Speaker 1: The brain is massively complex, so how have people tried
Speaker 1: to crack its mysteries? And what does this always have
Speaker 1: to do with the latest technologies that are available, And
Speaker 1: what does the history of neuroscience have to do with
Speaker 1: feeling bumps on someone's skull? Electricity, Frankenstein, animatronics, telegraphs, telephone exchanges, computers, LMS,
Speaker 1: and the next metaphor that we're going to use to
Speaker 1: try to capture the brain's magic. Welcome to Inner Cosmos
Speaker 1: with me David Eagelman. I'm a neuroscientist and an author
Speaker 1: at Stanford, and in these episodes we seek to understand
Speaker 1: why and how our lives look the way they do.
Speaker 1: If you reach out and you wrap your hand around
Speaker 1: your coffee cup and then bring it up to your lips,
Speaker 1: that all seems pretty effortless. But what's driving that motor
Speaker 1: action is a massive, invisible puppeteer living inside your skull,
Speaker 1: an organ made of tens of billions of very active
Speaker 1: cells called neurons, each one of which is firing off
Speaker 1: tiny electrical pulses tens or hundreds of times every second,
Speaker 1: and each of which is exchanging messages with thousands of
Speaker 1: other neurons. And somehow from this vast electrical storm, you
Speaker 1: get your hand moving, and you get your perception of
Speaker 1: the tattooed barista in a rolled up flannel pulling espresso
Speaker 1: shots and their of students hunched over laptops, and the
Speaker 1: scent of dark roast and cinnamon. And you get your
Speaker 1: memory of your name and your first kiss and what
Speaker 1: you had for breakfast. And generally, from this lightning storm
Speaker 1: of activity in the brain emerges the shimmering.
Speaker 2: Something that we call you.
Speaker 1: Now, we humans have been trying to understand this wrinkled
Speaker 1: three pounds of jelly for a long time, because it's
Speaker 1: been obvious that this is where all the action is.
Speaker 1: Why is it obvious that is all happening in the brain. Well,
Speaker 1: if you lose your leg, or your kidney or an eyeball,
Speaker 1: those are all terrible events, but you.
Speaker 2: Are still the same. But if you damage even a
Speaker 2: very small chunk of brain tissue, that can change you entirely.
Speaker 1: It changes your personality, or your decision making or your thoughts.
Speaker 1: It changes who you are. So that led people to
Speaker 1: realize slowly that somehow all the action of your behaviors
Speaker 1: and your conscious experience is all tied to these three
Speaker 1: pounds of tissue. So that sounds good, but it's really
Speaker 1: hard to crack the mystery of how this thing works.
Speaker 1: And that's for two main reasons. First, all the action
Speaker 1: going on is microscopic. When you look at it, you
Speaker 1: can't see anything. It just looks like a big, wrinkly
Speaker 1: thing with the consistency of mashed potatoes. You'll often hear
Speaker 1: me and others say that this is the most complex
Speaker 1: thing we've ever discovered on our planet, but that is
Speaker 1: a modern revelation. There's nothing obvious about that statement when
Speaker 1: you look at the brain, and in fact, ancient cultures
Speaker 1: used to throw out the brain at autopsy and be
Speaker 1: much more interested in other organs. So the brain's doing
Speaker 1: all kinds of things, but you can't see that. The
Speaker 1: second challenge is that the brain is locked up tightly
Speaker 1: inside the skull. It makes sense that it's well protected
Speaker 1: as very delicate stuff, but that has made it especially
Speaker 1: hard to study. So what's happened is that people have
Speaker 1: been trying to decipher this very complicated puzzle for a
Speaker 1: long time, and the history of neuroscience is, in a
Speaker 1: way the history of human self regard. Each generation peers
Speaker 1: into the darkness and sees its own technology reflected back.
Speaker 1: So in the seventeenth century, when hydraulics were the cutting
Speaker 1: edge of technology, the brain was imagined as a network
Speaker 1: of pipes and valves.
Speaker 2: Thoughts and sensations flowed like.
Speaker 1: Water, propelled by animal spirits that coursed through hollow nerves.
Speaker 1: Then in the eighteenth century, as electricity captured the imagination,
Speaker 1: the brain became a battery. It became a generator of sparks.
Speaker 1: By the nineteenth century it was a telegraph system, with
Speaker 1: messages darting along wires and transmitting coded signals from one
Speaker 1: station to another.
Speaker 2: And then as we moved into.
Speaker 1: The twentieth century with advancing technology, the metaphor shifted again.
Speaker 2: The brain became a telephone.
Speaker 1: Exchange, and then an electronic circuit and eventually a digital computer.
Speaker 1: Now the cool thing is that each metaphor brought new insights.
Speaker 1: So hydraulics inspired experiments on fluid pressure in the brain,
Speaker 1: and telegraphs shaped the search for nerve conduction, and the
Speaker 1: computer age fueled the rise.
Speaker 2: Of neural network models.
Speaker 1: But each metaphor also carries its blind spots. We become
Speaker 1: experts at seeing the brain through the lens of the
Speaker 1: day's technology, and sometimes we mistake the map for the territory.
Speaker 1: So nowadays we've got great tools. We have fMRI scanners
Speaker 1: that can track activity millimeter by millimeter. We have optogenetics
Speaker 1: that can tickle neurons on or off with light. We
Speaker 1: have genetic tools to clip out sections or even single letters.
Speaker 1: And yet the deepest mysteries of the brain still remain,
Speaker 1: and they're still stubborn. How does a three pound lump
Speaker 1: of biological tissue generate the first person experience of being alive?
Speaker 1: How does matter become mind? And how do all the
Speaker 1: technology metaphors that we use today blind us to some
Speaker 1: of the details. So that's why today I called up
Speaker 1: Matthew Cobb. He's an evolutionary neurobiologist who studies smell and
Speaker 1: memory at the University of Manchester, and he's also a
Speaker 1: historian who has spent decades tracing the shifting stories that
Speaker 1: we tell about the brain. He wrote a great book
Speaker 1: called The Idea of the Brain, and this is a
Speaker 1: fascinating history of neuroscience because it's not just a list
Speaker 1: of events, but instead it explores how science and culture
Speaker 1: and technology all braided together to shape our ideas and
Speaker 1: how time and again we've been certain that we were
Speaker 1: on the brink of a full explanation, only to discover
Speaker 1: that the brain is more complex than we imagined. You
Speaker 1: can't read this book without getting a deep appreciation for
Speaker 1: the centuries of labor that.
Speaker 2: It took to get us to the modern day picture.
Speaker 1: The history of neuroscience is about hundreds or thousands of
Speaker 1: people making small contributions, and presumably each person could never
Speaker 1: know how seminole the contribution was. They were just providing
Speaker 1: one puzzle piece that would end up clicking together with
Speaker 1: other pieces in the future, but it's always impossible to see.
Speaker 1: So the key is that in the end, the story
Speaker 1: of the brain is also the story of us. It's
Speaker 1: our technology, is what we understand at any point in history,
Speaker 1: and of course our relentless drive to understand the organ
Speaker 1: that is doing the understanding. So here's my conversation with
Speaker 1: Matthew Cobb. Okay, so, Matthew, I'm a giant fan of
Speaker 1: your book, The Idea of the Brain. I thought it
Speaker 1: was extraordinary because it's not just a history of neuroscience
Speaker 1: that is, it includes that, but much more importantly, it's
Speaker 1: a history of how to think about the brain, how
Speaker 1: people have thought about it.
Speaker 2: And one of your main points in the book.
Speaker 1: Is that we always draw on our technology as a metaphor.
Speaker 1: So science is not just an accumulation of facts, but
Speaker 1: instead we frame things in particular ways depending on what's
Speaker 1: going on around us.
Speaker 2: So let's start there this.
Speaker 3: Issue that you've just described a metaphor, of the metaphors
Speaker 3: that we use, and how that changes over time. That
Speaker 3: actually gives a frame to the book and makes the history,
Speaker 3: which otherwise might be a bit dull, actually much more
Speaker 3: interesting and enables you to think about how people could
Speaker 3: think the way they didn't, why they couldn't see anything different,
Speaker 3: Because that's always the problem with history, right, I mean,
Speaker 3: it looks so obvious to us, you know how why
Speaker 3: on earth didn't people realize that the brain is the
Speaker 3: center of thought?
Speaker 4: Why do they think it was the heart? And one
Speaker 4: of the rules.
Speaker 3: I think about history, in particular the history of science,
Speaker 3: is that you're not allowed to think that people in
Speaker 3: the past were stupid because they were amazingly smart.
Speaker 4: They just didn't know as much as we know. And
Speaker 4: we only know.
Speaker 3: That most of us because we've read it or we've
Speaker 3: been taught it in class. Relatively few people have actually
Speaker 3: made great breakthroughs that have changed how we think about
Speaker 3: the world. So when you've got these people who are very,
Speaker 3: very clever but can't see something, that the task is
Speaker 3: to try and put yourselves in their heads and say, well,
Speaker 3: what is it they don't understand And it can't just
Speaker 3: be the answer, you know, they didn't understand that nothing
Speaker 3: goes faster than light or whatever. You've got to think about, well,
Speaker 3: what is it the framework that they're living in the
Speaker 3: world they're living in. Trying and put yourselves in their shoes,
Speaker 3: and then it all starts to become clearer. And it also,
Speaker 3: I think for the reader, both the lay reader and
Speaker 3: for scientists becomes much more interesting because then you can
Speaker 3: start to say not only about now, but also about
Speaker 3: the future, about what might be coming and why we
Speaker 3: can't understand certain things at the moment.
Speaker 4: You know.
Speaker 1: One of the things that was quite stunning for me
Speaker 1: is I, like, you, have spent my whole career in neuroscience,
Speaker 1: but it was difficult for me to envision what it
Speaker 1: would have been like to live in neuroscience in an
Speaker 1: era where we didn't know something. So for example, the
Speaker 1: discovery of the action potential. This is where a neuron
Speaker 1: has a spike there, zips down the axon and carries
Speaker 1: signals rapidly in this way. I grew up just taking
Speaker 1: for granted that that's what neurons do.
Speaker 4: That.
Speaker 1: Of course, the brain uses electricity in this sense. But
Speaker 1: you know, you had a whole chapter about electricity and
Speaker 1: how people discovered that that was going on with the brain.
Speaker 2: Give us a sense of that.
Speaker 4: Well, it all began, I guess it began in America
Speaker 4: really with.
Speaker 3: The ability to Franklin to actually bring electricity down very
Speaker 3: dangerous experiments as everybody knows, with kites and keys and
Speaker 3: that kind of thing. But in the eighteenth century people
Speaker 3: began to be able to store electricity. They could produce
Speaker 3: it in the form of static electricity. So they'd get
Speaker 3: some amber or any kind of resin and you know,
Speaker 3: just like you, you know, maybe you do.
Speaker 4: This for your kid.
Speaker 3: So you know when you were a kid, your dad
Speaker 3: would do this. You get a balloon, you rub it
Speaker 3: on his jump and he stick it on the wall. Amazing, right,
Speaker 3: that static electricity you can actually generate by putting say
Speaker 3: some wool onto a resin wheel. That you spin round
Speaker 3: and they would do These people would do these kind
Speaker 3: of party tricks. They were called electricians, right, and they
Speaker 3: would go into your fancy house and they would they
Speaker 3: would do like one of these shows was called the
Speaker 3: Feathered Boy, and they'd get some hapless child and winch
Speaker 3: him up to the ceiling and then they'd charge him
Speaker 3: up with electricity using this amber and cloth, and then
Speaker 3: they'd throw a load of feathers in the air and
Speaker 3: of course they'd all stick on it. So they could
Speaker 3: do stuff like this. And also people were thinking about
Speaker 3: actually using a very primitive form of electroshock therapy. So
Speaker 3: these electricians, when you could generate it, they would travel
Speaker 3: around the countryside, you know, itinerant electricians, so too particularly
Speaker 3: significant figures. So John Wesley, the founder of Methodism, and Maha,
Speaker 3: the French revolutionary, were both itinerant electricians in the UK
Speaker 3: in the eighteenth century.
Speaker 4: Wandering around. Somebody say I'm.
Speaker 3: Feeling really miserable and terrible, and he said, well, just
Speaker 3: hold on to this, and then they'd wind it up
Speaker 3: and bang, you get an electric shark. So scientists knew
Speaker 3: that electricity was doing something, but that didn't mean to
Speaker 3: say that it was how bodies worked, and it became
Speaker 3: increasingly complex as people were able to actually store electricity
Speaker 3: through something called a lidon jar. You'd actually put it
Speaker 3: into a generate this electricity and it would then stay.
Speaker 3: But what happened it was then suddenly discharge if you
Speaker 3: touched it, like touching a cow fence.
Speaker 4: Right, you know, you know you've been touched.
Speaker 3: And so when French scientists didn't experiment with this, he
Speaker 3: got four hundred monks, which is they're all holding hands,
Speaker 3: and at one end he got one of these big
Speaker 3: jars full of electricity and he made the monk touch it,
Speaker 3: and then he watched us that they all jumped up
Speaker 3: as if the chargers down this line of monks must
Speaker 3: have been about whatever eight hundred meters more thousand meters
Speaker 3: kilometer along a monk. And then finally they were able
Speaker 3: to use They were showing that even they're doing experiments
Speaker 3: on animals, like in particular frogs, and they'd noticed that
Speaker 3: if you stimulated a frog nerve on attached to a leg,
Speaker 3: then their muscle would contract. Now this didn't show you
Speaker 3: what was going on, because you know if you put
Speaker 3: acid on a nerve then that it will also contract
Speaker 3: so maybe electricy was just an irritant, or maybe it
Speaker 3: was actually in bodies right, And this was.
Speaker 4: A big argument.
Speaker 3: One of the things that people stuid was electric fish,
Speaker 3: so the electric eel. And they found this structure that
Speaker 3: was produced as shock and at the beginning of the
Speaker 3: nineteenth century chap called vaulta hence vault. He decided he'd
Speaker 3: do a bit of biomimicry. He said, okay, well they've
Speaker 3: got this electric organ. Maybe I can use that electric organ.
Speaker 3: I think I can mimic the structure of this which
Speaker 3: had kind of layers and produce an electric current. And
Speaker 3: so he did created what he called a pile, which
Speaker 3: was layers of zinc and cardboard with los of acid
Speaker 3: around them. And it was because it was a black
Speaker 3: pile of pennies. And this would then produce electricity, but
Speaker 3: at a constant rate.
Speaker 4: Now in English we now call this a battery.
Speaker 3: So you've now got this continuous release of electricity. And
Speaker 3: then you can see, well, actually we can stimulate if
Speaker 3: we put these electrodes onto and they did very horrible
Speaker 3: experiments on animals, dead animals. You know, you could then
Speaker 3: make it if you put electrodes on either side of
Speaker 3: a cow's head, then its mouth would start to move,
Speaker 3: and its eyes would roll in its sockets. And in
Speaker 3: one particularly awful experiment in London, then a criminal who
Speaker 3: killed his wife and child and had been hanged was
Speaker 3: immediately taken down from the gallows as soon as he
Speaker 3: was dead, taken into a small room with about twelve
Speaker 3: learned gentleman and this experiment was done on his dead body,
Speaker 3: and of course he would then, you know, his arms
Speaker 3: started flailing about and all the rest of it.
Speaker 4: That these experiments convinced.
Speaker 3: People that there was electricity in nerves, that it wasn't
Speaker 3: just an irritant, that it was actually some kind of
Speaker 3: organizing principle.
Speaker 1: And by the way, this is starting to be one
Speaker 1: of the origins of Mary Shelley's frankenstn.
Speaker 3: It's not known, but she did regularly go to something
Speaker 3: called the Royal Institution in London, which is still there,
Speaker 3: amazing lecture theater where science is communicated to the public,
Speaker 3: and various British scientists spoke there, and at the beginning
Speaker 3: in about eighteen fourteen, there were a series of demonstrations
Speaker 3: of this power of electricity to animate dead bodies on
Speaker 3: sheep's heads and so on. You can go to the
Speaker 3: theater and see it. I mean, it was a huge,
Speaker 3: huge thing. And we don't know that Mary Godwin went,
Speaker 3: but it's pretty likely she did. And then a couple
Speaker 3: of years later, when she was whatever sixteen, she ran
Speaker 3: off with Shelley and they went on honeymoon. So they
Speaker 3: sat around in Morote ghost stories and she wrote Frankistan.
Speaker 1: One of the fascinations that I enjoyed with reading your
Speaker 1: book was all these things that we learned from biology
Speaker 1: that have created new technologies, like looking at the electric
Speaker 1: eel and the invention of the battery that fade away.
Speaker 1: Now we have a gajillion times more batteries on our
Speaker 1: planet than we have electric eels, but we have forgotten
Speaker 1: where that came from, that biomimicry. So what I really
Speaker 1: loved is the way you tie these things together. So
Speaker 1: tell us about phrenology, the pseudoscience of phrenology that was popular,
Speaker 1: and how you think this actually contributed in a positive
Speaker 1: way to how we think about neuroscience and modern times,
Speaker 1: even though phrenology itself was incorrect.
Speaker 3: If you want to have an argument with a neuroscientist,
Speaker 3: your studies the human brain in particular go groups them together.
Speaker 3: Say is function localized? Is there part of our brain
Speaker 3: that is devoted to doing a particular thing and that
Speaker 3: if you remove it you can't do that thing?
Speaker 4: Right?
Speaker 3: The answer is kind of yes, and no. Man is complicated, right.
Speaker 3: But in the nineteenth century it tormented people a great deal,
Speaker 3: partly because of the influence of French philosophy, and that
Speaker 3: the identification of the brain and the mind caused a
Speaker 3: huge problem because Descartes, who was for the French, the
Speaker 3: philosopher and no need for any other. He insisted that
Speaker 3: the mind was a united structure and therefore the brain
Speaker 3: must be as well. And if you look at the brain,
Speaker 3: it appears identical on either half, got two halves, and
Speaker 3: they look pretty symmetrical. So the French were very convinced
Speaker 3: that there was no localization of function. And yet there
Speaker 3: was this popular idea of phrenology, which is, you know,
Speaker 3: kind of grew up in the late eighteenth cent and
Speaker 3: was basically a version of oh, I don't like the
Speaker 3: look of his face, his eyes are too close together,
Speaker 3: or whatever, you know, version you he's got big ears,
Speaker 3: or whatever. Way of looking at somebody's face and not
Speaker 3: liking them and thinking that they're not shifty or whatever.
Speaker 3: This was then theorized in all sorts of ways by
Speaker 3: various thinkers who argued that by feeling the outside of
Speaker 3: your head skull, you could tell the shape of your
Speaker 3: brain and the lumps on your head, because we've all
Speaker 3: got lumps. They reveal the existence of organs and sections
Speaker 3: of the brain that are doing particular things.
Speaker 4: But rather than say, as.
Speaker 3: You know, you might say, well they're devoted to speech
Speaker 3: or you know, impulsiveness or vision, they were all about
Speaker 3: moral virtues and you know, being goodness and so basically
Speaker 3: it's like astrology, you know. And people would go and
Speaker 3: it was incredibly popular in the nineteenth century, and the
Speaker 3: scientists got really really cross about it because they said, look,
Speaker 3: you know, your.
Speaker 4: Skull's really thick.
Speaker 3: You may have a big lump on your head, but
Speaker 3: there's no lump corresponding lump underneath your brain. So this
Speaker 3: is just rubbish. So this went on until the late
Speaker 3: the early twentieth century. So this is pseudoscience, and despite
Speaker 3: it being widely not accepted, but you know, ordinary people,
Speaker 3: ordinary people from Queen Victoria to Carl Marx all thought
Speaker 3: it was very interesting.
Speaker 4: You know, there was nothing in it. It was complete,
Speaker 4: not of rubbish.
Speaker 3: But what it's got in there is this suggestion that
Speaker 3: maybe there is localization of function. And this is eventually
Speaker 3: resolved in about eighteen sixty when a French scientist called
Speaker 3: Broker was studying the brains of patients who who'd have
Speaker 3: strokes and have lost the power of speech, and he
Speaker 3: found that consistently when he did the dissections, the front
Speaker 3: left and area of the brain was damaged in these
Speaker 3: patients who had lost the power of speech. Patients who
Speaker 3: died and hadn't lost the power of speech did not
Speaker 3: have those leeches legiance.
Speaker 4: And he was really unhappy.
Speaker 3: About this because he was French and this could not
Speaker 3: be and yeah, I mean having I don't know if
Speaker 3: this has happened to you, David, but you know, sometimes
Speaker 3: you collect data and it.
Speaker 4: Just pushes you into a corner.
Speaker 3: You don't want to go to that corner because you
Speaker 3: don't think that's the way it is. But the data
Speaker 3: just in the end you've got no option. You're just
Speaker 3: going to say, well, that's the way it is, this
Speaker 3: is what happens. And poor old Broker had to say
Speaker 3: and shock the French establishment by saying there is localization
Speaker 3: of function. Of course, you can't see anything with the eye,
Speaker 3: the naked eye for this front left hand side of
Speaker 3: the brain. And then a couple and if you saw
Speaker 3: this was a couple of years ago, quite astonishing paper
Speaker 3: was produced between a group of neuroscientists were doing fMRI
Speaker 3: scans and a woman in her thirties was a secretary
Speaker 3: who had a college degree, who wrote to them. She
Speaker 3: saw an advert, you know, saying we're looking for people
Speaker 3: to have brain scanned. She said, I've been told there's
Speaker 3: something interesting about my brain. And they said, okay, yeah,
Speaker 3: come on in. They stuck her in the scanner and
Speaker 3: the left hand brain side of her brain is completely empty.
Speaker 3: There's nothing, there's a hemisphere completely and she has done
Speaker 3: this ever since birth, and yet she can speak perfectly normally.
Speaker 4: You would not know if you didn't use a scan out,
Speaker 4: you'd have no idea.
Speaker 1: I wrote about this in my book Live Wired. And
Speaker 1: you know, you see the same thing. When a child
Speaker 1: gets a hemisphere ectomy, they get half of their brain removed,
Speaker 1: let's say, because of an intractable epilepsy.
Speaker 2: Turns out they're fine.
Speaker 1: And this is because the left and right sides of
Speaker 1: the brain are sort of carbon copies of each other.
Speaker 1: They're redundant for the most part, and so you can
Speaker 1: take one out and the remaining hemisphere just rewires the
Speaker 1: real estate to drive the boat as it needs.
Speaker 4: To as long as you're very young. So it's a
Speaker 4: plasticity thing, right.
Speaker 1: They don't do hemispherectomies after about the age of eight typically, Yeah, yeah.
Speaker 3: So, And there are examples of people who have had
Speaker 3: unbelievable bad damage from strokes who then recovered their function
Speaker 3: completely her adults. But as I'm always very careful to
Speaker 3: say when I explain this to people, is this is
Speaker 3: very rare and in general strokes are pretty bad news.
Speaker 1: What's fascinating about that is sometimes somebody will get a
Speaker 1: stroke on their left side that impacts Broker's area, let's say,
Speaker 1: and they lose the ability to speak, maybe they get
Speaker 1: a lesion of Wernikey's area, or they you know, they
Speaker 1: lose the ability.
Speaker 2: To produce language correctly, they have flu into phasia.
Speaker 1: Okay, what happens is then they recover and people think, well, great,
Speaker 1: somehow that has you know, they have repaired that part
Speaker 1: of the brain. But what's in fact happened is it's
Speaker 1: just moved over to the other hemisphere and now they're
Speaker 1: taking care of language in their right hemisphere. And the
Speaker 1: way this was discovered this was, you know, fifty years ago,
Speaker 1: is then this poor person would let's say, have a
Speaker 1: stroke on their right side and they would lose language again.
Speaker 1: And so what this demonstrates is the massive plasticity the
Speaker 1: ability to move stuff around to other territories on the
Speaker 1: on the brain.
Speaker 4: Which also shows us why the answer is it localized?
Speaker 4: Man is is weird? So yes and no.
Speaker 1: This is the difficulty that neuroscience has always faced with
Speaker 1: the question about localization because sometimes, you know, if a
Speaker 1: bomb drops on the runway of an airport, you'll notice
Speaker 1: that all the planes stop, but you didn't hit the
Speaker 1: airport itself, just the runway. And this is of course
Speaker 1: what always happens with the brain. When we see brain
Speaker 1: damage and some function stops. We don't know if that's
Speaker 1: because that area was the function or just part of
Speaker 1: this larger network that has to be there for things
Speaker 1: to work for the planes to take off.
Speaker 3: I mean, it's a very interesting philosophical and methodological problem
Speaker 3: which people have been arguing about for decades, since since
Speaker 3: the beginning of the twentieth century. And now, of course
Speaker 3: we've got genetics, which does exactly the same thing. Or
Speaker 3: I knocked out this gene it's the gene for X. Well,
Speaker 3: very very occasionally it's the gene for X. But generally
Speaker 3: you've just pulled apart a component, some bit of the
Speaker 3: whole complicated network, and the whole thing falls apart, you know,
Speaker 3: And it's very difficult to know when this is going
Speaker 3: to happen. So analogies of things like bicycle wheels, you
Speaker 3: take a spoke out, you take a spoke out, and
Speaker 3: then eventually the wheel is going to collapse, but you
Speaker 3: can't it's very difficult to predict when. And it doesn't
Speaker 3: necessarily matter which order you take the spokes out or whatever.
Speaker 3: So this is very hard and it's part of the
Speaker 3: problem is it's easy for scientists to get excited about
Speaker 3: this thing. I've got this tool, and it's easy for
Speaker 3: the public to understand because it makes perfect sense. But
Speaker 3: to get back to your starting point, to get to
Speaker 3: the metaphor, if you ask the average person street you know,
Speaker 3: what is the brain like a computer?
Speaker 4: They go, yeah, well sure, but here's the thing.
Speaker 3: You know, You've just explained that you know, strokes or whatever,
Speaker 3: you can recover various elements from a function from them.
Speaker 3: But if I take out the microphone from this computer,
Speaker 3: you just won't hear me. There's no other part of it.
Speaker 3: Is going to rewire it and go okay, well ill
Speaker 3: use the camera instead. I mean, we're done.
Speaker 4: There will be no more podcast.
Speaker 3: And so that that fixity in a machine and the
Speaker 3: flexibility of anything organic, but in particular the brain, that
Speaker 3: just shows they are a different order of thing.
Speaker 1: So, by the way, I want to return to this
Speaker 1: issue about the metaphor of the brain as a computer,
Speaker 1: because you pointed out something surprising in the book that
Speaker 1: people were thinking about this the other way for a while.
Speaker 1: When von Neuman was developing the computer tell us about that.
Speaker 3: We all say the brain is like a computer. But
Speaker 3: when the first computers, like we all use now, the
Speaker 3: one with as you said, von Neumann architecture, when that
Speaker 3: was being developed, what von Neuman said he was going
Speaker 3: to do, and this is his pitch to the US
Speaker 3: government in nineteen forty five to get the vast sums
Speaker 3: of money to build this thing. Why did he she said,
Speaker 3: I'm going to build you a computer and it's going
Speaker 3: to be a computer like the brain.
Speaker 4: And why do I say that? Because I know how
Speaker 4: the brain's wired up.
Speaker 3: How do I know that because two years earlier to
Speaker 3: researchers McCullough, CA and Pits have published a paper on logic.
Speaker 4: It was called the Imminent Logic of the Nervous System
Speaker 4: in which they said, look the way the brain is wider.
Speaker 4: All the nervous systems are wired up.
Speaker 3: You've got things called synapses, which are where one neuron
Speaker 3: sends a message on to another, and you can have
Speaker 3: a straightforward relay.
Speaker 4: That's not very interesting.
Speaker 3: What gets interesting is if you've got some conditionality about
Speaker 3: You could have a two neurons coming in and the
Speaker 3: onward neuron will fire if both of them are inputs
Speaker 3: are firing, or if one but not the other, and
Speaker 3: these basic logical structures, if and then and not. Anybody
Speaker 3: who's done any logic or any programming says, wait a minute,
Speaker 3: I recognize that. And that's what McCulloch said, right, This
Speaker 3: is astonishing. This is how nervous systems are wired up.
Speaker 3: I'm going to build a computer like that now. I mean,
Speaker 3: it's not true. Nervous systems are not wired up like that.
Speaker 3: I mean, but it was a brilliant idea, and the
Speaker 3: idea of that there's this logical structure that enabled our
Speaker 3: brains or any brain function should produce an outcome to
Speaker 3: process information.
Speaker 4: And that's what von Neuman did.
Speaker 3: That architecture is hardwired into the computers that I'm using
Speaker 3: that you're all over the world.
Speaker 4: Every computer in the world uses that.
Speaker 3: Because he thought that mccullor ca and Pits had hit
Speaker 3: on while they had hit on something, but it wasn't
Speaker 3: particularly about neuroscience.
Speaker 1: So let me step back for a second, because this idea,
Speaker 1: this relationship between brains and computers, this is what we're
Speaker 1: all very used to. But give us a sense of
Speaker 1: the metaphors that people had used previously.
Speaker 3: The ancient Greeks, I mean, they thought about either many
Speaker 3: thought about something called numa pneuma. When you read this
Speaker 3: ancient Greek stuff and you think, what is this?
Speaker 4: Is it air?
Speaker 2: Is it wind?
Speaker 3: So there's this aerial phenomenon which they thought was what
Speaker 3: was going on, but they didn't, I mean, and that
Speaker 3: because you know, wind technology is the height of their technology.
Speaker 4: That's what they had, you know, that's what powered ships
Speaker 4: and so on.
Speaker 3: Then The real breakthrough, I think came with Descartes, who
Speaker 3: was in a Parisian park in the sixteen twenties wandering
Speaker 3: around and they had a load of animatronics that these
Speaker 3: statues which all work by hydraulics, which was pretty damn
Speaker 3: cool and better in some respects and clockwork at the time.
Speaker 4: And you know, you'd have a Hercules who would biff
Speaker 4: a dragon on the head with a great being.
Speaker 3: He had a club and he biffed this dragon. You know,
Speaker 3: you go to a theme park and it was basically that.
Speaker 3: But you know, I mean, just like you see old
Speaker 3: CGI today and it looks crap. Mean, this would have
Speaker 3: looked awful, but at the time it was amazing. And
Speaker 3: the key thing is that descar I mean, he didn't
Speaker 3: think they were alive, but he thought, wait a minute,
Speaker 3: maybe that's how our bodies and our nervous systems work.
Speaker 3: And he said, look, he's got this very famous drawing
Speaker 3: of this big kind of man baby touching a fire
Speaker 3: and he says, look, there's some kind of pressure from
Speaker 3: when you touch flane.
Speaker 4: The pressure goes up into the.
Speaker 3: Brain and then it bounces back it reflects, hence eventually
Speaker 3: reflex it bounces back and causes you to pull away
Speaker 3: from the fire.
Speaker 4: Now very quickly people tried to do experiments to see
Speaker 4: whether this was the case. I mean, he constructed a
Speaker 4: whole theory about it.
Speaker 3: By within twenty thirty years and seventeenth century, people had
Speaker 3: done fairly simple things like, you know, chopped a frog's
Speaker 3: nerve and no liquid came spurting out, so it didn't
Speaker 3: look like it was under some kind of hydraulic pressure.
Speaker 3: People thought of other things. Maybe they thought it was
Speaker 3: like a vibration. There's something going down the nerve. If
Speaker 3: you hit a plank at one end, you can feel
Speaker 3: the vibration at the other, which's pretty smart, but there's
Speaker 3: not much you can do about it, and thinking about,
Speaker 3: you know, a load of vibrating planks in your head
Speaker 3: doesn't really help you much.
Speaker 4: So even descartes idea wasn't really usable, but that was
Speaker 4: kind of how it was thought.
Speaker 3: And then with electricity, the big breakthrough wasn't just that
Speaker 3: electricity is what's in this weird chemical way in neurons,
Speaker 3: But as soon as that was applied in the shape
Speaker 3: of the telegraph system, then people immediately drew an analogy
Speaker 3: both ways. They said look, here's there are maps the
Speaker 3: telegraph system in the UK in the eighteen forties with
Speaker 3: all the telegraph wires which were all running along railway
Speaker 3: linees course, going down to London. And what people said
Speaker 3: is London is like the brain of the country and
Speaker 3: it receives information.
Speaker 4: This is a word they use.
Speaker 3: It's information from the provinces and it can also send
Speaker 3: instructions out and tell bits of the body politic what
Speaker 3: to do. And hey, we look at the nervous system,
Speaker 3: because they had fabulous dissections of the human nervous system.
Speaker 3: It's all going up into the brain and motor uron's
Speaker 3: coming down, et cetera. So this parallel was now we've
Speaker 3: gone from water that don't really make much sense or
Speaker 3: something electric. Now we've got a real communication metaphor of
Speaker 3: the telegraph system. And this chap called Alfred Smith, he
Speaker 3: was absolutely convinced that literally this was what was going
Speaker 3: on inside the nervous system. And he even invented the
Speaker 3: facts or a kind of telegraph, the photo.
Speaker 4: He said, if you could get.
Speaker 3: A photo electric cell and send a man image down
Speaker 3: a telegraph, why you could should be able to reproduce
Speaker 3: it at the other end. So you'd actually get and
Speaker 3: that's what's going on in your eyes, he said, going
Speaker 3: into your brain. And he drew these amazing diagrams which
Speaker 3: and I show them to computation neuroscientists.
Speaker 4: They get so excited because it looks like a primitive
Speaker 4: version of you know, the large language models that.
Speaker 3: Everybody's excited about these days, all these crisscross interactions. I mean,
Speaker 3: I've tried to make head and tail of what he
Speaker 3: was getting at, and I think he was basically bonkers.
Speaker 3: And he hads to have looked. It looks it looks familiar,
Speaker 3: but it was actually insane. He made it, made a
Speaker 3: model of the brain out of brass, these strange bits
Speaker 3: of brass that he shows in his drawings, and you
Speaker 3: look at it and think, I don't you know what
Speaker 3: could this do? How could this process information in any
Speaker 3: meaningful way? And there so you know, we got electricity.
Speaker 3: And then in the eighteen eighties you've got the problem
Speaker 3: about telegraph wise is it's just the static. They don't change,
Speaker 3: whereas we know that the brain has to respond to
Speaker 3: all sorts of different signals. And then the big change
Speaker 3: was the advent of the telephone and the telephone exchange
Speaker 3: because younger listeners.
Speaker 4: Will have no idea what one of these is.
Speaker 3: There's there's something called a telephone exchange where what would
Speaker 3: happen is you pick up your phone. You can see
Speaker 3: this in old silent movies on YouTube. You pick up
Speaker 3: your phone and there's a telephone operator who was normally
Speaker 3: a woman in the exchange, and she would a light
Speaker 3: would come on saying you picked up your phone.
Speaker 4: She would connect a wire to your light, and you would.
Speaker 3: Then ask for a particular number, and she would then
Speaker 3: connect the other end of that wire to that number.
Speaker 3: So you've got this flexibility. It's a switchboard, and I
Speaker 3: mean it's not bad. I mean it is in a
Speaker 3: way because you're rerouting information. There's nothing permanent, there's no
Speaker 3: fixed connections that can all be flexible. And that carries
Speaker 3: on into the First World War, and that's where ideas
Speaker 3: about information start to appear. Before this is mathematized, people
Speaker 3: started to use the word information. In particular chap called
Speaker 3: Edgar Adrian, who's beloved of electrophysiologists, but unknown to most people.
Speaker 3: He won the Nobel Prize. Two of his students won
Speaker 3: the Nobel Prize. He was the president of the Royal Society.
Speaker 3: He was the vice chancellor of Cambridge University. I mean
Speaker 3: you could not get greater accolades than that. And now
Speaker 3: nobody knows who he is apart from the students at
Speaker 3: the University of Lesdoo got an Adrian building. Anyway, he
Speaker 3: writes these popular science books and he tries to show
Speaker 3: to explain to people what's happening. His first person to
Speaker 3: record from a single neuron, to record a single spike.
Speaker 3: You can see it going down there as you described it.
Speaker 3: And he also shows a spike is either on or off.
Speaker 3: You either have it or you don't, so it's all
Speaker 3: or none, as it said. What the nervous system does
Speaker 3: with the information of spikes is to actually group them together.
Speaker 3: And it's an in the code as he called it.
Speaker 3: He talked about codes in nervous systems, so are codes
Speaker 3: carrying information. And he has this lovely diagram of a
Speaker 3: again another poor old frog. It's the end of a
Speaker 3: stretch a neuron which is attached to a stretch receptor.
Speaker 3: It's a stretch receptor attached to a muscle, and he
Speaker 3: puts increasing weights on it. And as he puts more
Speaker 3: and more weights, the cell stretches and you get more
Speaker 3: and more spikes, so it's still it's digital in the
Speaker 3: sense it's all or nothing each of those spikes. But
Speaker 3: what the nervous system is seeing is not zeros and ones.
Speaker 3: It's seeing an intensity code and a frequency code. How
Speaker 3: many spikes have there been a second perhaps even how
Speaker 3: they organized within a unit time.
Speaker 4: These are all stuff that people are still very interested in.
Speaker 3: So then when you finally get the computer all the
Speaker 3: in the mid late forties, early fifties, it all kinds
Speaker 3: of starts to make sense.
Speaker 4: Yes, and this is where we this is part of
Speaker 4: my You know, we haven't gone any further, right, I
Speaker 4: don't think, so let me ask you this.
Speaker 2: When I read your book, I was curious your opinion
Speaker 2: on this.
Speaker 1: So you published this in twenty twenty, and since that time,
Speaker 1: we've had this explosion with the transformer architecture and large
Speaker 1: language models, which have changed the game a little bit,
Speaker 1: by which I mean, I think this is probably true
Speaker 1: for both of us. You know, our whole career in neuroscience,
Speaker 1: we always looked at AI and sort of snickered at
Speaker 1: the idea that it was going to be anything as
Speaker 1: good as the brain, And then suddenly just a few
Speaker 1: years ago, it's got quite extraordinary, and so I'm curious
Speaker 1: how you think that changes the metaphors that we're using now.
Speaker 1: And I think we would probably agree that there's never
Speaker 1: going to be a perfect metaphor.
Speaker 2: But how do you think LM's change the way we're
Speaker 2: talking about things?
Speaker 4: Well, honestly, how they can help? How do they work?
Speaker 2: Well, they don't know.
Speaker 4: Yeah, so if you have Jeffrey Hinton, how does it work?
Speaker 4: He goes, I don't know.
Speaker 2: So this is a really interesting thing though.
Speaker 1: So for example, you and I both are interested in,
Speaker 1: let's say Eve Martyr's work where she tries to describe
Speaker 1: the lobster.
Speaker 4: The example I give.
Speaker 1: You great, Okay, great, so well here, why don't you
Speaker 1: go ahead and give it about Eve Martyr. But the
Speaker 1: point I want to make is that the questions that
Speaker 1: remain about what's happening in neuroscience, we have the same
Speaker 1: explainability crisis with lllm's.
Speaker 2: So they're not distinguished in that way.
Speaker 3: No, indeed, absolutely right, But rather it's so the example
Speaker 3: in the Eve martt so, yes, it's the lobster's stomach.
Speaker 3: She has spent a whole less a very smart woman
Speaker 3: spent her whole academic career studying the thirteen years that
Speaker 3: make up the system that controls them the activity of
Speaker 3: the lobster's stomach, and the lobster's stomach has to grind
Speaker 3: up its food a bit like a gizzard in a chicken,
Speaker 3: and it's got two rhythms, two ways of doing this,
Speaker 3: and this is controlled by the neurons. And she knows
Speaker 3: everything about those neurons. She knows the genes that are expressed,
Speaker 3: she knows all about the neurotransmitters, the neural hormones that
Speaker 3: they bathe in that kind of modulate the way that
Speaker 3: it all activates. But at the moment, she cannot explain,
Speaker 3: using the tools we've had up to now, why those
Speaker 3: thirty neurons produce those two rhythms and not others, because
Speaker 3: according to the models, they should be able to do
Speaker 3: other things, but they don't. And she can't predict, either
Speaker 3: experimentally or with a model, or by a conjunction of
Speaker 3: the two, what will happen if she is to alter
Speaker 3: the activity of one of those components. So, yeah, I mean,
Speaker 3: I take your point. This is, you know, we've got
Speaker 3: two completely inexplicable things we don't understand what's going on
Speaker 3: in the LLLM. But that's partly because the way it's built.
Speaker 1: It feels like the LM should be so straightforward to
Speaker 1: understand in the sense that we've put it in there.
Speaker 1: It's a cartoon model of the brain in the sense
Speaker 1: that it's just units and connections between the units. And
Speaker 1: what this illustrates, I think is once things reach some
Speaker 1: level of complexity, it's not clear that we have the
Speaker 1: correct metaphors or the correct tools and science to capture
Speaker 1: it in some way.
Speaker 4: Yeah.
Speaker 3: I mean, like everybody, I'm amazed by what the LLMS
Speaker 3: can do. But I mean today, may this will all
Speaker 3: be old news by the time this is released. But
Speaker 3: today the big thing is chut GPT fives come out
Speaker 3: and it still doesn't know how many bees there are
Speaker 3: in blueberry, right, And it gets very indignant and insists
Speaker 3: that there are three because the capital letter counts for two,
Speaker 3: or because the bee in the middle for berry counts
Speaker 3: for two.
Speaker 4: I mean, you know, so people are arguing with it,
Speaker 4: and this thing sounds real.
Speaker 3: It sounds that it knows what it's talking about, you know,
Speaker 3: So it's that's very confident.
Speaker 1: Here's what seems clear though, is that they are intelligent
Speaker 1: in a different way, as in, they get some simple
Speaker 1: things wrong, but they also do quite extraordinary other things
Speaker 1: that we're no good at.
Speaker 2: So it's like we've invented a species that it is
Speaker 2: just a bit different from us.
Speaker 4: So that is that true? I mean, you know you
Speaker 4: can why is it intelligent?
Speaker 3: I mean you've got any Any machine that you have
Speaker 3: enables you to do things that you can't do yourself.
Speaker 3: I mean, draw a straight line, right, I can draw
Speaker 3: straight line on my computer in a way that I
Speaker 3: can't with my hand and a pencil, and I can
Speaker 3: do it better with a pencil than I can with
Speaker 3: a rock.
Speaker 2: That's right.
Speaker 1: But I think the surprise has been for us the
Speaker 1: way that it can put ideas together in ways that
Speaker 1: had seemed just a few years ago like some magical
Speaker 1: thing that only humans could do.
Speaker 2: Writing a pair of you can ask you look, Jimmy.
Speaker 1: A shakespeareance on it, where you know, poke Kiemon meets
Speaker 1: Godzilla on the surface of Mars, and it does it
Speaker 1: instantly and extraordinarily, And these sorts of things we would
Speaker 1: not have.
Speaker 2: Expected even just a few years ago.
Speaker 3: Yeah, and we probably would have expected they'd know how
Speaker 3: many bays they were in blue booth.
Speaker 2: Yeah, exactly, so it's doing something.
Speaker 1: But here's my question for you, is the metaphor has
Speaker 1: changed a bit just the last few years from von
Speaker 1: Neumann computers to having these large language models. That change
Speaker 1: a little bit the way we think about what might
Speaker 1: be happening in the brain, whether or not it's correct
Speaker 1: that that's what's happening in the brain.
Speaker 2: For young people who are growing up right now thinking.
Speaker 1: About large language models and thinking about the brain, the
Speaker 1: metaphors are going to carry over in terms of Okay,
Speaker 1: what if you just have a giant network with lots
Speaker 1: of connections and you can pay attention in the network
Speaker 1: to certain words. That's going to be the next crop
Speaker 1: of scientists are all going to draw on that metaphor.
Speaker 4: I mean, in a way we've been here for a
Speaker 4: long time.
Speaker 3: Francis Crick, as everybody knows, co discover the double heading
Speaker 3: structure of the DNA in nineteen fifty three with Jim
Speaker 3: Watson on the basis of work that been done by
Speaker 3: Maurice Wilkins and Russell and Franklin, and then he worked
Speaker 3: in molecular genetics and then in the mid seventies he
Speaker 3: decides he's going to move into neuroscience, and then I'm
Speaker 3: working on the book, and always in every chapter except
Speaker 3: the chapter on neurotransmitters, Crick popped up. And he wasn't
Speaker 3: just popping up as you know, to somebody who was
Speaker 3: on the fringes. He was actually driving ideas and thinking
Speaker 3: and so on. In particular I was particularly impressed by
Speaker 3: was his embracing of something that was called parallel distributed programming,
Speaker 3: which was developed by Jeffrey Hinton and others in San
Speaker 3: Diego and around San Diego in California in the mid eighties,
Speaker 3: which is the ancestor of today's LMS.
Speaker 4: He knew John Hotfield and hot.
Speaker 3: Field and Hint of just won the Nobel Prize in
Speaker 3: Physics last year for their work on these neural networks
Speaker 3: and all the rest of it. And people were amazed
Speaker 3: by what these very very primitive neural networks could do.
Speaker 3: Because there'd been something called the AI winter from the
Speaker 3: excitement of the nineteen fifties. In the nineteen sixties and seventies,
Speaker 3: people thought, actually, this isn't just isn't going to work,
Speaker 3: and you.
Speaker 4: Know, no funding FREEI was a disaster.
Speaker 3: And then these tiny little programs were developed which could
Speaker 3: do things like learn the past tense is of English verbs,
Speaker 3: and they.
Speaker 4: Would make mistakes like a human child.
Speaker 3: That's so even if you told that told the computer
Speaker 3: that the past participle of go is went, it would
Speaker 3: say God, just like child. That's you know, so it's
Speaker 3: generalizing the e the most frequently accounted version, even though
Speaker 3: it's been told differently. So that got people are very excited,
Speaker 3: and Crick throughout this period was very impressed by these things,
Speaker 3: but he was continually arguing people saying, look, your models
Speaker 3: are amazing, but as models of the brain, they've.
Speaker 4: Got to be biologically relevant.
Speaker 3: They've got to be structured've got to have an organization,
Speaker 3: and that is similar to what's going on in the brain,
Speaker 3: which we didn't have much idea about. And if it
Speaker 3: relies upon, in particular, something called back propagation, which is
Speaker 3: where the signal comes back and goes up through the
Speaker 3: same connections in these computer programs, these connections equipment of neurons,
Speaker 3: and there isn't any backprop you know, it doesn't work
Speaker 3: that way. And yes, you know work arounds, but that's
Speaker 3: not the way it works. He got very irate with
Speaker 3: his erstwhile colleagues and friends because of that, And that's
Speaker 3: clearly right. You know, if you want to understand the
Speaker 3: human brain rather than perhaps you know, understand the eerie
Speaker 3: alien intelligence, if you want to stand human consciousness, then
Speaker 3: you've got to keep that in mind, I think, and
Speaker 3: crit was right about that. So until we need to
Speaker 3: know how they're doing what they're doing, I think for
Speaker 3: it to be a really useful model.
Speaker 2: So okay, a couple of things.
Speaker 1: First, I still assert that the lms are going to
Speaker 1: change our metaphors the way that people of the next
Speaker 1: generation talk about the brain, correctly or incorrectly.
Speaker 2: It's going to change the way they talk about it.
Speaker 1: And and of course there's a history back to you know,
Speaker 1: parallel distributed computation and well before that as well. But
Speaker 1: but there are step changes, and I think we've just
Speaker 1: experienced one. So that's that's my assertion.
Speaker 4: I was just wondering, if you know, I mean, you
Speaker 4: may well be right. You know, I can't. I I
Speaker 4: it'd be very interesting to see, and I just want
Speaker 4: a bit more meat on it.
Speaker 1: Yeah, you know, it may it may be that it
Speaker 1: just requires some years before we see what that what
Speaker 1: that meat looks like on the book. Yeah, let me
Speaker 1: jump to another topic that I was that I loved
Speaker 1: reading about in your book. One of the things that
Speaker 1: I talk about on this podcast, probably more than anything else,
Speaker 1: is this concept of the internal model. In other words,
Speaker 1: your brain is locked in silence and darkness and it's
Speaker 1: making a model of the outside world. And it does
Speaker 1: this eventually so it can better predict what's going to
Speaker 1: happen next. And the thing that I hadn't quite clocked
Speaker 1: until I read your book is that, at least the
Speaker 1: way that you put it, it's sort of correct me
Speaker 1: if I'm wrong here. It started with the young Craike
Speaker 1: in England who wrote a paper, and then other people
Speaker 1: picked up on this idea. Give us, give us that
Speaker 1: story about the internal model.
Speaker 3: It really goes back to Helmholtz, who's the guy who
Speaker 3: discovers the all or nothing action potential. What Helmholtz said
Speaker 3: is that because there's a big argument about ah, is
Speaker 3: the activity in say, your optic nerve, is it qualitatively
Speaker 3: different from the activity in your auditory nerve? And what
Speaker 3: Helmholtz said, it's all the same. It's just activity. And
Speaker 3: the difference is is what your brain makes of that signal.
Speaker 3: And what he says is that the brain gets stimulated
Speaker 3: and then it makes an inference about what that means,
Speaker 3: about what that is, and you can you can see
Speaker 3: this really easily, but don't do it very hard. The
Speaker 3: pressure eyeballs. Shut your eyes, pressure eyeballs, and you see colors,
Speaker 3: but there is no light. What is happening is those
Speaker 3: neurons are being activated by pressure and they're sending a
Speaker 3: signal to the brain and the brain, hey, I optic
Speaker 3: nerve it must be light, so you see light now.
Speaker 3: Craig who was a psychologist in Cambridge, he wrote he
Speaker 3: wrote this little paper where he said, well, basically, what
Speaker 3: the brain must be doing is constructing this model of
Speaker 3: the outside world of things that it can affect, and
Speaker 3: it's trying to work out what the best things to do,
Speaker 3: how can modulate this outside was so effect Therefore the
Speaker 3: input that it receives in order to achieve whatever ends
Speaker 3: it may be interested in me.
Speaker 4: They're eating you know.
Speaker 3: I'm hungry. I'm right, my body's hungry. Therefore I need
Speaker 3: to find some food, so I've got to looking for it.
Speaker 3: And then the model that the key idea is that
Speaker 3: he's got this suggestion as you say that there's a model,
Speaker 3: a representation of some kind of the outside world, and
Speaker 3: the way you can affect it is both things. It's
Speaker 3: not just a static photo, but it's also a set
Speaker 3: of potential alterations, and you can you can predict, Okay,
Speaker 3: if I go to the shop, I will be able
Speaker 3: to get a cream bun and I will satisfy my hunger.
Speaker 3: If I go to the bookshop, I will not be
Speaker 3: able to buy a cream, but you know, I can
Speaker 3: get a book. But that's not that satisfy a different
Speaker 3: kind of hunger? Is it the same kind of hunger?
Speaker 2: Noe and so on?
Speaker 3: So that model, I mean, is really quite astonishingly powerful,
Speaker 3: the idea that there's this representation at such a complex
Speaker 3: level of the outside world and of our ways of
Speaker 3: altering it that's been developed.
Speaker 4: And I mean, he was working, like many people. He
Speaker 4: was working in the war, and he was working on how.
Speaker 3: People could cope of work in the dark on chips
Speaker 3: and so on, and how they'd be able to predict
Speaker 3: a movement of enemy Aircraft's all.
Speaker 4: This was kind of classic thing that scientists were involved
Speaker 4: in it.
Speaker 3: And in his meantime he was thinking about, well, what
Speaker 3: does it all mean in terms of how the brain
Speaker 3: might function. So this little paper, and there's another one
Speaker 3: as well that he wrote, is astonishing the influential.
Speaker 1: You say in the book that it's going to take
Speaker 1: us a long time to even understand very basic things.
Speaker 1: For example, there are a lot of connectome projects which
Speaker 1: look at, hey, where are all the cells and the connections.
Speaker 1: And let's say a maggot brain, a very tiny little brain.
Speaker 1: You know, what if we understood every single bit of
Speaker 1: the wiring diagram here and yet, as we know, that
Speaker 1: hasn't really unpacked the answer for us.
Speaker 2: And so what do you see coming.
Speaker 1: Down the line?
Speaker 2: How, given this whole enormous, beautiful history that you've written
Speaker 2: of the field of neuroscience, what do you see in
Speaker 2: the future.
Speaker 3: But what we're lacking is is ideas, ways of and
Speaker 3: maybe this is where the llms are going to do
Speaker 3: it without even telling us what the damn ideas are.
Speaker 3: You know, we'll be able to put all the data in,
Speaker 3: put all the connectomic data. I'm sure if eve Marda
Speaker 3: is not doing this, she really should check it in
Speaker 3: see what the AIS makes of it all. I think
Speaker 3: we need more ideas about very concrete things, not about
Speaker 3: how conscious, what consciousness is, or how it emerges. I
Speaker 3: think we need more data on that so we can
Speaker 3: actually agree what we're studying. I guess the other thing
Speaker 3: that maybe you know, it's a negative thing to come
Speaker 3: back to Crick. You know, what he was focused on
Speaker 3: with Christophe Copp for the last twenty years was finding
Speaker 3: the neural correlates of consciousness or of retention of visual awareness.
Speaker 3: And we've done that in that we can actually record
Speaker 3: from you know, people's brains, patients who are very kindly
Speaker 3: given up there allowing people to poke around in their
Speaker 3: brain while they're having an operation, and you can identify
Speaker 3: people have now even been able to reconstruct things that
Speaker 3: they have they have seen using the activity that's recorded.
Speaker 3: So it actually got some of the things that Crick wanted.
Speaker 3: And yet it's still not it's not it. There's something
Speaker 3: still lacking. And maybe I mean the argument of the
Speaker 3: book would be it's going to be the next step
Speaker 3: in technology, and your argument that just being well, yeah,
Speaker 3: we've got that here, Well.
Speaker 4: Let's see maybe that's it, or maybe it's going to
Speaker 4: be something further on.
Speaker 2: Oh, surely it'll be something further on. And by the way.
Speaker 1: One of your arguments is that are metaphors. While they
Speaker 1: can be helpful, they also always constrain absolutely absolutely.
Speaker 3: Can't you know, they're they're I had this cute phrase,
Speaker 3: they're they're they're their frame, their frame. But a frame
Speaker 3: is not just you know, helping you put things on.
Speaker 3: It also limits you can't see outside of the frame.
Speaker 3: And that's that's why things look obvious in the past,
Speaker 3: because we can see the things that they can't. And
Speaker 3: that's why people sometimes think people in the past were stupid.
Speaker 3: But they're not very clever. They're just limited and so
Speaker 3: to we. And this is when scientists, I mean, you've
Speaker 3: been very kind, you've read the book, you've thought about
Speaker 3: a lot. But when you just chat to people and
Speaker 3: they had the unwritten God, that's amazing. And then they said, well,
Speaker 3: what's the next big thing going to be into which
Speaker 3: I say, well, you know, if I knew that, I
Speaker 3: wouldn't tell you.
Speaker 4: I'd be very rich living on my island somewhere. I
Speaker 4: had no idea.
Speaker 3: But it's going to come and it But I mean,
Speaker 3: maybe you're right, I mean I'm maybe I'm not sufficiently
Speaker 3: tuned into the world in particular of kind of human
Speaker 3: euroscience and human brain studies to see that the excitement
Speaker 3: of the parallels with the with the llms maybe changing
Speaker 3: how people are thinking things. But certainly up until twenty twenty,
Speaker 3: which is when the book was finished twenty nineteen, there
Speaker 3: was very much in the tens or whatever we call it,
Speaker 3: the teens, there's very much a sense of stagnation. I
Speaker 3: mean vast amounts of data which were people are drowning in.
Speaker 3: But also and then, what what are we going to
Speaker 3: do now this kind of uncertainty? Maybe that's being resolved.
Speaker 1: Well, I think in your language it would just be
Speaker 1: another frame given to us by the technology things that
Speaker 1: we can't say beyond that.
Speaker 4: But do you think that's happening? Is that a shift
Speaker 4: in the human.
Speaker 2: Community for two reasons though.
Speaker 1: One is looking at llms and saying, wow, that's quite
Speaker 1: extraordinary what they can produce. The other thing is just
Speaker 1: being able to use these as a tool to apply
Speaker 1: them to the massive data sets that we have shored
Speaker 1: up already and be able to say, oh, here are
Speaker 1: patterns of the data that we would never have seen ourselves.
Speaker 1: That was my interview with Matthew Cobb, neuroscientist and author
Speaker 1: of the idea of the brain. So the central lesson
Speaker 1: that surfaces in his book is that we draw our
Speaker 1: metaphors from the technology that exists around us at the time,
Speaker 1: and as we invent new technologies that gives us new
Speaker 1: insight into how the brain might be functioning. But also
Speaker 1: metaphors can limit They are frames that can block out
Speaker 1: what we're able to see. This shows us something about
Speaker 1: how science works. Despite everything that we learn in school,
Speaker 1: science is not just the accumulation of facts, but instead
Speaker 1: a grasping with our language towards something trying to get
Speaker 1: more effective metaphors. When we look back at the history
Speaker 1: of brain science, it's easy to fall into the trap
Speaker 1: of feeling like the present is the pinnacle that we
Speaker 1: finally arrived at the right way of thinking about this.
Speaker 1: We smile at the hydraulic brain of the sixteen hundreds,
Speaker 1: the clockwork brain of the Enlightenment, the telegraph brain of
Speaker 1: the eighteen hundreds, the telephone exchange of the late nineteenth century.
Speaker 1: They seem like charming relics from a simpler time, but
Speaker 1: each of those metaphors were stepping stones for our thinking.
Speaker 1: They framed the kind of experiments that people built. The
Speaker 1: data that they noticed, the questions that they thought it
Speaker 1: was even possible to ask. Every metaphor illuminated one part
Speaker 1: of the landscape, even while it left the rest in shadow,
Speaker 1: and that shadow is still with us. For my whole career,
Speaker 1: the metaphor of choice was the digital computer, the brain
Speaker 1: as hardware and software as an information processing device with
Speaker 1: inputs and outputs and circuits, and that idea has given
Speaker 1: us extraordinary advances like artificial neural networks and brain machine
Speaker 1: interfaces and entire fields of computational neuroscience. But it also
Speaker 1: narrows our vision. And in my book live Wired, I
Speaker 1: wrote that everything we think about in Silicon Valley is
Speaker 1: trim and efficient hardware with a layer of software on top.
Speaker 1: But the brain is clearly so much more than that.
Speaker 1: That's why I use the term livewear to make this
Speaker 1: distinction clear. It's a system that is constantly reconfiguring its
Speaker 1: own circuitry based on experiences. In other words, what flows
Speaker 1: through the network changes the network. Now we're all watching
Speaker 1: artificial neural networks like the Transformer architecture, which has changed
Speaker 1: our world, and those certainly seem to capture something a
Speaker 1: bit better. But whatever we use and whatever we come
Speaker 1: up with to make these distinctions. Will our descendants see
Speaker 1: our metaphors as limiting, just another product of our technological moment.
Speaker 2: Of course they will.
Speaker 1: For all we know, there may never be a final metaphor,
Speaker 1: no single model that captures it all. The brain has
Speaker 1: its own idio syncratic, massive complexity, and strangeness, making it
Speaker 1: not quite like a telegraph network, not quite like your laptop,
Speaker 1: not quite the same as an LLM. We might never
Speaker 1: have a metaphor that feels complete, because, as they say,
Speaker 1: the map is never the same as the territory. But
Speaker 1: the pursuit continues to compel us because every era's metaphor,
Speaker 1: although it's flawed, still moves us forward. The hydraulic brain
Speaker 1: led to experiments on fluid pressure. The telegraph brain inspired
Speaker 1: the search for nerve conduction speeds. The computer brain pushed
Speaker 1: us into the era of big data and AI. Our
Speaker 1: past models were all part of the process. So I'm
Speaker 1: left with two feelings, humility in the face of the
Speaker 1: unknown and excitement about what's next. Somewhere out there in
Speaker 1: the midst of the future, there is a technology or
Speaker 1: an idea that's going to give us a fresh lens
Speaker 1: one that we can't yet imagine, and with it a
Speaker 1: new way of asking the oldest question in neuroscience, how
Speaker 1: does matter give rise to mind? So as you go
Speaker 1: about your day hearing and seeing and remembering and imagining,
Speaker 1: consider this, the thing doing all that work is also
Speaker 1: the thing asking the questions about its own functioning. Despite
Speaker 1: all that we've learned, it is still, in many ways
Speaker 1: an undiscovered country, and every day we take a step
Speaker 1: deeper into that uncharted land. Go to eagleman dot com
Speaker 1: slash podcast for more information and to find further reading.
Speaker 1: Join the weekly discussions on my substack, and check out
Speaker 1: and subscribe to Inner.
Speaker 2: Cosmos on YouTube for videos.
Speaker 1: Of each episode and to leave comments until next time.
Speaker 1: I'm David Eagleman, and this is Inner Cosmos.