education, then they had another group that had high school education, and then people who were, you know, advanced education. So it turns out that the onset of Alzheimer's was earlier for the people who had no education, and it was the latest for the people who had the most education.

Now this is interesting, isn't it? 'Cause it's, and presumably the genes aren't that different, right? I mean, they're all Chinese. So one possibility, and obviously we don't really know why, but one possibility is that the more you exercise your brain with education, the more reserve you have later in life.

- I believe in the notion, and I don't have a better word for it, maybe you do, or a phrase for it, is of kind of a cognitive velocity. You know, I sometimes will play with this. I'll read slowly, or I'll see where my default pace of reading is at a given time of day, and then I'll intentionally try and read a little bit faster while also trying to retain the knowledge I'm reading.

So I'm not just reading the words, I'm trying to absorb the information. And you can feel the energetic demand of that. And then I'll play with it. I'll kind of back off a little bit, and then I'll go forward. And I try and find the sweet spot where I'm not reading at the pace that is reflexive, but just a little bit quicker while also trying to retain the information.

And I learned this when I had a lot of catching up to do at one phase of my educational career. Fortunately, it was pretty early, and I was able to catch up on most things. You know, occasionally things slip through, and I have to go back and learn how to learn, you know?

And if I get anything wrong on the internet, they sure as heck point it out, and then we go back and learn. And guess what? I'd never forget that because punishment, social punishment is a great signal. So thank you all for keeping me learning. But I picked that up from my experience of trying to get good at things like skateboarding or soccer when I was younger.

There's a certain thing that happens when skateboarding, that was my sport growing up, where it's actually easier to learn something going faster. You know, most kids try and learn how to ollie and kickflip standing in the living room on the carpet. That's the worst way to learn how to do it.

It's all easier going a bit faster than you're comfortable. It's also the case that if you're not paying attention, you can get hurt. It's also the case that if you pay too much cognitive attention, you can't perform the motor movements. So there's this sweet spot that eventually I was able to translate into an understanding of when I sit down to read a paper or a news article, or even listen to a podcast, there's a pace of the person's voice, and then I'll adjust the rate of the audio, where I have to engage cognitively, and I know I'm in a mode of retaining the information and learning.

Whereas if I just go with my reflexive pace, it's rare that I'm in that perfect zone. So I point this out because perhaps it will be useful to people. I don't know if it's incorporated into your learning how to learn course, but I do think that there is something, which I call kind of cognitive velocity, which is ideal for learning versus kind of leisurely scrolling.

And this is why I think that social media is detrimental. I think that we train our brain basically to be slow, passive, and multi-context cycling through. And unless something is very high salience, it kind of makes us kind of fat and lazy, forgive the language, but I'm going to be blunt here, fat and lazy cognitively, unless we make it a point to also engage learning.

And my guess is it's tapping into this mitochondrial system. Very likely, that's one part of it. By the way, the way that you've adjusted the speed is very interesting because it turns out that stress, everybody thinks stress is bad, but no, it turns out stress that is transient, that is only for a limited amount of time that you control is good for you.

It's good for your brain. It's good for your body. I run intervals on the beach, just the way that you do cognitive intervals when you're reading. In other words, I run like hell for about 10 seconds. And then I go to a jog and I run like hell for another 10 seconds.

And it's pushing your body into that extra gear that helps the muscles. The muscles need to know that this is what they've got to put out. And that's where you gain muscle mass, not from just doing the same running pace every day. - Well, your intellectual and physical vigor is undeniable.

I've known you a long time. You've always had a slight forward center of mass in your intellect. And even the speed at which you walk, Terry, dare I say, for a Californian, you're a quick walker. - Okay. - Yeah, so that's a compliment, by the way. East coasters know what I'm talking about.

And Californians would be like, you know, why not slow down? The reason to not slow down too much for too long is that these mitochondrial systems, the energy of the brain and body, as you point out, are very linked. And I do think that below a certain threshold, it makes it very hard to come back.

Just like below a certain threshold, it's hard to exercise without getting very depleted or even injured, that we need to maintain this. So perhaps now would be a good time to close the hatch on this issue of how to teach young people. Everyone should take this learning to learn course as a free resource, amazing.

As it relates to AI, do you think that young people and older people now, I'm 49, so I'll put myself in the older bracket, should be learning how to use AI? - They are already learning how to use AI. And again, it's just like new technology comes along, who picks it up first?

It's the younger people. And it's astonishing. You know, they're using it a lot more than I am. You know, I use it almost every day, but I know a lot of students who basically, and by the way, it's like any other tool. It's a tool that you need to know how to use it.

- Where do you suggest people start? So I have started using Claude AI. This was suggested to me by somebody expert in AI as an alternative to ChatGPT. I don't have anything against ChatGPT, but I'll tell you, I really like the aesthetic of Claude AI. It's a bit of a softer beige aesthetic.

It feels kind of Apple-like. I like the Apple brand and it gives me answers. Maybe it's the font. Maybe it's the feel. Maybe this goes back to the example you used earlier where I like Claude AI and I'm a big fan of it. And they don't pay me to say this.

I have never met them. I have no relationship to them, except that it gives me answers in a bullet pointed format that feels very aesthetically easy to transfer that information into my brain or onto a page. So I like Claude AI, use ChatGPT. How should people start to explore AI for sake of getting smarter, learning knowledge, just for the sake of knowledge, having fun with it?

What's the best way to do that? - Well, I think exactly what you did, which is there's now dozens and dozens of different chatbots out there and different people will feel comfortable with one or the other. ChatGP is the first, so that's why it's kind of taken over a lot of the cognitive space, right?

It's become like Kleenex, right? That word, that was why I used it as the first word in my book, because it's iconic. But some of them, I have to say that, for example, there are some that are really much better at math than others. - Such as? - Google's Gemini recently did some fine tuning with what's called a chain of reasoning.

And when you reason, you go through a sequence of steps. And when you solve a math problem, you go through a sequence of steps of first finding out what's missing and then adding that. And it went from 20% correct to 80, right? On those problems. - And as people hear that, they probably think, "Well, that means 20% wrong still." But could you imagine any human or panel of humans behind a wall where if you asked it a question and then another question and another question, that it would give you back better than 80% accurate information in a matter of seconds?

- So I think we are being perhaps a little bit unfair to compare these large language models to the best humans rather than the average human, right? As you said, most people couldn't pass the LSAT, the loss test to get into law school, or MCAT, the test to get into medical school.

And JETGPT has. - Is there a world now where we take the existing AI, LLMs, these computers, basically, that can learn like a collection of human brains and send that somehow into the future, right? Give them an imagined future, okay? Could we give them outcome A and outcome B and let them forage into future states that we are not yet able to get to and then harness that knowledge and explore the two different outcomes?

- I think that's perhaps the better question in some sense, because we can't travel back in time, but we can perhaps travel into the future with AI if you provide it different scenarios. And you say, unlike a panel of people, panel of experts, medical experts, or space travel experts, or sea travel experts, you can't say, hey, you know what?

Don't sleep tonight. You're just gonna work for the next 48 hours. In fact, you're gonna work for the next three weeks or three months. And you know what? You're not gonna do anything else. You're not gonna pay attention to your health. You're not gonna do anything else, but you can take a large language model and you can say, just forage for knowledge under the following different scenarios, and then have that fleet of large language models come back and give us the information like, I don't know, tomorrow.

- Okay, so I've lived through this myself. Back in the 1980s, I was just starting my career and I was one of the pioneers in developing learning algorithms for neural network models. Jeff Hinton and I collaborated together on something called the Boson machine, and he actually won a Nobel Prize for this recently.

- Yeah, just this year. - Yeah, he's one of my best friends. Brilliant, and he well-deserved it for not just the Boson machine, but all the work he's done since then on machine learning and then back propagation and so forth. But back then, Jeff and I had this view of the future.

AI was dominated by symbol processing, rules, logic, right? Writing computer programs. For every problem, you need a different computer program, and it was very human resource intensive to write programs so that it was very, very slow going. And they never actually got there. They never wrote a program for vision, for example, even though the computer vision community really worked hard for a long time.

But we had this view of the future. We had this view that nature has solved these problems, and there's existence proof that you can solve the vision problem. Look, every animal can see, even insects, right? Come on, let's figure out how they did it. Maybe we can help by following up on nature.

We can actually, again, going back to algorithms, I was telling you, and so in the case of the brain, what makes it different from a digital computer, digital computers basically can run any program, but a fly brain, for example, only runs the program that a special purpose hardware allows it to run.

- Not much neuroplasticity. - There's enough there, just enough habituation and so forth so that it can survive, and this is- - Survive 24 hours. I'm not trying to be disparaging to the fly biologists, but when I think of neuroplasticity, I think of the magnificent neuroplasticity of the human brain to customize to a world of experience.

- I agree. - When I think about a fly, I think about a really cool set of neural circuits that work really well to avoid getting swatted, to eating, and to reproducing, and not a whole lot else. They don't really build technology. They might have interesting relationships, but who knows, who cares?

It's just sort of like, it's not that it doesn't matter. It's just a question of the lack of plasticity makes them kind of a meh species. - Okay, I can see I've pressed your button here. - No, no, no, no, I love fly biology. They taught us about algorithms for direction selectivity in the visual system.

Oh, no, no, I love the Drosophila biology. I just think that the lack of neuroplasticity reveals a certain key limitation, and the reason we're the curators of the earth is 'cause we have so much plasticity. - Of course, of course, but you have to, one step at a time, nature first has to be able to create creatures that can survive, and then their brains get bigger as the environment gets more complex, and here we are, but the key is that it turns out that certain algorithms in the fly brain are present in our brain, like conditioning, classical conditioning.

You can classical condition a fly in terms of training it to, when you give it a reward, it will produce the same action, right? This is like conditioned behavior, and that algorithm that I told you about that is in your value function, right? Temporal difference learning, that algorithm is in the fly brain, it's in your brain, so we can learn about learning from many species.

- I was just having a little fun poking at the fly biologists. I actually think Drosophila has done a great deal, as has honeybee biology. For instance, if you give caffeine to bees on particular flowers, they'll actually try and pollinate those flowers more, because they actually like the feeling of being caffeinated.

There's a bad pun about a buzz here, but I'm not gonna make that pun, 'cause everyone's done it before. - Right, right. - No, I fully absorb and agree with the value of studying simpler organisms to find the algorithms. - Right, that's where we are right now. But now, just go into the future.

Now, I'm telling the story about where we were. We were predicting the future. We were saying, this is an alternative to traditional AI. We were not taken seriously. Everybody was, experts said, "No, no, write programs, "write programs." They were getting all the resources, the grants, the jobs, and we were just like the little furry mammals under the feet of these dinosaurs, right, in retrospect.

- I love the analogy. - But here's the point. - But the dinosaurs died off. - But the point I'm making is that it's possible for our brain to make these extrapolations into the future. Why not AI versions of brains? Why not? I think your idea is a great one.

- Yeah, I mean, the reason I'm excited about AI, and increasingly so across the course of this conversation, is because there are very few opportunities to forage information at such large scale and around the circadian clock. I mean, if there's one thing that we are truly a slave to as humans is the circadian biology.

You got to sleep sooner or later. And even if you don't, your cognition really waxes and wanes across the circadian cycle. And if you don't, you're going to die early. We know this. Computers can work, work, work. Sure, you got to power them. There's the cooling thing. There are a bunch of things related to that, but that's trackable.

So computers can work, work, work. And the idea that they can provide a portal into the future and that they can just bring it back so we can take a look-see. I'm not saying we have to implement their advice, but to be able to send a panel of diverse, computationally diverse, experientially diverse AI experts into the future and bring us back a panel of potential routes to take, to me is so exciting.

Maybe a good example would be like treatments for schizophrenia. This is an area that I want to make sure that we talk about. I grew up learning as a neuroscience student that schizophrenia was somehow a disruption of the dopamine system, because if you give neuroleptic drugs that block dopamine receptors, that you get some improvement in the motor symptoms and some of the hallucinations, et cetera.

You now also have people who say, "No, that's not really the basis of schizophrenia. "I'd love your thoughts." And you have incredible work from people like Chris Palmer at Harvard. And we even have a department at Stanford now focusing, we even have people at Stanford now focusing on what Chris really founded as a field, which is metabolic psychiatry.

The idea that, who could imagine, I'm being sarcastic here, what you eat impacts your mitochondria, how you exercise impacts your mitochondria, mitochondria impacts brain function. And lo and behold, metabolic health of the brain and body impacts schizophrenia symptoms. And he's looked at ways that people can use ketogenic diet, maybe not to cure, but to treat, and in some cases, maybe even cure schizophrenia.

So here we are at this place where we still don't have a "cure" for schizophrenia, but you could send LLMs into the future and start to forage the most likely, all of the data in those fields, probably could do that in an hour, plus come up with a bunch of hypothesized different positive and negative result clinical trials that don't even exist yet.

10,000 subjects in Scandinavia who go on ketogenic diet, who have a certain level of susceptibility to schizophrenia based on what we know from twin studies, things that never, ever, ever would be possible to do in an afternoon, maybe even in a year, there's isn't funding, there isn't. And boom, get the answers back and let them present us those answers.

And then you say, "Well, it's artificial." But so are human brains coming up with these experiments. So to me, I'm starting to realize that it's not that we have to implement everything that AI tells us or offers us, but it sure as hell gives us a great window into what might be happening or is likely to happen.

- Specifically for schizophrenia, I'm pretty sure that if we had these large language models 20 years ago, we would have known back then that ketamine would have been a really good drug to try to help these people. - Tell us about the relationship between ketamine and schizophrenia. - Okay.

- Because I think a lot of people, and maybe you could define schizophrenia, even though most people think about people hearing voices and psychosis, like there's a bit more to it that maybe we just can't bring out. - Okay, so one of the things now that we know, see, the problem is that if you look at the end point, that doesn't tell you what started the problem.

It started early in development. Schizophrenia is something that appears when late adolescence, early adulthood, but it actually is already a problem, a genetic problem from the get-go. - So what is the concordance in identical twins? Meaning if you have one identical twin, if you have identical twins in the womb, and one is destined to be full-blown schizophrenic, what's the probability the other will be?

- So here's the experiment. Okay, this has been replicated many, many times, in mice, I should say. Oh no, actually, okay, let me start with a human. Okay, so ketamine is, for a long time, and it still is, a party drug, special K. - I've never taken it, but this is what I hear.

- I haven't either. I don't know. - It's a dissociative anesthetic, right? - But I'll tell you what happens, 'cause I've talked to these people who've done this. You take ketamine, sub-anesthetic, by the way, it's an anesthetic. It's given to children. It's a pretty good anesthetic, and it's also used in veterinary medicine.

But in any case, you give it to, you take young adults, here's what they experience. They experience out-of-body experience. They have this wonderful feeling of energy, and they're very, it's a high, but it's a very unusual high. Now, if they just go and have one experience, but if they have two, they party two days in a row, a lot of them come into the emergency room, and here's what the symptoms are.

Full-blown psychosis, full-blown. We're talking about indistinguishable from a schizophrenic break. - So auditory hallucinations. - Yeah, auditory hallucinations, paranoia, very, very advanced. We say that, my God, this person here is really, has become a schizophrenic, and this is really, like you say, the symptoms are the same. However, if you isolate them for a couple days, they'll come back, right?

So it means that schizophrenia can induce, I mean, sorry, ketamine can induce a form of schizophrenia, psychosis, temporarily, not permanently, fortunately. Okay, so what does it attack? Okay, and there's another literature on this. It turns out that it binds to a form of receptor, a glutamate receptor, called NMDA receptors, which are very important, by the way, for learning and memory, but we know the target, and we also know what the acute outcome is, that it reduces the strength of the inhibitory circuit, the interneurons that use inhibitory transmitters, the enzyme that creates the inhibitory transmitter is downregulated, and what does that do?

It means that there's more excitation, and what does that mean, when there's more excitation? It means that there's more activity in the cortex, and there's actually much more vigor, and you start becoming crazy, right, if it's too much activity. So this is interesting. So this is telling us, I think, that we should be thinking about, and now there's a whole field now in psychiatry that has to do with the glutamate hypothesis for the first, where the actual imbalance first occurs.

It's an imbalance between the excitatory and inhibitory systems that are in the cortex that keep you in balance. - And NMDA and methyldiaspartate receptors are glutamate receptors. - Yes, they are glutamate. - They're one class. - That's one class, that's right. Okay, so now, here is a hypothesis for why ketamine might be good for depression.

People are taking it now who are depressed, right? So here you have a drug that causes overexcitation, and here you have a person who is underexcited. Depression is associated with lower excitatory activity in some parts of the cortex. Well, if you titrate it, you can come back into balance, right?

So what you do is you fight depression with schizophrenia, a touch of schizophrenia. Now, you have to keep giving. I think once every three weeks, they have to have a new dose of ketamine, but it's helped an enormous number of people with very, very severe clinical depression. So as we learn more about the mechanisms underlying some of these disorders, the better we are going to be at extrapolating and coming up with some solutions, at least to prevent it from getting worse.

By the way, I'm pretty sure that the large language models could have figured this out long ago. - So in an attempt to understand how we might be able to leverage these large language models now, how would we have used these large language models long ago? Let's say you had 2024 AI technology in 19, to have fun here, 1998, the year that I started graduate school.

- Right. - At that time, it was like the dopamine hypothesis is schizophrenia was in every textbook. There was a little bit about glutamate, perhaps, but it was all about dopamine. So how would the large language models have discovered this? Ketamine was known as a drug. Ketamine, by the way, is very similar to PCP, fencyclidine, which also binds the NMD receptor.

So how would- - Which is also a part of- - Which is also, yeah, not one I recommend, nor ketamine. Frankly, I don't recommend any recreational drugs, but I'm not a recreational drug guy. But what would those large language models do if they, so you've got 2024 technology placed into 1998.

They're foraging for existing knowledge, but then are they able to make predictions? Like, hey, this stuff is gonna turn out to be wrong, or hey, this stuff- - Okay, okay, you know, this is all very, very speculative. And really, we can begin actually to see this happening now. So I have a colleague at the Salk Institute, Rusty Gage, very distinguished neuroscientist.

And he discovered that there are new neurons being born in the hippocampus, right? Which is something, in adults, which is something that in a textbook says that doesn't happen, right? - Yeah, that was around 1998 that Rusty did that. - Yeah, yeah, right, that's right. And I actually have a paper with him where we tested LTP, long-term potentiation, actually, the effects of exercise on neurogenesis.

- Exercise increases neurogenesis. - Yeah, it increases the cells, that increases neurogenesis, and also the cells that are active become part of the circuit. More cells become integrated. - And this is true in humans as well, right? - Yeah, and there was some cancer drug that was given that, you know, that they showed that there were new cells that they were able to, later in post-mortem, to actually see that they were born in the adult.

Okay, so here we are, okay, in 1998. And the question is, can you jump? Can you jump into the future? Okay, so Rusty, we were at, you know, happened to talk about this issue about, you know, he's using these large language models now for his research. I said, "Oh, wow, how do you use it?" And he said, "We use it as an idea pump." What do you mean, idea pump?

Well, you know, we give it all of the experiments that we've done, and we have it, you know, the literature, it's access to the literature and so forth, and we ask it for ideas for new experiments. - Oh, I love it, I love it. I was on a plane where I sat next to a guy that works at Google, and he's one of the main people there in terms of voice-to-text, and text-to-voice software.

And he showed me something, I'll provide a link to it, 'cause it's another one of these open resource things. And I'm not super techie, I'm not like the, I don't get an F in technology, I don't get an A+. I'm kind of in the middle, so I think I'm pretty representative of the average listener for this podcast, presumably.

What he showed me is that you can take, you open up this website, and you can take PDFs, or you take URLs, so websites, website addresses, and you just place them in the margin. You literally just drag and drop them there. And then you can ask questions, and the AI will generate answers that are based on the content of whatever you put into this margin, those PDFs, those websites.

And the cool thing is, it references them, so you know which article it came from. And then you can start asking it more sophisticated questions, like, in the two examples of the effects of a drug, one being very strong, and one being very weak, which of these papers do you think is more rigorous, based on subject number, but also kind of the strength of the findings?

Pretty vague thing. Strength of findings is pretty vague, right? Anyone that argues those are weak findings, those aren't enough subjects, well, we know a hell of a lot about human memory from one patient, HM. So strength of findings, when people, is a subjective thing. You really have to be an expert in a field to understand strength of findings, and even that.

And what's amazing is, it starts giving back answers, like, well, if you're concerned about number of subjects, this paper, but that's a pretty obvious one, which one had more subjects. But it can start critiquing these statistics that they used in these papers in very sophisticated ways, and explain back to you why certain papers may not be interesting, and others are more interesting, and it starts to weight the evidence.

Oh my God. And then you say, well, with that weighted evidence, can you hypothesize what would happen if, and so I've done a little bit of this, where it starts trying to predict the future, based on 10 papers that you gave it five minutes ago. Amazing. I don't think any professor could do that, except in their very specific area of interest, and if they were already familiar with the papers, and it would take them many hours, if not days, to read all those papers in detail.

And they might not actually come up with the same answers, right? Right. Yeah, so this is, so actually this is something that is happening in medicine, by the way, for doctors who are using AI as an assistant. This is really interesting. So, and this is dermatology, it was a paper in Nature, you know, skin lesions, there's several, 2,000 skin lesions, and some of them are cancerous, and others are benign.

And so, in any case, they tested the expert doctors, and then they tested an AI, and they were both doing about, you know, 90%, right? However, if you let the doctor use the AI, it boosts the doctor to 98%. 98% accuracy. Yes, and what's going on there? It's very interesting.

So it turns out that, although they got the same 90%, they had different expertise, that the AI had access to more data, and so it could look at the lesions that were rare, that the doctor may never have seen, okay? But the doctor has more in-depth knowledge of the most common ones that he's seen over and over again, and knows the subtleties and so forth.

But so, putting them together, it makes so much sense that they're gonna improve if they work together. And I think that now, what you're saying is that using AI as a tool for discovery, with the expert who's interpreting, and looking at the arguments, the statistical arguments, and also looking at the paper, maybe in a new way, maybe that's the future of science.

Maybe that's what's gonna happen. Everybody's worried about, oh, AI's gonna replace us. It's gonna be much better than we are at everything, and humans are obsolete. Nothing could be further from the case. Our strengths and weaknesses are different, and by working together, it's gonna strengthen both what we do and what AI does, and it's gonna be a partnership.

It's not gonna be adversarial. It's gonna be a partnership. - Would you say that's the case for things like understanding or discovering treatments for neurologic illness, for avoiding large-scale catastrophes, like can it predict macro movements? Let me give an example. Here in Los Angeles, there's occasionally an accident on the freeway.

You have a lot of cameras over freeways nowadays. You have cameras in cars. You can imagine all of the data being sent in in real time, and you could probably predict accidents pretty easily. I mean, these are just moving objects, right, at a specific rate, who's driving haphazardly, but you could also potentially signal takeover of the brakes or the steering wheel of a car and prevent accidents.

I mean, certain cars already do that, but could you essentially eliminate... Well, let's do something even more important. Let's eliminate traffic. (laughs) I don't know if you can do that, 'cause that's a funnel problem, but could you predict physical events in the world into the future? - Okay, this has already been done, not for traffic, but for hurricanes.

As you know, the weather is extremely difficult to predict, except here in California, where it's always gonna be sunny, right? (laughs) But now what they've done is to feed a lot of previous data from previous hurricanes and also simulations of hurricanes. You can simulate them in a supercomputer. It takes days and weeks, so it's not very useful for actually accurately predicting where it's gonna hit Florida.

But what they did was, after training up the AI on all of this data, it was able to predict, with much better accuracy, exactly where in Florida it's gonna make a landfall. And it does that on your laptop in 10 minutes. - Incredible. So something just clicked for me, and it's probably obvious to you and to most people, but I think this is true.

I think what I'm about to say is true. At the beginning of our conversation, we were talking about the acquisition of knowledge versus the implementation of knowledge, just learning facts versus learning how to implement those facts in the form of physical action or cognitive action, right? Math problem is cognitive action, physical action.

Okay. AI can do both knowledge acquisition, it can learn facts, long lists of facts and combinations of facts, but presumably it can also run a lot of problem sets and solve a lot of problem sets. I don't think, except with some crude, still to me, examples of robotics, that it's very good at action yet, but it will probably get there at some point.

Robots are getting better, but they're not doing what we're doing yet. But it seems to me that as long as they can acquire knowledge and then solve different problem sets, different iterations of combinations of knowledge that basically they are in a position to take any data about prior events or current events and make pretty darn good predictions about the future and run those back to us quickly enough and to themselves quickly enough that they could play out the different iterations.

And so I'm thinking one of the problems that seems to have really vexed neuroscientists and the field of medicine and the general public has been like the increase in the, at least diagnosis of autism. I've heard so many different hypotheses over the years. I think we're still pretty much in the fog on this one.

Could AI start to come up with new and potential solutions and treatments if they're necessary, but maybe get to the heart of this problem? - It might. And it depends on the data you have. It depends on the complexity of the disease, but it will happen. In other words, we will use those tools the best we can, 'cause obviously if you can make any progress at all and jump into the future, wow, that would save lives.

That would help so many people out there. I mean, I really think the promise here is so great that even though there are flaws and there are regulatory problems, we just, we really, really have to really push. And we have to do that in a way that is going to help people, in terms of making their jobs better and helping them solve problems that otherwise they would have had difficulty with and so forth.

It's beginning to happen, but these are early days. So we're at a stage right now with AI that is similar to what happened after the first flight of the Wright brothers. In other words-- - It's that significant. - The achievement that the Wright brothers made was to get off the ground 10 feet and to power forward with a human being 100 feet.

That was it, that was the first flight. And it took an enormous amount of improvements. The most difficult thing that had to be solved was control. How do you control it? How do you make it go in the direction you want it to go? And shades of what's happening now in AI is that we are off the ground.

We were not going very far yet, but who knows where it will take us into the future. - Let's talk about Parkinson's disease, a depletion of dopamine neurons that leads to difficulty in smooth movement generation and also some cognitive and mood-based dysfunction. Tell us about your work on Parkinson's and what did you learn?

- So as you point out, Parkinson's is first a degenerative disease. It's very interesting because the dopamine cells are at a particular part of the brain, the brainstem, and they are the ones that are responsible for procedural learning. I told you before about temporal difference. It's dopamine cells. And it's a very powerful way for the, it's a global signal, it's called a neuromodulator because it modulates all the other signals taking place throughout the cortex.

And also it's very important for learning sequences of actions that produce survival, for survival. But the problem is that with certain environmental insults, especially toxins like pesticides, those neurons are very vulnerable. And when they die, you get all of the symptoms that you just described. The people who have lost those cells, actually before the treatment, L-DOPA, which is a dopamine precursor, they actually were, became comatose, right?

They didn't move. They were still alive, but they just didn't move at all. You know, they-- - It's tragic. - Yeah, it's locked in, it's called. Yeah, it's tragic, tragic. So when the first trials of L-DOPA were given to them, it was magical because suddenly they started talking again.

So, I mean, this is amazing, amazing. - I'm curious, when they started talking again, did they report that their brain state during the locked in phase was slow velocity? Like, was it sort of like a dreamlike state or they felt like they were in a nap or were they in there like screaming to get out?

Because their physical velocity obviously was zero. They're locked in after all. And I've long wondered when coming back from a run or from waking up from a great night's sleep, when I shift into my waking state, whether or not physical velocity and cognitive velocity are linked. - Okay, that's a wonderful observation or a question.

I'll bet you know the answer. Okay, here's something that is really amazing. It was discovered, interestingly, when they tend to move slowly, as you said, but to them, cognitively, they think they're moving fast. Now, it's not because they can't move fast, because you can say, well, can you move faster?

Sure. And they move normal, right? But to them, they think they're moving at super velocities. - So it's a set point issue. - So it's a set point issue. Yes, it's all about set points. That's what's really going on. And as the set point gets further and further down, without moving at all, they think they're moving, right?

I mean, this is what's going on. By the way, you can ask them, what was it like? We were talking to you, and you didn't respond. Oh, I didn't feel like it. - The brain confabulates an answer. - They have, well, that they confabulated it because they didn't have enough energy, or they couldn't initiate, they couldn't initiate actions.

That's one of the things that they have trouble with, with movements, starting a movement. - Yeah, as you can tell, I'm fascinated by this notion of cognitive velocity. And again, there may be a better or more accurate or official language for it, but I feel like it encompasses so much of what we try to do when we learn.

And the fact that during sleep, you have these very vivid dreams during rapid eye movement sleep. So cognitive velocity is very fast. Time perception is different than in slow wave sleep dreams. And I really think there's something to it as at least one metric that relates to brain state.

I've long thought that we know so much more about brain states during sleep than we do about wakeful brain states. We talk about focus, motivated, flow. I mean, these are not scientific terms. I'm not being disparaging of them. They're pretty much all we've got until we come up with something better.

But we're biologists and neuroscientists and computational neuroscientists in your case. And we're like trying to figure out like what brain state are we in right now? Our cognitive velocity is a certain value. But I think the more that people think about this, I'll venture to say that the more that they think a little bit about their cognitive velocity at different times of day, we start to notice that there's a, tends to be a few times of day.

For me, it tends to be early to late mid morning. And then again, in the evening, after a little bit of trough and energy that boy, that hour and a half each, like that's the time to get real work done. - I didn't have the same experience. - I can mentally sprint far at those times.

But there are other times of day when I don't care how much caffeine I drink. I don't care, unless it's a stressful event that I need to meet the demands of that stress. I just can't, I can't get to that faster pace while I'm also engaging. You can read faster, you can listen, but you're not using the information.

You're not storing the information. - That's right. - What times of day for you are? - I get most done in the morning. And then you're right, later after dinner is also different though. I think in the morning, I'm better at creative stuff. And then I think that in the evening, I'm better at actually just cranking it out.

- Interesting. Given the relationship between a body temperature and circadian rhythm, I would like to run an experiment that relates core body temperature to cognitive velocity. - I've actually noticed, this is something that is just purely subjective, but the temperature of the salt inside the building is kept 75.

It's like, you know, it's rock solid. But in the afternoon, I feel a little chilly. It's probably my, you know, internal. - Sure, body temperature starts to come down. - Yeah, it's probably going down. And that may correspond to the loss of energy. You know, the amount of the ability for the brain and everything else.

By the way, you know, this is Q10. This is a jargon. Every single enzyme in your, every cell can go at different rates depending on the temperature, right? And so, yeah, so if the body temperature is doing this, then all the cells are doing this too, right? So this is, it's an explanation.

I'm not sure if it's the right one, but. - Yeah, Craig Heller, my colleague at Stanford in the biology department has beautifully described how the enzymatic control over pyruvate, I believe it is, controls muscular failure. That local muscular failure, you know, when people are trying to move some resistance, has everything to do with the temperature, the local temperature that shuts down certain enzymatic processes that don't allow the muscles to contract the same way.

You know, he knows the details and he covered them on this podcast. I'm forgetting the details. You start to go, wow, like these enzymes are so beautifully controlled by temperature. And of course, his laboratory is focused on ways to bypass those temperature or to change temperature locally in order to bypass those limitations and have shown them again and again.

It's just incredible. Yeah, I don't, I hear we're speculating about what it would mean for cognitive velocity. But I think it's such a different world to think about the underlying biology as opposed to just thinking about like a drug. You know, you increase dopamine and norepinephrine and epinephrine, the so-called catecholamines, and you're gonna increase energy focus and alertness, but you're gonna pay the price.

You're gonna have a trough in energy focus and alertness that's proportional to how much greater it was when you took the drug. - Boy, amphetamines are a good example. Boy, you know, you're going a mile a minute when you're taking the drug. Of course, you know, it's, I fully understand that that's your impression.

And the reality is you don't actually accomplish that much more. - Have any LLMs, so AI, been used to answer this really pressing question of what is going to be the consequence on cognition for these young brains that have been weaned while taking Ritalin, Adderall, Vyvanse, and other stimulants?

'Cause we have, you know, millions of kids that have been raised this way. - We did this experiment on our, you know, whole cadre, a whole generation. And you know, I really would like to know the answer. I wonder if anybody's studying that. That's really a great question. 'Cause we gave them speed, effectively.

You know, the drug that causes the brain to be activated. But by the way, but you know, there's the consequences that, you know, when it wears off, you have no energy, right? You're just completely spent, that's it. - That's the pit. - That's the pit. And so, and, but that's why you take more of it.

You see, that's the problem is it's a spiral. - I love how today you're making it so very clear how computation, how math and computers and AI now are really shaping the way that we think about these biological problems, which are also psychological problems, which are also daily challenges.

I also love that we touched on mitochondria and how to replenish mitochondria. I want to make sure that we talk about a couple of things that I know are in the back of people's minds, no pun intended here, which are consciousness and free will. Normally, I don't like to talk about these things, not because they're sensitive, but because I find the discussions around them typically to be more philosophical than neurobiological.

And they tend to be pretty circular. And so you get people like Kevin Mitchell, who is a real, I think he has a book about free will. He believes in free will. You've got people like Robert Sapolsky, who wrote the book "Determined." He doesn't believe in free will. How do you feel about free will?

And is it even a discussion that we should be having? - Well, if you go back 500 years, you know, it's the middle ages, the concept didn't exist, or at least not in the way we use it. Because everybody, it was the way that humans felt about the world and how it worked and its impact on them was that it's all fate.

They had this concept of fate, which is that there's nothing you can do that something is going to happen to you because of what's going on in the gods up above, or whatever it is, right? You attribute it to the physical forces around you that caused it, not to your own free will, not to something that caused this to happen to you, right?

So I think that these words, by the way, that we use, free will, consciousness, intelligence, understanding, they're weasel words because you can't pin them down. There is no definition of consciousness that everybody agrees on. And it's tough to solve a problem, a scientific problem, if you don't have a definition that you can agree on.

And, you know, there's this big controversy about whether these large language models understand language or not, right? The way we do. And what it really is revealing is we don't understand what understanding is. Literally, we don't have a really good argument or a measure that you could measure someone's understanding and then apply it to the GDP and see whether it's the same.

It probably isn't exactly the same, but maybe there's some continuum here we're talking about, right? You know, the way I look at it, it's as if an alien suddenly landed on Earth and started talking to us in English, right? And the only thing we could be sure of, it was that it's not human, right?

- I met some people that I wondered about their terrestrial origins. - Okay, okay. Well, okay, now there's a big diversity amongst humans too. You're right about that. - Yeah, yeah, yeah. Certain colleagues of ours at UCSD years ago, one in particular in the physics department who I absolutely adore as a human being, just had such an unusual pattern of speech, of behavior, totally appropriate behavior, but just unusual.

In the middle of a faculty meeting, would just kind of turn to me and start talking while the other person was presenting. And I was like, "Maybe not now." And he would say, "Oh, okay." But in any other domain, you'd say he was very socially adept. And so, you know, there's certain people that just kind of discard with convention and you kind of want to like, "Is he an alien?" It's kind of cool, in a cool way.

Like, you know, he's one of my, again, a friend and somebody I really delight in. - It's true, it's true. You know, not everybody has adopted the same social conventions. It could be a touch of autism. That's a problem that, I mean, in other words, there are very high functioning autistic people out there.

- He's brilliant. - And often they are, you know. There are high people who are brilliant with autism, but, you know. - Could you build an LLM that was more on one end of the spectrum versus the other to see what kind of information they forage for? - I reviewed a paper.

- It seemed like it would be a really important thing to do. - That it's been done. Okay, there was a paper that I reviewed where they took the LLM and they fine-tuned it with different data from people with different disorders, you know, autism and so forth. And sociopaths, you know.

- That's scary. But you want to know the answer. - No, and they got these LLMs to behave just like those people who have these disorders. You can get them to behave that way, yes. - Could you do political leaning and values? - I haven't seen that, but it's pretty clear that, to me at least, that if you can do sociopathy, you can probably do any political belief, you know.

- But you could also view all this as, you could take benevolent tracks. You could also say hyper-creative, sensitive to emotional tone of voices and find out what kind of information that person brings, excuse me, that LLM brings back versus somebody who is very oriented towards just the content of people's words as opposed to what, you know.

Because among people, you find this. You know, if you've ever left a party with a significant other, and sometimes someone will say, "I've had this experience with like, "did you see that interaction between so-and-so?" I'm like, "no, what are you talking about? "Like, did you hear that?" I'm like, "no, not at all.

"I didn't hear, I heard the words, "but I did not pick up on what you were picking up on." And it was clear that there's two very different experiences of the same content based purely on a difference in interpretation of the tonality. - Okay, there's a lot of information that, as you point out, which has to do with the tone, the spatial expressions.

You know, there's a tremendous amount of information that is passed not just with words, but with all the other parts of the visual input and so forth. And some people are good at picking that up and others are not. There's a tremendous variability between individuals. And, you know, biology is all about diversity, and it's all about, you know, needing a gene pool that's very diverse so that you can evolve and survive catastrophic changes that occur in a climate, for example.

But wouldn't it be wonderful if we could create a LLM that could understand what those differences are? Now just think about it, right? Like a truly diverse LLM that integrated all those differences. - Yeah, so here's how, what you'd have to do. What you'd have to do is to train it up on data from a bunch of individuals, human individuals.

Now, one of the things about these LLMs is that they don't have a single persona. They can adopt any persona. You have to tell it what you're expecting from. - Or ask it in a way that works for you and you'll get back a certain persona. - If you, I once gave it an abstract from a paper, very technical, a computational paper.

And I said, "You are a neuroscientist. "I want you to explain this abstract to a 10-year-old." It did it in a way that I could never have done it. It really simplified it. - Was it accurate? - Some of the subtleties were not in it, but it explained, you know, what plasticity it was and explained what a synapse is.

And you know, it did that. - Amazing. - Almost like a qualifying exam for a graduate student. I saw something today on X, formerly known as Twitter, that blew my mind that I wanted your thoughts on that is very appropriate to what you're saying right now, which is someone was asking questions of an LLM on ChatGPT or maybe one of these other, Anthropic or Claude or something like that.

I probably misused those names. One of the AI online sites. And somewhere in the middle of its answers, the LLM decided to just take a break and start looking at pictures of landscapes in Yosemite. Like the LLM was doing what a maybe cognitively fatigued person or what any kind of online person online would do, which was to like take a break and look at a couple of pictures of something they, you know, maybe they're thinking about going camping there or something, and then get back to whatever task.

We hear about hallucinations in AI, that it can imagine things that aren't there, just like a human brain. But that blew my mind. - I haven't encountered that, but, you know, isn't it fascinating? You know, that's a sign of a real generative internal model. See, here's the thing that, the thing that most distinguishes, I think, an LLM from a human is that, you know, if you go into a room, quiet room, and just sit there without any sensory stimulation, your brain keeps thinking, right?

In other words, you think about what you wanna do, you know, planning ahead or something that happened to you during the day, right, your brain is always generating internally. You know, after talking to you, one of these large language models just goes blank. There is no self-continuous, self-generated thoughts.

- And yet we know self-generated thought, and in particular brain activity during sleep, as you illustrated earlier, with the example of sleep spindles and rapid eye movement, sleep are absolutely critical for shaping the knowledge that we experience during the day. So these LLMs are not quite where we are at yet.

I mean, they can outperform us in certain things like Go, but how soon will we have LLMs, AI that is, with self-generated internal activity? - We're getting closer. And so this is something I'm working on myself, actually, trying to understand how that's done in our own brains, was generating continual brain activity that leads to planning and things.

We don't know what the answer to that is yet in neuroscience. And by the way, you go to a lecture and you hear the words one after the next over an hour, and you see the slides one after the next. At the end, you ask a question, right? Just let's think about what you just did.

Somehow you're able to integrate all that information over the hour and then use your long-term memory then to come up with some insight or some issue that you want. How does your brain remember all that information? Working memory, traditional working memory that neuroscientists study is only for a few seconds, right, or maybe a telephone number or something.

But we're talking about long-term working memory. We don't understand how that is done. And LLMs, actually, large language models, can do something, it's called in-context learning. And it's a really, it was a great surprise because there is no plasticity. The thing learns at the beginning, you train it up on data, and then all it does after that is to inference, you know, fast loop of activity one word after the next, right, that's what happens with no learning, no learning.

But it's been noticed that as you continue your dialogue, it seems to get better at things. How could that be? How could it be in context learning, even though there's no plasticity? That's a mystery. We don't know the answer to that question yet. But we also don't know what the answer it is, what the answer is for humans either.

- Right. Could I ask you a few questions about you and as it relates to science and your trajectory? Building off of what you were just saying, do you have a practice of meditation or eyes closed, sensory input reduced or shut down to drive your thinking in a particular way?

Or are you, you know, at your computer talking to your students and postdocs and sprinting on the beach? - You know, it's funny you mentioned that 'cause I get my best ideas, not sprinting on the beach, but you know, just either walking or jogging. And it's wonderful, I don't know.

I think, you know, serotonin goes up. It's another neuromodulator. I think that that stimulates ideas and thoughts. And so inevitably, I come back to my office and I can't remember any of those great ideas. - What do you do about that? - Well, now I take notes. - Okay, voice memos?

- Yeah. And some of them, it's a pan out. You know, there's no doubt about it. You're put into a situation. It is a form of meditation. You know, if you're running in a steady pace, nothing distracting about, you know, the beach. - Or do you listen to music or podcasts?

- No, I never listen to anything except my own thoughts. - So there's a former guest on this podcast who, she happens to be triple degreed from Harvard, but she's more in the kind of like personal coach space, but very, very high level and impressive mind, impressive human all around.

And she has this concept of wordlessness that can be used to accomplish a number of different things, but this idea that allowing oneself or creating conditions for oneself to enter states throughout the day, or maybe once a day of very minimal sensory input, no lecture, no podcast, no book, no music, nothing, and allowing the brain to just kind of idle and go a little bit non-linear, if you will.

- Right. - Where we're not constructing thoughts or paying attention to anyone else's thoughts through those media venues in any kind of structured way as a source of great ideas and creativity. - It's been studied. Psychologists call it mind-wandering. - Mind-wandering. - Yeah, it is a significant literature. And it's often when you have an aha moment, right?

You know, your mind is wandering and it's thinking non-linearly in the sense of not following a sequence that is logical, you know, hopping from thing to thing. Often that's when you get a great idea, with just letting your mind wander. Yeah, and that happens to me. - I wonder whether social media and just texting and phones in general have eliminated a lot of the, you know, walks to the car after work where one would normally not be on a call or in communication with anyone or anything.

I used to do experiments where I was, you know, like pipetting and running immunohistochemistry. And it was very relaxing. And I could think while I was doing 'cause I knew the procedures. And then, you know, you had to pay attention to certain things, write them down. But I would often feel like, wow, I'm both working and relaxing and thinking of things.

And then I would listen to music sometimes. - Okay, so we have a whole session, you know, a clip in "Learning How to Learn" about exactly this phenomenon. Here's what we tell our students, right? Is that, you know, if you're having trouble with some concept or, you know, you don't understand something, you're beating your head against the wall, don't, stop, stop.

Just go off and do something. Go off and clean the dishes. Go off and, you know, walk around the block. And inevitably what happens is when you come back, your mind is clear and you figure out what to do. And that's one of the best pieces of advice that anybody could get.

Because, you know, we don't, nobody has told us how the brain works, right? Some people are really good at intuiting because they've experienced, you may be, but everybody I, okay. The other thing is everybody I know who's really made important contributions and I'll bet you're one of them. You know, you're struggling with some problem at night and you go to bed and you wake up in the morning.

Ah, that's the solution. That's what I should do, right? - First thing in the morning when I wake up is when I'm almost bombarded with, I wouldn't say insight and not always meaningful insight, but certainly what was unclear becomes immediately clear on waking. - That's right. That's the thing that is so amazing about sleep.

And you can see people who know this can count on it. In other words, the key is to think about it before you go to sleep, right? Your brain works on it during the sleep period, right? And so, you know, don't watch TV because then who knows what your brain's gonna work on.

You know, use the time before you fall asleep to think about something that is bothering you or maybe something that, you know, you're trying to understand, maybe, you know, a paper that you've, you read the paper and say, oh, you know, I'm tired, I'm gonna go to sleep. You wake up in the morning and say, oh, I know what's going on in that paper.

Yeah, I mean, that's what happens. You can use, you know, once you know something about how the brain works, you can take advantage of that. - Do you pay attention to your dreams? Do you record them? - No, no. Okay, so here's the problem. Dreams seem so iconic and a lot of people, you know, somehow attribute things to them, but there has never been any good theory or any good understanding, first of all, why we dream.

We still, I mean, it's still not completely clear. I mean, there are some ideas, but, or what trig, why this particular dream? Is this, does that have some significance for you? And the only thing that I know that might explain a little bit is that, you know, the dreams are often very visual, you know, rapid eye movement, sleep, so that there's something happening.

Actually, it's interesting. All the neuromodulators are downregulated during sleep and then during REM sleep, the acetylcholine comes up, right? So that's a very powerful neuromodulator. It's important for attention, for example, but it doesn't come up in the prefrontal cortex, which means that the circuits in the prefrontal cortex that are interpreting the sensory input coming in are not turned on.

So any of these, whatever happens in your visual cortex is not being monitored anymore. So you get bizarre things, you know, that you start floating and, you know, things happen to you and, you know, it's not anchored anymore. And so, but that still doesn't explain why, right? Why you have that period.

It's important 'cause if you block it, and there are some sleeping pills that do block it, you know, it really does cause problems with, you know, normal cognitive function. - Cannabis as well. People who come off cannabis experience a tremendous REM rebound and lots of dreaming in the, you know, the days and weeks and months after cannabis.

- Wow. - I don't wanna call it withdrawal 'cause that has a different meaning. - No, no, it's a imbalance that was caused of, you know, because the brain adjusted to the endocannabinoid levels. And now it's gotta go back and then it takes time, but it's interesting. It's an interesting, it affects dreams.

I think that may be a clue. - Yeah, very, very common phenomenon. I'm told, I'm not a cannabis user, but no judgment there, I just am not. - There's actually a book I read years ago when I was in college, so a long time ago, by Alan Hobson, who was out at Harvard.

- Oh yeah, I know him. - Oh, cool, so I never met him, but he had this interesting idea that dreams, in particular rapid eye movement dreams, were so very similar to the experience that one has on certain psychedelics, LSD, lysergic acid, diethylamide, or psilocybin, and that perhaps dreams are revealing the unconscious mind, not saying this in any psychological terms, that when we're asleep, our conscious mind can't control thought and action in the same way, obviously, and it's sort of a recession of the waterline, so we're getting more of the unconscious processing revealed.

- You know, that's an interesting hypothesis. How would you test it? - I'd probably have to put someone in a scanner, have them go to sleep, put them in the scanner on a psilocybin journey, this kind of thing. You know, it's tough. I mean, any of these observational studies, of course we both know, are deficient in the sense that what you'd really like to do is control the neural activity.

You'd like to get in there and tickle the neurons over here and see how the brain changes, and you'd love to get real-time subjective report. This is the problem with sleep and dreaming, is you can wake people up and ask them what they were just dreaming about, but you can't really know what they're dreaming about in real time.

- It's true, yeah, it's true. By the way, you know, there are two kinds of dreams. Very interesting. So if you wake someone up during REM sleep, you get very vivid changing. Dreams, they're always different and changing, but if you wake someone up during slow-wave sleep, you often get a dream report, but it's a kind of dream that keeps repeating over and over again every night, and it's a very heavy emotional content.

- Interesting, that's in slow-wave sleep? - Yeah. - 'Cause I've had a few dreams over and over and over throughout my life, so this would be in slow-wave sleep. - Yeah, probably slow-wave sleep, yeah. - Fascinating. As a neuroscientist who's computationally oriented, but really you incorporate the biology so well into your work, so that's one of the reasons you're you, you're this luminary of your field, and who's also now really excited about AI, what are you most excited about now?

Like if you had, and you know, of course this isn't the case, but if you had like 24 more months to just pour yourself into something, and then you had to hand the keys to your lab over to someone else, what would you go all in on? - Well, so the NIH has something called the Pioneer Award, and what they're looking for are big ideas that could have a huge impact, right?

So I put one in recently, and here's the title, is Temporal Context in Brains and Transformers. - And in brains and transforms? - Transformers. - Formers. - AI, right, the key to GTP is the fact there's this new architecture, it's a deep learning architecture, feed-forward network, but it's called a transformer, and it has certain parts in it that are unique.

There's one called self-attention, and it's a way of doing what is called temporal context, what it does is it connects words that are far apart, you give it a sequence of words, and it can tell you the association, like if I use the word this, and then you have to figure out in the last sentence what it did refer to, well, there's three or four nouns it could have referred to, but from context, you can figure out which one it does, and you can learn that association.

- Could I just play with another example to make sure I understand this correctly? I've seen these word bubble charts, like if we were to say piano, you'd say keys, you'd say music, you'd say seat, and then it kind of builds out a word cloud of association. And then over here, we'd say, I don't know, I'm thinking about the Salk Institute, I'd say sunset, Stonehenge, anyone that looks up, there's this phenomenon of Salkhenge.

Then you start building out a word cloud over there. These are disparate things, except I've been to a classical music concert at the Salk Institute twice, so they're not completely non-overlapping, and so you start getting associations at a distance, and eventually they bridge together. Is this what you're referring to?

- Yes, I think that that's an example, but it turns out that every word is ambiguous, it has like three, four meanings, and so you have to figure that out from context. So in other words, there are words that live together, and that come up often, and you can learn that from just by, you know, predicting the next word in a sentence, that's how a transformer is trained.

You give it a bunch of words, and it keeps predicting the next word in a sentence. - Like in my email now, it tries to predict the next word. - Exactly. - And it's mostly right part of the time. - Okay, well, that's because it's a very primitive version of this algorithm.

What happened is if you train it up on enough, not only can it answer the next word, it internally builds up a semantic representation in the same way you describe the words that are related to each other, having, you know, associations. It can figure that out, and it has representations inside this very large network with trillions of parameters, and unbelievable how big they've gotten.

And those associations now form an internal model of the meaning of the sentence. Literally, it's been, this is something that now we've probed these transformers, and so we pretty much are pretty confident. And that means that it's forming an internal model of the outside world, in this case, a bunch of words.

And that's how it's able to actually respond to you in a way that is sensible, that makes sense, and actually is interesting, and so forth. And it's all for the self-attention I'm talking about. So in any case, my pioneer proposal is to figure out how does the brain do self-attention, right?

It's gotta do it somehow. And I'll give you a little hint. Basal ganglia. - It's in the basal ganglia. - That's my hypothesis. Well, we'll see. I mean, I'll be working with experimental people. I've worked with John Reynolds, for example, who studies primate visual cortex, and we've looked at traveling waves there, and there are other people that have looked at, in primates.

And so now, these traveling waves, I think, are also a part of the puzzle, pieces of the puzzle that are gonna give us a much better view of how the cortex is organized and how it interacts with the basal ganglia. We've already been there. But we still, neuroscientists have studied each one of these parts of the brain independently, and now we have to start thinking about putting the pieces of the puzzle together, right?

Trying to get all the things that we know about these areas and see how they work together in a computational way. And that's really where I want to go. - I love it. And I do hope they decide to fund your Pioneer Award. - I do too. - Yeah.

And should they make the bad decision not to, maybe we'll figure out another way to get the work done. Certainly you will. Terry, I want to thank you, first of all, for coming here today, taking time out of your busy cognitive and running and teaching and research schedule to share your knowledge with us.

And also for the incredible work that you're doing on public education and teaching the public, I should say, giving the public resources to learn how to learn better at zero cost. So we will certainly provide links to learning how to learn and your book and to these other incredible resources that you've shared.

And you've also given us a ton of practical tools today related to exercise mitochondria and some of the things that you do, which of course are just your versions of what you do, but that certainly, certainly are going to be a value to people, including me, in our cognitive and physical pursuits and frankly, just longevity.

I mean, this is not lost on me and those listening that your vigor is, as I mentioned earlier, undeniable. And it's been such a pleasure over the years to just see the amount of focus and energy and enthusiasm that you bring to your work and to observe that it not only hasn't slowed, but you're picking up velocity.

So thank you so much for educating us today. I know I speak on behalf of myself and many, many people listening and watching. This is a real gift, a real incredible experience to learn from you. So thank you so much. - Well, thank you. And I have to say that I've been blessed over the years with wonderful students and wonderful colleagues.

And I count you among them who really, I've learned a lot from. - Thank you. - But, you know, we're, you know, science is a social activity and we learn from each other and we all make mistakes, but we learn from our mistakes. And that's the beauty of science is that we can make progress.

Now, you know, your career has been remarkable too, because you have affected and influenced more people than anybody else I know personally with the knowledge that you are broadcasting through your interviews, but also, you know, just in terms of your interests. Really, I'm really impressed with what you've done and I want you to keep, you know, at it because we need people like you.

We need scientists who can actually express and reach the public. If we don't do that, everything we do is behind closed doors, right? Nothing gets out. And so you're one of the best of the breed in terms of being able to explain things in a clear way that gets through to more people than anybody else I know.

- Well, thank you. I'm very honored to hear that. It's a labor of love for me and I'll take those words in and I really appreciate it. It's an honor and a privilege to sit with you today and please come back again. - I would love to, yeah. - All right, thank you, Terry.

- You're welcome. - Thank you for joining me for today's discussion with Dr. Terry Sinowski. To find links to his work, the Zero Cost Online Learning Portal that he and his colleagues have developed and to find links to his new book, please see the show note captions. If you're learning from and or enjoying this podcast, please subscribe to our YouTube channel.

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It's my very first book. It's entitled "Protocols, An Operating Manual for the Human Body." This is a book that I've been working on for more than five years and that's based on more than 30 years of research and experience. And it covers protocols for everything from sleep, to exercise, to stress control, protocols related to focus and motivation.

And of course, I provide the scientific substantiation for the protocols that are included. The book is now available by presale at protocolsbook.com. There you can find links to various vendors. You can pick the one that you like best. Again, the book is called "Protocols, An Operating Manual for the Human Body." If you're not already following me on social media, I'm Huberman Lab on all social media platforms.

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If you haven't already subscribed to our Neural Network Newsletter, our Neural Network Newsletter is a zero cost monthly newsletter that includes podcast summaries, as well as protocols in the form of brief one to three page PDFs. Those one to three page PDFs cover things like deliberate heat exposure, deliberate cold exposure.

We have a foundational fitness protocol. We also have protocols for optimizing your sleep, dopamine, and much more. Again, all available, completely zero cost. Simply go to HubermanLab.com, go to the menu tab, scroll down to newsletter and provide your email. We do not share your email with anybody. Thank you once again for joining me for today's discussion with Dr.

Terry Sadnowski. And last, but certainly not least, thank you for your interest in science. you