February 16, 2026 • 36 mins
Does brain science need a new grand plan? Is the brain less like an assembly line and more like a weather system? What does this mean for what counts as explanatory, and how might AI help us in the near future? What does any of this have to do with how the drug Ritalin got its name? Today we’ll speak with neuroscientist Nicole Rust, author of Elusive Cures.
Listen
Watch
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:05):
Does brain science need a new grand plan? Is the
brain less like an assembly line and more like a
weather system? And if so, what does this mean for
how we might go about understanding how to think about it,
and how might AI help us in the near future?
And what does this have to do with how the
(00:25):
drug riddle In got its name. Today we'll speak with
scientists Nicole Rust who's been thinking about these issues. So
get ready for a great brain stretch. Welcome to Inner
Cosmos with me David Eagleman. I'm a neuroscientist and an
author at Stanford, and in these episodes we sailed deeply
into our three pound universe to understand how we see
(00:48):
the world and for that matter, how we should view
the brain. For a very long time now, neuroscience has
been driven by the hope that if we could just
(01:10):
zoom in far enough, the brain would finally give up
its secrets, if we could just do one more electron
microscope upgrade, or nail one more molecular pathway, or get
one more brain network labeled and circled in a textbook. Now,
the approach so far of gathering tons of data has
delivered real triumphs. We've learned an enormous amount about how
(01:35):
neurons fire, how circuits form, how chemicals are released and sensed,
And when you flip open any modern neuroscience textbook, it
really is a marvel. It's densely packed with discoveries that
would have been unimaginable a generation ago. But there's an
(01:55):
uncomfortable question hovering in the background. If we understand so
much much more than we used to, why do so
many neuroscience problems remain so stubbornly unsolved? Why do entire
classes of brain disorders like psychiatric illness or neuroggeneration, or
(02:16):
disorders of mood and thought continue to resist our best
efforts And it feels like that's been happening decade after decade.
Why does it feel sometimes like knowledge is accelerating, but
meaningful clinical breakthroughs lag behind. These questions force us to
ask whether the challenge lies in the way we're framing
(02:39):
the problem. That is, maybe we should be asking whether
the brain is a different kind of system than the
metaphors we've relied on. We should be asking whether reductionism,
which is figuring out all the pieces and parts, can
ever by itself, fully explain something that evolved to be
(03:01):
adaptive and live wired, and where you have eighty six
billion neurons that are like live little creatures, moving and
adjusting every moment of your life. Every scientific field eventually
reaches moments like this, moments where success at one scale
reveals blind spots in another. Fields reach a point where
(03:24):
accumulating facts is no longer sufficient and what's needed instead
is a rethinking of first principles. That's the moment neuroscience
may be in now, and it's why I want to
talk with today's guest. Nicole Rust is a neuroscientist at
the University of Pennsylvania, and she has spent years thinking
(03:46):
deeply about her experiments and data, but also, in more
recent years thinking about the trajectory of the field itself,
about how we got here and what assumptions we've inherited,
and what kinds of questions we might have to ask
if we want to move forward in a meaningful way.
She's written a great book about this, called Elusive Cures.
(04:10):
Nicole is part of a growing group of scientists who
are stepping back from the daily grind of incremental results
to ask a simple and hard question, what kind of
thing is the brain? Really? What would it mean to
study it on its own terms. So today Nicole and
I sat down to talk about neuroscience at a crossroads,
(04:32):
about complexity, what counts as an explanation, and the challenge
of understanding the most intricate system we've ever encountered. Okay, So, Nicole,
a few years ago you started working on this idea
that we need a new grand plan in neuroscience. What
(04:55):
led you to that conclusion?
Speaker 2 (04:57):
I was hearing concerns from the heads of funding agencies
and elsewhere that while researchers had been discovering a lot
of things about the brain, those discoveries hadn't been moving
the needle in helping individuals with certain classes of disorders.
Speaker 1 (05:17):
So you know, one of the textbooks in our field
is Principles of Neuroscience. That it just keeps getting fatter
over the years, absolutely, and it always has struck us
that if it really were principles, it should be getting thinner.
But what we just keep doing is a dated dump
of all the information we're getting. But your point is
we're not seeing, Ah, here's the clear pathway to solving
(05:40):
certain problems exactly.
Speaker 3 (05:41):
For certain conditions.
Speaker 2 (05:43):
So for some conditions we have been moving the needle
quite effectively. And so those include things like new drugs
from ingrained headache or insomnia, epilepsy and pain. But there
are other classes of conditions that we've been more frustrated with.
Speaker 3 (05:58):
And so yeah, that's the big question.
Speaker 1 (06:00):
So one of the arguments you make in your new
book is that many of the pharmaceutical treatments that we have,
for example, were discovered by accidents, So things like pain
or ADHD or in some cases depression. So tell us
about that. What's the story there?
Speaker 2 (06:15):
Yes, absolutely, so those stories are wonderful, the serendipitous discoveries
that happened long ago before we knew much about the
brain at all. One example is the first antidepressant, which
was discovered during clinical trials for the lung infecting bacteria tuberculosis.
So their clinical trials for the drug for TB and
what they found was the patients were joyous. There's even
(06:38):
a picture of light in Life magazine of them dancing around.
They were so happy. So they realized this chemical probably
has a different purpose. They put it through clinical trials
and it became our first antidepressant.
Speaker 1 (06:48):
And what was the name of that drug.
Speaker 3 (06:50):
Ipronia is it?
Speaker 1 (06:51):
And so that was totally an accident.
Speaker 3 (06:53):
It was totally an accident.
Speaker 1 (06:54):
And interestingly, you know the history of medical science is
shot through with these sorts of accidents, really is Yeah,
tell us about pain medications.
Speaker 2 (07:03):
Pain medications? So are opioid drugs? Those come from ancient
Mesopotamia where the Mesopotamians were harvesting opium from the poppy plants.
And our drugs today, like oxycodone, are just a slow
release form of that drug that we harvested from opium
in the early nineteen hundreds.
Speaker 1 (07:22):
How do they end up ingesting that?
Speaker 3 (07:24):
I don't know. That's a great question.
Speaker 1 (07:25):
That's a great question.
Speaker 3 (07:26):
How did they figure it out?
Speaker 1 (07:27):
Yeah? Yeah, yeah, okay. So and adhd M.
Speaker 3 (07:32):
That's another great one. Riddlin.
Speaker 2 (07:34):
So, Ridlin was developed in the nineteen forties by a
chemist who was Swiss, and he was using a technique
that we call try it and see what happens. We
don't do that much anymore. But so he synthesized the drug.
He liked it.
Speaker 3 (07:48):
He gave some to his wife. She liked it too, because.
Speaker 2 (07:51):
It improved her tennis game, and so he named it
after her. Her name was Rita, and that's why we
call it Rita Lynn. So another great story of as
a drug that happened long before we understood much about
the brain at all and certainly wasn't based on some
big discovery about the brain that led to a new breakthrough.
So there are a lot of discoveries like these.
Speaker 3 (08:09):
Yeah.
Speaker 1 (08:09):
Great. So your argument is that several of the drugs
that we have were totally accidental. And when it comes
to things that involve science as we typically do it,
where we say hey, look here's the gene, here's the
chemical involved, and so on, it's an enormous undertaking. So
give us a sense of let's say, for insomnia.
Speaker 2 (08:32):
Yes, yes, you're right, when a new discovery leads to
a new drug, those discovery stories are absolutely epic. So
one example of that. A drug for insomnia is subarexcent,
so superreccent. The way it works is it blocks chemicals
in our brain that actually keep us awake. And so
(08:53):
the discovery of superreccent dates back to nineteen ninety eight
when brain researchers discovered these chemicals in our brain the
first time. They were then linked later to insomnia via
studying some dogs that had genetically inherited narcolepsy. So these
dogs fall asleep spontaneously during.
Speaker 1 (09:11):
The day, and this was the chemical erectionin.
Speaker 3 (09:13):
These chemical ereccin exactly.
Speaker 2 (09:15):
And yeah, so they figured out this was a problem
in the erecxin pathway in the brain. It was then
linked to human narcilepsy. And once researchers discovered that there
are these chemicals in our brain that exists to keep
us awake, the assumption was that at least some of
us have insomnia because these chemicals are too active. So
the pharmaceutical industry went wild trying to find chemicals to
(09:36):
block the effectiveness of these keep you awake, the erecxins
in the brain. And so Mark then went through to
try to find such a chemical. They screened two million
different chemicals to find the right one, and once they
found a chemical it was effective, they improved it even
further to increase its efficacy reduce its side effects. So
(09:57):
Whurexcin then went through clinical trials and merged in twenty
fourteen as a new drug. So altogether there was a
sixteen year process from the big discovery about the brain
the erecsans to this new drug to block their activity.
Speaker 1 (10:12):
And what kind of money is involved in that?
Speaker 3 (10:13):
It was about a billion dollars.
Speaker 2 (10:15):
Yeah, and that's about as quick as has ever happened
from a big discovery to a new therapy.
Speaker 3 (10:21):
Yeah. So it's absolutely epic.
Speaker 1 (10:22):
Got it. So many discoveries are accidental. Ones that aren't
accidental are epic in terms of the amount of time
and money they take. So where does that put us
in modern neuroscience research. Let's jump to nineteen ninety eight
when Eric Candell wrote a paper suggesting, look, here's the
framework by which we should think about these things.
Speaker 3 (10:43):
Yeah.
Speaker 2 (10:43):
So in Eric Kendall's nineteen ninety eight paper, he was
really channeling the ethos of an era of brain research
that followed on excitement around two big new technologies, our
ability to sequence genes and image the human brain non
invasively with techniques such as functional nandecoresonance imaging.
Speaker 3 (11:02):
And Yeah, he laid out.
Speaker 2 (11:04):
A proposal of the new intellectual framework, as he called it. So,
in Kendell's framework, it all begins with genes. Our genes
are the code that is used to make our brain cells,
which are wired into these circuits, and it's the activation
of those circuits that give rise to all of mental
function and in term behavior, Kendell suggested that there's one
(11:24):
big feedback loop, so our behavior, our interactions with the
world feedback to shape how our brains are wired up.
Speaker 3 (11:30):
That's learning.
Speaker 2 (11:32):
And Kendell focused on this big arrow from how the
brain gives rise to the mind as the great challenge
for psychologists and biologists to delineate the relationship between those
two things.
Speaker 1 (11:43):
And the arrow is pointing from genes, two circuits exsolately, Yes,
experience and behavior.
Speaker 2 (11:48):
Okay, so yeah, to summarize this idea about the brain
and the type of thing it is, it's really set
up as a big chain of causes that lead to effects.
And the notion then is that when the brain becomes dysfunctional,
when you have some type of disorder, it's a broken.
Speaker 3 (12:02):
Link in the chain.
Speaker 2 (12:03):
It might be a mutated gene that leads to a
disorder that you might want to target with a drug,
or it might be a part of the brain has
aberrant activity which you could then target with stimulation.
Speaker 3 (12:13):
So this era of brain research I.
Speaker 2 (12:15):
Like to call find the broken link in the chain
so we can go in and target it for a fix.
And that example that we just talked about super excent
it was very much of that type of find the
broken link in the chain target it for a fixed
type of approach that led to that big discovery.
Speaker 1 (12:44):
Right, So sometimes that works, and that probably felt like
real progress. I'm sure when Eric Candell no Bel laureate
wrote this paper in ninety eight, he felt like, Hey,
we're really simplifying this and getting this straight how one
thing leads to another. But when you take a look
at what's going on in the field, you think that's
somehow not sufficient.
Speaker 2 (13:05):
Absolutely so, there are certain classes of disorders that have
really proven to be somewhat impenetrable using that type of
find the broken link in a chain approach. What's then example,
So they include our psychiatric conditions like depression and anxiety
and schizophrenia. So those are all cases in which we
do have therapies, but they don't work for everyone. And
(13:26):
many of those therapies date to pre date our understanding
of the brain, so they were discovered serendipitously. Also our
neurodegenerative conditions like Alzheimer's and Parkinson's and als, where we
do have some treatments in some cases, for example Parkinson's,
but we don't have ways to slow down the degeneration
that's happening in the brain that's leading to the decline.
Speaker 1 (13:47):
In other words, when we look at all these disorders,
we think, wow, this is really somehow more complicated. And
why because when we look for, let's say, a gene
for schizophrenia, what do we find.
Speaker 2 (13:58):
Absolutely so, in the case of schizophrenia, it's very rare
to have a single gene variation or mutation that leads
to the disorder. More likely, well, now that we've sequenced
lots of genes, we know that it's variation in hundreds
of genes that are tied to the condition. So if
(14:19):
one identical twin has schizophrenia, the chances of the other
identical twin having schizophrenia they're fifty percent. It's not one
hundred percent, it's fifty percent. So there is a big
genetic component to all of this, but there are also
environmental effects and other issues at.
Speaker 1 (14:35):
Play, and these intertwine in ways that are super complex.
As a side note, you know, the first gene pulled
for a major disease was for hunting tins and it
was a gene and if you have that gene, you're
going to die of hunting tins unless you dive something
else first, Yes, and everyone thought this is great, We're
going to figure out the gene that goes with every disease,
and it turned out to be much more complicated.
Speaker 2 (14:56):
Yeah, And even now, thirty years later, we still don't
have an effect of treatment for Huntingtons, although fingers crossed,
it looks like maybe there might be one on the
way in clinical trials, but it's taken over thirty years
to get there, even when we knew exactly what the problem.
Speaker 1 (15:10):
Was, right, Okay, So but for something like schizphrenia or
major depressive disorder, we're looking at something that's much more complicated.
We can't even find a single gene for it. As
you said, we find hundreds of genes. So where does
that put us?
Speaker 2 (15:24):
Yeah, so I think researchers are definitely waking up to
the idea that this idea about the brain as a big,
long chain is probably oversimplified. The human brain is often
held up as the most complex thing in the entire universe,
and this chain of causes the lead to effects, it's
not very complicated. So we might ask ourselves, well, what's
(15:45):
so complicated about the brain and what are we missing
in this chain?
Speaker 3 (15:50):
Like idea.
Speaker 1 (15:52):
And by the way, it still might be causes leading
to effects, right, but it's the feedback loops at every
stage exactly.
Speaker 2 (15:58):
That's the complexity that we're beginning to embrace. So causes
that lead to effects that feed back on themselves as causes.
The brain is not like a chain in this type
of idea. It's a whole different type of thing.
Speaker 1 (16:10):
Right. So your analogy that you make in the book,
which is wonderful, is like a weather system, right, So
unpack that for us.
Speaker 2 (16:19):
Yes, so you can think about these complex systems that
have many interdependent parts, and the weather is a terrific
example of that, and you can think about when a
complex system goes awry, it's a lot like a weather
breaking out into a storm.
Speaker 1 (16:35):
Yeah.
Speaker 2 (16:36):
We know a lot about systems like these because we've
studied them quite extensively. And one thing we know about
them is they're very, very hard to perturb in ways
that you would want to shift them out of their storms,
which in the case of the brain, would be shifting
the brain from a less healthy to a more healthy state.
Speaker 1 (16:54):
And one second tangent in your book, you tell us
a wonderful story about Johnny von Neuman and weather I
had no idea tell us then.
Speaker 3 (17:01):
Yeah.
Speaker 2 (17:02):
So in the nineteen forties, the end goal of weather
research really was to control the weather. Researchers wanted to
not just dissipate hurricanes, which is a worthy goal in
and of itself, but they also even wanted to weaponize
the weathers.
Speaker 1 (17:15):
This was the US government that, This was.
Speaker 3 (17:17):
The US government.
Speaker 2 (17:18):
Yeah, so they were very interested in funding weather research
with that end goal. Part of von Neumann's development of
the first computers was explicitly in the end goal, first
predict the weather, then learn how to control it.
Speaker 3 (17:34):
And so researchers tried that out and it didn't work out.
Speaker 1 (17:38):
So well, and it still hasn't worked out.
Speaker 3 (17:40):
It still hasn't worked out.
Speaker 1 (17:41):
And why it's because the weather's so complicated.
Speaker 2 (17:43):
It's so complicated, and it is a system because you
have these big feedback loops, right, any type of intervention
you try to do will reverberate in unexpected ways, and
so that's what makes these systems really really difficult to control.
Speaker 1 (17:56):
So when we look at something like major depressive disorder,
the temptation to say, look, if we could just find
the gene. There is no theugen, but if we could
just do this pharmaceutical here there, we can solve this,
and that has proved to be ineffective precisely because of
the complexity of the system here.
Speaker 3 (18:13):
Yes, absolutely, and so.
Speaker 1 (18:15):
One example that you talked about in the book was
emotions research and the one hundred years' war that's happened there.
So explain to us what that is.
Speaker 2 (18:24):
Yes, So, our ability to understand what's happening in many
of the psychiatric conditions comes down to wanting to be
able to measure an emotion, say, in the brain, and
that's proven to be very difficult to try to do.
Speaker 1 (18:37):
So.
Speaker 2 (18:38):
Researchers for over one hundred years have been arguing about
what types of things are emotions in the brain and
how are they organized. It might be that different emotions
like fear and disgust and happiness, they might be organized
in kind of their own little compartments in the brain,
kind of like our sensory systems where we have one
part of our brain for vision and another part for hearing,
(18:59):
Or might be that they're much more intermingled in the
brain such that and a lot like color vision. Right,
So color vision, we have one visual system and there's
a continuous space in our brain upon which we put
labels like cyan and red and magenta. We don't really
know how emotions are organized in our brain, whether they're
(19:19):
more like these compartments or more continuously, but understanding that
is one of the keys to trying to measure an
emotion in the brain. You have to figure out where
is it that you want to look and how is
it going to be reflected there?
Speaker 1 (19:31):
Yeah, and so that's led to this one hundred years
war because there are people on both sides of this argument.
It's either separate or it's spectral.
Speaker 3 (19:38):
Absolutely, yeah.
Speaker 1 (19:39):
And so we're looking at things like emotions and we
all want an explanation for it. But the question is
what will it take for us to be able to
answer something like that.
Speaker 2 (19:47):
It will take an appreciation that the way that emotions
manifest in the brain is not going to be simple
and straightforward. It's not going to be an individual neuron
that's activated when we feel aggression or sadness.
Speaker 3 (20:01):
It's going to take.
Speaker 2 (20:02):
Embracing these ideas that in the brain, if brain area
a sense information to b be send information back to
A again, and so we expect emotions to be reflected
in ways that kind of reverberate and dynamically evolve in
the brain as opposed to snapshots.
Speaker 3 (20:16):
That you could take a picture of.
Speaker 1 (20:17):
That's a really simple way, and in fact, it might
not even be that we can talk about in let's
cut two one hundred years from now, that we can
talk about area A and area B, right, because in
a sense, the whole brain is spectral in the sense
of you've got eighty six billion neurons that are all
doing their things, but they don't have border walls between them.
(20:38):
So what we do as neuroscientists as we say, oh,
this area seems to be involved in blah, but boy,
these things are spread out.
Speaker 3 (20:46):
How do you think about that?
Speaker 2 (20:47):
When you think about the brain in terms of how
compartmentalized is it? How much is everything everywhere all at once?
What's your take on that? You've thought a lot about it?
Speaker 1 (20:56):
I know, I mean, you know, so starting with the
experiments of Carl Lashly whatever last century, as you all know,
Lashley was trying to figure out where is a memory stored?
So he trained little mice to run a maze and
then he would cut parts of the brain to see, Okay,
where is that memory stored? So if I take all
(21:17):
these rats and I cut different parts. Where can I
find the memory store? And what he found is that
none of the experiments yielded anything because the memory is
somehow stored in a distributed manner. It's more like cloud
computing rather than here's my hard drive and you've just
broken the hard drive. And so that was one of
the first examples of Wow, we're looking at a big
(21:37):
complex system here where stuff is really distributed in ways
that's hard for us as humans to say, oh, yeah,
you're just restoring zeros and ones there. It's a very
different sort of thing. Every attempt we've made to compartmentalize
the brain doesn't seem to hold that well over time.
We still do find temptations say, look, this is the
(22:00):
visual cortex and this is auditory and so on, and
that's mostly true. But even embedded in here, you've got many,
many neurons that are reaching across long distances to talk
to other areas. And you know, when we look at
baby's brains, we find, you know, there are neurons and
the auditory cortex that are that are activating the visual
(22:22):
cortex when they're sound, and in the visual cortext they
are activating the auditory cortex when their site and as
we grow, those things start talking less to each other,
but they're still there. And if you go, let's say blind,
at some point those neurons sitting in an auditory cortex
will start We'll start taking over that territory right away,
because those cross connections are all sitting there. I love
(22:45):
the fact that you're pursuing this because it is a
system that we have always been tempted to simplify and say, Okay, look,
it's probably going to be this. And by the way,
this is the wonderful thing about science is saying hey, hey,
there's going to be a way to really simplify this,
and that's where we get progress. And yet we've attempted
(23:06):
to oversimplify here.
Speaker 2 (23:07):
Absolutely, Yeah, I completely agree with that. Yeah, but I
think we're also ready for the first time in history
to take on the complexity like we've never been able
to do this before. So it is an exciting era
for brain research to build on this oversimplification.
Speaker 1 (23:21):
That's right. And so you've been looking at other systems
and other scientific voices from the last fifty years that
have suggested things. So what do you see as possible
ways forward there.
Speaker 3 (23:34):
Yes, so.
Speaker 2 (23:36):
There's been a long thread through brain research. It's been
more of an undercurrent than the most dominant idea that
the way we should be thinking about the brain is
something much more akin to the weather, a dynamical system
where we're interested in how it evolves in time in
terms of things like it's patterns of activity and how
(23:59):
it is structured, not just as a computer, but something
that's continuously adapting to change. And these ideas date back
to Norbert Reener in cybernetics in the nineteen forties and
there's been an undercurrent of them throughout history and brain research,
including John Hopfield's Nobel Prize on he won in twenty
(24:21):
twenty four for physics for these ideas based on work
that he did in the nineteen eighties.
Speaker 1 (24:25):
And tell us about cybernetics.
Speaker 2 (24:27):
Cybernetics was this idea that the brain exists to control
the body and interact with the environment and a big
feedback loop.
Speaker 3 (24:38):
That was the gist of cybernetics.
Speaker 1 (24:40):
Yeah, and so that was Norbert Veener and other people
have built on that idea of having dynamic systems, lots
of feedback loops and so where do you see that
moving forward. So if we think today, okay, look, let's
think of the brain as a very complicated system with
lots of feedback. How do you tackle something like that.
Speaker 2 (24:58):
Well, there are a couple of different things are really important.
One is because these types of systems are so integrated,
you have to measure all their parts at the same time.
You can't measure their parts one at a time, And
for the first time in history, we're able to do that.
Twenty years ago, when I was recording from brain cells
and looking at their activity, I was able to look
(25:19):
at one at a time. Today we can record from
one million brain cells simultaneously in a mouse.
Speaker 3 (25:27):
It's remarkable.
Speaker 2 (25:28):
That's exactly the type of data that you need in
order to understand how all these brain cells are interacting
with one another. We also have to build these really
complicated models to make.
Speaker 3 (25:39):
Sense of these dynamical systems.
Speaker 2 (25:40):
Again, causes lead to effects that feedback on themselves as causes.
These are not things you can think through and try
to reason through. You need computers in order to do this,
and for the first time in history, we have artificial
intelligence of a type that can actually help us sift
through and make sense of this data. And build computer
(26:01):
programs that rival something as complicated as the types of
things that we can do. So it's a really exciting
era those two technologies, biotechnology and artificial intelligence coming together
in order to enable us to really embrace this type
of complexity.
Speaker 1 (26:30):
Give us a sense of, for example, David Anderson's lab
at Caltech, how he looks at this giant data and
figures out, hey, here's a way to capture what's going on.
Speaker 2 (26:39):
Yeah, that's a great example, and it's so relevant to
a problem that we've really been struggling with, and that
is how do we measure an emotion in the brain.
So in David's lab, he is really interested in the
evolutionarily ancient emotions like aggression, fighting, or feeding, and he
looks into a part of the brain that we know
(27:01):
is involved, the hypothalamus. And we know it's involved because
if you naturally, if you put two male mice together.
Speaker 3 (27:08):
They'll fight. They're aggressive.
Speaker 2 (27:10):
If you stimulate the hypothalamus of a mouse, even if
they're all alone, it will cause that type of aggression.
And if a mouse has damage to their hypothalamus, they
won't be aggressive anymore. So we know the hypothalamus is
definitely involved in mouse aggression. But if you look at
the activity of the brain cells in that part of
the hypothalamus, it really just doesn't make any sense because
(27:32):
not very many of them are active when the mice
are aggressive, and even the brain cells that are activated
during aggression they do all sorts of other things as well,
So you really can't look in the hypothalamus and understand
why is it that this part of the brain is
so important for aggression.
Speaker 1 (27:50):
In other words, it's not like the cells turn on
and then turn off.
Speaker 2 (27:53):
Okay, yep, yeah, It's just not an obvious answer. And
so these researchers in this group they started to shift
to this new way of thinking about the brain, not
as a big chain, but again as one of these
systems with these big feedback loops. So they shifted to
this new way of thinking about activity and the hypothalamus
that is a lot like a landscape of hills and valleys,
(28:16):
where at any one point in time, the activity of
the hypothalamus is somewhere on that landscape, and where it
falls where it ends up, determines how aggressive the mouse
will be.
Speaker 1 (28:28):
So you're measuring all the cells and you're representing it
as a point on the landscape.
Speaker 3 (28:33):
Yes, that's right.
Speaker 2 (28:34):
So at any one point in time, the activity and
the hypothalamus will be somewhere on this landscape, and where
it ends up falling in the valley along this long
line determines how aggressive the mouse will be. At one
end of the valley, that will translate into a mouse
that's not going to be aggressive, perhaps because what they've
(28:54):
seen is maybe a female mouse or not a mouse
at all.
Speaker 3 (28:57):
On the other end.
Speaker 2 (28:59):
Of the valley, that's where the population ends up sitting,
that will cause the mouse to be aggressive. And they
could see that this was true, not just by doing
observational work where they observe what's happening in the hypothalamus,
but they actually could use this new generation of tools
where they could causally perturb the system and confirm that
that indeed was causing the mice to be aggressive.
Speaker 1 (29:21):
Amazing. So instead of looking at a particular cell or
a group of cells and trying to think through it,
you have to take all the cells and collapse that
high dimensional activity onto a point on a landscape, and
then you can start describing what that landscape is doing.
Speaker 2 (29:36):
Absolutely, and the big shift here is that that landscape
can't be shaped by a big chain of causes.
Speaker 3 (29:44):
It lead to effects.
Speaker 2 (29:45):
The formation of the landscape depends on thinking about the
brain as having these big feedback loops in it.
Speaker 1 (29:51):
Yeah, you know, it's funny. Even in any neuroscience textbook,
you know you have sell A talks to sell B.
And of course, so we know that every cell in
the cortex is talking to you about ten thousand of
its neighbors, and lots of these are very complicated feedback loops,
and of course you have excitatory and inhibitory neurons, and
so straight away, I think any clever student looks at
(30:13):
this and says, wait a minute, something is something is
crazy here to think about? Oh a does s? And
yet our textbooks still read that way because we don't
know how to teach in a way where we're saying, look,
start from square one, we're going to talk about dynamical systems.
So how would you think about revising the way we
(30:33):
teach neuroscience.
Speaker 2 (30:35):
That's a really important question. Back in the nineteen forties
and fifties, we used to have an ecology food chains,
and then at some point they became food webs because
we realized that these ecological systems. There are these complex
dynamical systems with these big feedback loops in them, and
(30:55):
so we started to teach starting from elementary school, we
started to teach ecology differently, and so, yeah, I very
much think that that's what we need to start doing
in brain research as well, is starting from the beginning
teaching about the brain as a system chuck full of
these feedback loops and what are all of the consequences
of that.
Speaker 1 (31:13):
Yeah, And even if dynamical systems science as we understand
it now turns out not to be the full picture,
at least we're getting closer.
Speaker 3 (31:21):
Absolutely.
Speaker 1 (31:22):
Yeah. And Nicole, despite the limitations and where neuroscience research
has gone, you're very optimistic.
Speaker 3 (31:28):
Tell us why absolutely.
Speaker 2 (31:31):
When I started to write this book, I actually wasn't
sure where it would lead, and I started from a
place of kind of confusion and even a little bit
of pessimism because I could see that there were these
certain conditions for which we were get a little bit stuck.
On the other side of writing the book, I'm unequivocally
(31:51):
optimistic about the future of our field for the conditions
like the psychiatric conditions and their degenerative conditions.
Speaker 1 (31:58):
And why it's.
Speaker 2 (31:59):
Because I see that the changes that need to happen
are already happening in our fields. Right we were oversimplifying
the brain. We were treating it like this chain of
causes that lead to effects, and it was just a
massive oversimplification of the most complex thing in the entire
known universe.
Speaker 3 (32:15):
But now researchers are starting to embrace.
Speaker 2 (32:17):
This important type of complexity that we can again for
the first time in history, because we have new biotechnology,
we have artificial intelligence. For the first time, we're really
to study the brain in this way, and I am
very excited about the idea that that will be the
key to unlocking progress for all of the millions, billions
(32:37):
actually of individuals who are suffering from these conditions.
Speaker 1 (32:45):
That was my interview with Nicole Rust. This conversation circled
around the idea that the brain may not be the
kind of object we once hoped it was. For a
long time, neuroscience advanced under a parsimonious assumption that if
we can you could just identify the right pieces, the
right links in the chain, the story would come into focus.
(33:07):
Genes lead to proteins. Proteins built cells, cells form circuits,
Circuits generate thoughts and motions and behavior fix the broken link,
and the system heals. Sometimes that strategy works, but there
are entire domains where it doesn't, where no single gene
or molecule or brain region carries the explanatory weight that
(33:29):
we wanted to. Gradually, it's become clear to us that
most disorders don't behave like oh, there's a broken part,
but instead you have altered states of a whole system.
That means you can't just swap out apart. You have
to figure out if it's possible to nudge a complex
landscape that realization slash. That admission changes a lot, because
(33:54):
it reveals that the brain is more like a dynamic
environment shaped by feedback loops and continual self adjustment. It's
a system that can settle into values of activity that
are hard to escape. And by the way, it's a
system whose behavior depends not just on what's out there
in front of it now, but often on many things
(34:17):
that have interacted with it throughout its lifetime. So this
reframing has consequences for how we do experiments, for one,
but also it explains why some breakthroughs arrive accidentally while
others require decades of effort. It sheds light on why
prediction is hard, why control is even harder, and why
(34:40):
treating brain disorders sometimes resembles influencing the weather in Nicole's analogy,
more than it resembles fixing an engine. But although this
might seem like a pessimistic story, it is in fact
an optimistic one because for the first time, we might
actually have the tools to take this complexity seriously. We
(35:01):
can measure vast populations of neurons that once, we can
model systems that evolve in time. We can leverage artificial
intelligence to help us see patterns that are invisible to
our intuition alone. In other words, neuroscience may finally be
growing into the kind of science the brain requires. Every
(35:24):
mature field eventually has to let go of its simplest metaphors.
Physics moved beyond clockwork, universes, ecology moved from food chains
to food webs, and now neuroscience may be moving beyond
linear causality towards something richer and stranger and closer to
(35:45):
the truth. The challenge ahead is about learning how to
think in dynamic landscapes instead of static links, and if
we get that right, the payoff is going to be
new ways of helping the millions of people whose lives
are shaped by brains that have settled into difficult states,
and that's where the next era of neuroscience is going
(36:08):
to really begin. Go to eagleman dot com slash podcast
for more information and to find further reading. Join the
weekly discussions on my substack and check out Subscribe to
Inner Cosmos on YouTube for videos of each episode and
(36:29):
to leave comments until next time. I'm David Eagleman and
this is Inner Cosmos.
Does brain science need a new grand plan? Is the
brain less like an assembly line and more like a
weather system? And if so, what does this mean for
how we might go about understanding how to think about it,
and how might AI help us in the near future?
And what does this have to do with how the
(00:25):
drug riddle In got its name. Today we'll speak with
scientists Nicole Rust who's been thinking about these issues. So
get ready for a great brain stretch. Welcome to Inner
Cosmos with me David Eagleman. I'm a neuroscientist and an
author at Stanford, and in these episodes we sailed deeply
into our three pound universe to understand how we see
(00:48):
the world and for that matter, how we should view
the brain. For a very long time now, neuroscience has
been driven by the hope that if we could just
(01:10):
zoom in far enough, the brain would finally give up
its secrets, if we could just do one more electron
microscope upgrade, or nail one more molecular pathway, or get
one more brain network labeled and circled in a textbook. Now,
the approach so far of gathering tons of data has
delivered real triumphs. We've learned an enormous amount about how
(01:35):
neurons fire, how circuits form, how chemicals are released and sensed,
And when you flip open any modern neuroscience textbook, it
really is a marvel. It's densely packed with discoveries that
would have been unimaginable a generation ago. But there's an
(01:55):
uncomfortable question hovering in the background. If we understand so
much much more than we used to, why do so
many neuroscience problems remain so stubbornly unsolved? Why do entire
classes of brain disorders like psychiatric illness or neuroggeneration, or
(02:16):
disorders of mood and thought continue to resist our best
efforts And it feels like that's been happening decade after decade.
Why does it feel sometimes like knowledge is accelerating, but
meaningful clinical breakthroughs lag behind. These questions force us to
ask whether the challenge lies in the way we're framing
(02:39):
the problem. That is, maybe we should be asking whether
the brain is a different kind of system than the
metaphors we've relied on. We should be asking whether reductionism,
which is figuring out all the pieces and parts, can
ever by itself, fully explain something that evolved to be
(03:01):
adaptive and live wired, and where you have eighty six
billion neurons that are like live little creatures, moving and
adjusting every moment of your life. Every scientific field eventually
reaches moments like this, moments where success at one scale
reveals blind spots in another. Fields reach a point where
(03:24):
accumulating facts is no longer sufficient and what's needed instead
is a rethinking of first principles. That's the moment neuroscience
may be in now, and it's why I want to
talk with today's guest. Nicole Rust is a neuroscientist at
the University of Pennsylvania, and she has spent years thinking
(03:46):
deeply about her experiments and data, but also, in more
recent years thinking about the trajectory of the field itself,
about how we got here and what assumptions we've inherited,
and what kinds of questions we might have to ask
if we want to move forward in a meaningful way.
She's written a great book about this, called Elusive Cures.
(04:10):
Nicole is part of a growing group of scientists who
are stepping back from the daily grind of incremental results
to ask a simple and hard question, what kind of
thing is the brain? Really? What would it mean to
study it on its own terms. So today Nicole and
I sat down to talk about neuroscience at a crossroads,
(04:32):
about complexity, what counts as an explanation, and the challenge
of understanding the most intricate system we've ever encountered. Okay, So, Nicole,
a few years ago you started working on this idea
that we need a new grand plan in neuroscience. What
(04:55):
led you to that conclusion?
Speaker 2 (04:57):
I was hearing concerns from the heads of funding agencies
and elsewhere that while researchers had been discovering a lot
of things about the brain, those discoveries hadn't been moving
the needle in helping individuals with certain classes of disorders.
Speaker 1 (05:17):
So you know, one of the textbooks in our field
is Principles of Neuroscience. That it just keeps getting fatter
over the years, absolutely, and it always has struck us
that if it really were principles, it should be getting thinner.
But what we just keep doing is a dated dump
of all the information we're getting. But your point is
we're not seeing, Ah, here's the clear pathway to solving
(05:40):
certain problems exactly.
Speaker 3 (05:41):
For certain conditions.
Speaker 2 (05:43):
So for some conditions we have been moving the needle
quite effectively. And so those include things like new drugs
from ingrained headache or insomnia, epilepsy and pain. But there
are other classes of conditions that we've been more frustrated with.
Speaker 3 (05:58):
And so yeah, that's the big question.
Speaker 1 (06:00):
So one of the arguments you make in your new
book is that many of the pharmaceutical treatments that we have,
for example, were discovered by accidents, So things like pain
or ADHD or in some cases depression. So tell us
about that. What's the story there?
Speaker 2 (06:15):
Yes, absolutely, so those stories are wonderful, the serendipitous discoveries
that happened long ago before we knew much about the
brain at all. One example is the first antidepressant, which
was discovered during clinical trials for the lung infecting bacteria tuberculosis.
So their clinical trials for the drug for TB and
what they found was the patients were joyous. There's even
(06:38):
a picture of light in Life magazine of them dancing around.
They were so happy. So they realized this chemical probably
has a different purpose. They put it through clinical trials
and it became our first antidepressant.
Speaker 1 (06:48):
And what was the name of that drug.
Speaker 3 (06:50):
Ipronia is it?
Speaker 1 (06:51):
And so that was totally an accident.
Speaker 3 (06:53):
It was totally an accident.
Speaker 1 (06:54):
And interestingly, you know the history of medical science is
shot through with these sorts of accidents, really is Yeah,
tell us about pain medications.
Speaker 2 (07:03):
Pain medications? So are opioid drugs? Those come from ancient
Mesopotamia where the Mesopotamians were harvesting opium from the poppy plants.
And our drugs today, like oxycodone, are just a slow
release form of that drug that we harvested from opium
in the early nineteen hundreds.
Speaker 1 (07:22):
How do they end up ingesting that?
Speaker 3 (07:24):
I don't know. That's a great question.
Speaker 1 (07:25):
That's a great question.
Speaker 3 (07:26):
How did they figure it out?
Speaker 1 (07:27):
Yeah? Yeah, yeah, okay. So and adhd M.
Speaker 3 (07:32):
That's another great one. Riddlin.
Speaker 2 (07:34):
So, Ridlin was developed in the nineteen forties by a
chemist who was Swiss, and he was using a technique
that we call try it and see what happens. We
don't do that much anymore. But so he synthesized the drug.
He liked it.
Speaker 3 (07:48):
He gave some to his wife. She liked it too, because.
Speaker 2 (07:51):
It improved her tennis game, and so he named it
after her. Her name was Rita, and that's why we
call it Rita Lynn. So another great story of as
a drug that happened long before we understood much about
the brain at all and certainly wasn't based on some
big discovery about the brain that led to a new breakthrough.
So there are a lot of discoveries like these.
Speaker 3 (08:09):
Yeah.
Speaker 1 (08:09):
Great. So your argument is that several of the drugs
that we have were totally accidental. And when it comes
to things that involve science as we typically do it,
where we say hey, look here's the gene, here's the
chemical involved, and so on, it's an enormous undertaking. So
give us a sense of let's say, for insomnia.
Speaker 2 (08:32):
Yes, yes, you're right, when a new discovery leads to
a new drug, those discovery stories are absolutely epic. So
one example of that. A drug for insomnia is subarexcent,
so superreccent. The way it works is it blocks chemicals
in our brain that actually keep us awake. And so
(08:53):
the discovery of superreccent dates back to nineteen ninety eight
when brain researchers discovered these chemicals in our brain the
first time. They were then linked later to insomnia via
studying some dogs that had genetically inherited narcolepsy. So these
dogs fall asleep spontaneously during.
Speaker 1 (09:11):
The day, and this was the chemical erectionin.
Speaker 3 (09:13):
These chemical ereccin exactly.
Speaker 2 (09:15):
And yeah, so they figured out this was a problem
in the erecxin pathway in the brain. It was then
linked to human narcilepsy. And once researchers discovered that there
are these chemicals in our brain that exists to keep
us awake, the assumption was that at least some of
us have insomnia because these chemicals are too active. So
the pharmaceutical industry went wild trying to find chemicals to
(09:36):
block the effectiveness of these keep you awake, the erecxins
in the brain. And so Mark then went through to
try to find such a chemical. They screened two million
different chemicals to find the right one, and once they
found a chemical it was effective, they improved it even
further to increase its efficacy reduce its side effects. So
(09:57):
Whurexcin then went through clinical trials and merged in twenty
fourteen as a new drug. So altogether there was a
sixteen year process from the big discovery about the brain
the erecsans to this new drug to block their activity.
Speaker 1 (10:12):
And what kind of money is involved in that?
Speaker 3 (10:13):
It was about a billion dollars.
Speaker 2 (10:15):
Yeah, and that's about as quick as has ever happened
from a big discovery to a new therapy.
Speaker 3 (10:21):
Yeah. So it's absolutely epic.
Speaker 1 (10:22):
Got it. So many discoveries are accidental. Ones that aren't
accidental are epic in terms of the amount of time
and money they take. So where does that put us
in modern neuroscience research. Let's jump to nineteen ninety eight
when Eric Candell wrote a paper suggesting, look, here's the
framework by which we should think about these things.
Speaker 3 (10:43):
Yeah.
Speaker 2 (10:43):
So in Eric Kendall's nineteen ninety eight paper, he was
really channeling the ethos of an era of brain research
that followed on excitement around two big new technologies, our
ability to sequence genes and image the human brain non
invasively with techniques such as functional nandecoresonance imaging.
Speaker 3 (11:02):
And Yeah, he laid out.
Speaker 2 (11:04):
A proposal of the new intellectual framework, as he called it. So,
in Kendell's framework, it all begins with genes. Our genes
are the code that is used to make our brain cells,
which are wired into these circuits, and it's the activation
of those circuits that give rise to all of mental
function and in term behavior, Kendell suggested that there's one
(11:24):
big feedback loop, so our behavior, our interactions with the
world feedback to shape how our brains are wired up.
Speaker 3 (11:30):
That's learning.
Speaker 2 (11:32):
And Kendell focused on this big arrow from how the
brain gives rise to the mind as the great challenge
for psychologists and biologists to delineate the relationship between those
two things.
Speaker 1 (11:43):
And the arrow is pointing from genes, two circuits exsolately, Yes,
experience and behavior.
Speaker 2 (11:48):
Okay, so yeah, to summarize this idea about the brain
and the type of thing it is, it's really set
up as a big chain of causes that lead to effects.
And the notion then is that when the brain becomes dysfunctional,
when you have some type of disorder, it's a broken.
Speaker 3 (12:02):
Link in the chain.
Speaker 2 (12:03):
It might be a mutated gene that leads to a
disorder that you might want to target with a drug,
or it might be a part of the brain has
aberrant activity which you could then target with stimulation.
Speaker 3 (12:13):
So this era of brain research I.
Speaker 2 (12:15):
Like to call find the broken link in the chain
so we can go in and target it for a fix.
And that example that we just talked about super excent
it was very much of that type of find the
broken link in the chain target it for a fixed
type of approach that led to that big discovery.
Speaker 1 (12:44):
Right, So sometimes that works, and that probably felt like
real progress. I'm sure when Eric Candell no Bel laureate
wrote this paper in ninety eight, he felt like, Hey,
we're really simplifying this and getting this straight how one
thing leads to another. But when you take a look
at what's going on in the field, you think that's
somehow not sufficient.
Speaker 2 (13:05):
Absolutely so, there are certain classes of disorders that have
really proven to be somewhat impenetrable using that type of
find the broken link in a chain approach. What's then example,
So they include our psychiatric conditions like depression and anxiety
and schizophrenia. So those are all cases in which we
do have therapies, but they don't work for everyone. And
(13:26):
many of those therapies date to pre date our understanding
of the brain, so they were discovered serendipitously. Also our
neurodegenerative conditions like Alzheimer's and Parkinson's and als, where we
do have some treatments in some cases, for example Parkinson's,
but we don't have ways to slow down the degeneration
that's happening in the brain that's leading to the decline.
Speaker 1 (13:47):
In other words, when we look at all these disorders,
we think, wow, this is really somehow more complicated. And
why because when we look for, let's say, a gene
for schizophrenia, what do we find.
Speaker 2 (13:58):
Absolutely so, in the case of schizophrenia, it's very rare
to have a single gene variation or mutation that leads
to the disorder. More likely, well, now that we've sequenced
lots of genes, we know that it's variation in hundreds
of genes that are tied to the condition. So if
(14:19):
one identical twin has schizophrenia, the chances of the other
identical twin having schizophrenia they're fifty percent. It's not one
hundred percent, it's fifty percent. So there is a big
genetic component to all of this, but there are also
environmental effects and other issues at.
Speaker 1 (14:35):
Play, and these intertwine in ways that are super complex.
As a side note, you know, the first gene pulled
for a major disease was for hunting tins and it
was a gene and if you have that gene, you're
going to die of hunting tins unless you dive something
else first, Yes, and everyone thought this is great, We're
going to figure out the gene that goes with every disease,
and it turned out to be much more complicated.
Speaker 2 (14:56):
Yeah, And even now, thirty years later, we still don't
have an effect of treatment for Huntingtons, although fingers crossed,
it looks like maybe there might be one on the
way in clinical trials, but it's taken over thirty years
to get there, even when we knew exactly what the problem.
Speaker 1 (15:10):
Was, right, Okay, So but for something like schizphrenia or
major depressive disorder, we're looking at something that's much more complicated.
We can't even find a single gene for it. As
you said, we find hundreds of genes. So where does
that put us?
Speaker 2 (15:24):
Yeah, so I think researchers are definitely waking up to
the idea that this idea about the brain as a big,
long chain is probably oversimplified. The human brain is often
held up as the most complex thing in the entire universe,
and this chain of causes the lead to effects, it's
not very complicated. So we might ask ourselves, well, what's
(15:45):
so complicated about the brain and what are we missing
in this chain?
Speaker 3 (15:50):
Like idea.
Speaker 1 (15:52):
And by the way, it still might be causes leading
to effects, right, but it's the feedback loops at every
stage exactly.
Speaker 2 (15:58):
That's the complexity that we're beginning to embrace. So causes
that lead to effects that feed back on themselves as causes.
The brain is not like a chain in this type
of idea. It's a whole different type of thing.
Speaker 1 (16:10):
Right. So your analogy that you make in the book,
which is wonderful, is like a weather system, right, So
unpack that for us.
Speaker 2 (16:19):
Yes, so you can think about these complex systems that
have many interdependent parts, and the weather is a terrific
example of that, and you can think about when a
complex system goes awry, it's a lot like a weather
breaking out into a storm.
Speaker 1 (16:35):
Yeah.
Speaker 2 (16:36):
We know a lot about systems like these because we've
studied them quite extensively. And one thing we know about
them is they're very, very hard to perturb in ways
that you would want to shift them out of their storms,
which in the case of the brain, would be shifting
the brain from a less healthy to a more healthy state.
Speaker 1 (16:54):
And one second tangent in your book, you tell us
a wonderful story about Johnny von Neuman and weather I
had no idea tell us then.
Speaker 3 (17:01):
Yeah.
Speaker 2 (17:02):
So in the nineteen forties, the end goal of weather
research really was to control the weather. Researchers wanted to
not just dissipate hurricanes, which is a worthy goal in
and of itself, but they also even wanted to weaponize
the weathers.
Speaker 1 (17:15):
This was the US government that, This was.
Speaker 3 (17:17):
The US government.
Speaker 2 (17:18):
Yeah, so they were very interested in funding weather research
with that end goal. Part of von Neumann's development of
the first computers was explicitly in the end goal, first
predict the weather, then learn how to control it.
Speaker 3 (17:34):
And so researchers tried that out and it didn't work out.
Speaker 1 (17:38):
So well, and it still hasn't worked out.
Speaker 3 (17:40):
It still hasn't worked out.
Speaker 1 (17:41):
And why it's because the weather's so complicated.
Speaker 2 (17:43):
It's so complicated, and it is a system because you
have these big feedback loops, right, any type of intervention
you try to do will reverberate in unexpected ways, and
so that's what makes these systems really really difficult to control.
Speaker 1 (17:56):
So when we look at something like major depressive disorder,
the temptation to say, look, if we could just find
the gene. There is no theugen, but if we could
just do this pharmaceutical here there, we can solve this,
and that has proved to be ineffective precisely because of
the complexity of the system here.
Speaker 3 (18:13):
Yes, absolutely, and so.
Speaker 1 (18:15):
One example that you talked about in the book was
emotions research and the one hundred years' war that's happened there.
So explain to us what that is.
Speaker 2 (18:24):
Yes, So, our ability to understand what's happening in many
of the psychiatric conditions comes down to wanting to be
able to measure an emotion, say, in the brain, and
that's proven to be very difficult to try to do.
Speaker 1 (18:37):
So.
Speaker 2 (18:38):
Researchers for over one hundred years have been arguing about
what types of things are emotions in the brain and
how are they organized. It might be that different emotions
like fear and disgust and happiness, they might be organized
in kind of their own little compartments in the brain,
kind of like our sensory systems where we have one
part of our brain for vision and another part for hearing,
(18:59):
Or might be that they're much more intermingled in the
brain such that and a lot like color vision. Right,
So color vision, we have one visual system and there's
a continuous space in our brain upon which we put
labels like cyan and red and magenta. We don't really
know how emotions are organized in our brain, whether they're
(19:19):
more like these compartments or more continuously, but understanding that
is one of the keys to trying to measure an
emotion in the brain. You have to figure out where
is it that you want to look and how is
it going to be reflected there?
Speaker 1 (19:31):
Yeah, and so that's led to this one hundred years
war because there are people on both sides of this argument.
It's either separate or it's spectral.
Speaker 3 (19:38):
Absolutely, yeah.
Speaker 1 (19:39):
And so we're looking at things like emotions and we
all want an explanation for it. But the question is
what will it take for us to be able to
answer something like that.
Speaker 2 (19:47):
It will take an appreciation that the way that emotions
manifest in the brain is not going to be simple
and straightforward. It's not going to be an individual neuron
that's activated when we feel aggression or sadness.
Speaker 3 (20:01):
It's going to take.
Speaker 2 (20:02):
Embracing these ideas that in the brain, if brain area
a sense information to b be send information back to
A again, and so we expect emotions to be reflected
in ways that kind of reverberate and dynamically evolve in
the brain as opposed to snapshots.
Speaker 3 (20:16):
That you could take a picture of.
Speaker 1 (20:17):
That's a really simple way, and in fact, it might
not even be that we can talk about in let's
cut two one hundred years from now, that we can
talk about area A and area B, right, because in
a sense, the whole brain is spectral in the sense
of you've got eighty six billion neurons that are all
doing their things, but they don't have border walls between them.
(20:38):
So what we do as neuroscientists as we say, oh,
this area seems to be involved in blah, but boy,
these things are spread out.
Speaker 3 (20:46):
How do you think about that?
Speaker 2 (20:47):
When you think about the brain in terms of how
compartmentalized is it? How much is everything everywhere all at once?
What's your take on that? You've thought a lot about it?
Speaker 1 (20:56):
I know, I mean, you know, so starting with the
experiments of Carl Lashly whatever last century, as you all know,
Lashley was trying to figure out where is a memory stored?
So he trained little mice to run a maze and
then he would cut parts of the brain to see, Okay,
where is that memory stored? So if I take all
(21:17):
these rats and I cut different parts. Where can I
find the memory store? And what he found is that
none of the experiments yielded anything because the memory is
somehow stored in a distributed manner. It's more like cloud
computing rather than here's my hard drive and you've just
broken the hard drive. And so that was one of
the first examples of Wow, we're looking at a big
(21:37):
complex system here where stuff is really distributed in ways
that's hard for us as humans to say, oh, yeah,
you're just restoring zeros and ones there. It's a very
different sort of thing. Every attempt we've made to compartmentalize
the brain doesn't seem to hold that well over time.
We still do find temptations say, look, this is the
(22:00):
visual cortex and this is auditory and so on, and
that's mostly true. But even embedded in here, you've got many,
many neurons that are reaching across long distances to talk
to other areas. And you know, when we look at
baby's brains, we find, you know, there are neurons and
the auditory cortex that are that are activating the visual
(22:22):
cortex when they're sound, and in the visual cortext they
are activating the auditory cortex when their site and as
we grow, those things start talking less to each other,
but they're still there. And if you go, let's say blind,
at some point those neurons sitting in an auditory cortex
will start We'll start taking over that territory right away,
because those cross connections are all sitting there. I love
(22:45):
the fact that you're pursuing this because it is a
system that we have always been tempted to simplify and say, Okay, look,
it's probably going to be this. And by the way,
this is the wonderful thing about science is saying hey, hey,
there's going to be a way to really simplify this,
and that's where we get progress. And yet we've attempted
(23:06):
to oversimplify here.
Speaker 2 (23:07):
Absolutely, Yeah, I completely agree with that. Yeah, but I
think we're also ready for the first time in history
to take on the complexity like we've never been able
to do this before. So it is an exciting era
for brain research to build on this oversimplification.
Speaker 1 (23:21):
That's right. And so you've been looking at other systems
and other scientific voices from the last fifty years that
have suggested things. So what do you see as possible
ways forward there.
Speaker 3 (23:34):
Yes, so.
Speaker 2 (23:36):
There's been a long thread through brain research. It's been
more of an undercurrent than the most dominant idea that
the way we should be thinking about the brain is
something much more akin to the weather, a dynamical system
where we're interested in how it evolves in time in
terms of things like it's patterns of activity and how
(23:59):
it is structured, not just as a computer, but something
that's continuously adapting to change. And these ideas date back
to Norbert Reener in cybernetics in the nineteen forties and
there's been an undercurrent of them throughout history and brain research,
including John Hopfield's Nobel Prize on he won in twenty
(24:21):
twenty four for physics for these ideas based on work
that he did in the nineteen eighties.
Speaker 1 (24:25):
And tell us about cybernetics.
Speaker 2 (24:27):
Cybernetics was this idea that the brain exists to control
the body and interact with the environment and a big
feedback loop.
Speaker 3 (24:38):
That was the gist of cybernetics.
Speaker 1 (24:40):
Yeah, and so that was Norbert Veener and other people
have built on that idea of having dynamic systems, lots
of feedback loops and so where do you see that
moving forward. So if we think today, okay, look, let's
think of the brain as a very complicated system with
lots of feedback. How do you tackle something like that.
Speaker 2 (24:58):
Well, there are a couple of different things are really important.
One is because these types of systems are so integrated,
you have to measure all their parts at the same time.
You can't measure their parts one at a time, And
for the first time in history, we're able to do that.
Twenty years ago, when I was recording from brain cells
and looking at their activity, I was able to look
(25:19):
at one at a time. Today we can record from
one million brain cells simultaneously in a mouse.
Speaker 3 (25:27):
It's remarkable.
Speaker 2 (25:28):
That's exactly the type of data that you need in
order to understand how all these brain cells are interacting
with one another. We also have to build these really
complicated models to make.
Speaker 3 (25:39):
Sense of these dynamical systems.
Speaker 2 (25:40):
Again, causes lead to effects that feedback on themselves as causes.
These are not things you can think through and try
to reason through. You need computers in order to do this,
and for the first time in history, we have artificial
intelligence of a type that can actually help us sift
through and make sense of this data. And build computer
(26:01):
programs that rival something as complicated as the types of
things that we can do. So it's a really exciting
era those two technologies, biotechnology and artificial intelligence coming together
in order to enable us to really embrace this type
of complexity.
Speaker 1 (26:30):
Give us a sense of, for example, David Anderson's lab
at Caltech, how he looks at this giant data and
figures out, hey, here's a way to capture what's going on.
Speaker 2 (26:39):
Yeah, that's a great example, and it's so relevant to
a problem that we've really been struggling with, and that
is how do we measure an emotion in the brain.
So in David's lab, he is really interested in the
evolutionarily ancient emotions like aggression, fighting, or feeding, and he
looks into a part of the brain that we know
(27:01):
is involved, the hypothalamus. And we know it's involved because
if you naturally, if you put two male mice together.
Speaker 3 (27:08):
They'll fight. They're aggressive.
Speaker 2 (27:10):
If you stimulate the hypothalamus of a mouse, even if
they're all alone, it will cause that type of aggression.
And if a mouse has damage to their hypothalamus, they
won't be aggressive anymore. So we know the hypothalamus is
definitely involved in mouse aggression. But if you look at
the activity of the brain cells in that part of
the hypothalamus, it really just doesn't make any sense because
(27:32):
not very many of them are active when the mice
are aggressive, and even the brain cells that are activated
during aggression they do all sorts of other things as well,
So you really can't look in the hypothalamus and understand
why is it that this part of the brain is
so important for aggression.
Speaker 1 (27:50):
In other words, it's not like the cells turn on
and then turn off.
Speaker 2 (27:53):
Okay, yep, yeah, It's just not an obvious answer. And
so these researchers in this group they started to shift
to this new way of thinking about the brain, not
as a big chain, but again as one of these
systems with these big feedback loops. So they shifted to
this new way of thinking about activity and the hypothalamus
that is a lot like a landscape of hills and valleys,
(28:16):
where at any one point in time, the activity of
the hypothalamus is somewhere on that landscape, and where it
falls where it ends up, determines how aggressive the mouse
will be.
Speaker 1 (28:28):
So you're measuring all the cells and you're representing it
as a point on the landscape.
Speaker 3 (28:33):
Yes, that's right.
Speaker 2 (28:34):
So at any one point in time, the activity and
the hypothalamus will be somewhere on this landscape, and where
it ends up falling in the valley along this long
line determines how aggressive the mouse will be. At one
end of the valley, that will translate into a mouse
that's not going to be aggressive, perhaps because what they've
(28:54):
seen is maybe a female mouse or not a mouse
at all.
Speaker 3 (28:57):
On the other end.
Speaker 2 (28:59):
Of the valley, that's where the population ends up sitting,
that will cause the mouse to be aggressive. And they
could see that this was true, not just by doing
observational work where they observe what's happening in the hypothalamus,
but they actually could use this new generation of tools
where they could causally perturb the system and confirm that
that indeed was causing the mice to be aggressive.
Speaker 1 (29:21):
Amazing. So instead of looking at a particular cell or
a group of cells and trying to think through it,
you have to take all the cells and collapse that
high dimensional activity onto a point on a landscape, and
then you can start describing what that landscape is doing.
Speaker 2 (29:36):
Absolutely, and the big shift here is that that landscape
can't be shaped by a big chain of causes.
Speaker 3 (29:44):
It lead to effects.
Speaker 2 (29:45):
The formation of the landscape depends on thinking about the
brain as having these big feedback loops in it.
Speaker 1 (29:51):
Yeah, you know, it's funny. Even in any neuroscience textbook,
you know you have sell A talks to sell B.
And of course, so we know that every cell in
the cortex is talking to you about ten thousand of
its neighbors, and lots of these are very complicated feedback loops,
and of course you have excitatory and inhibitory neurons, and
so straight away, I think any clever student looks at
(30:13):
this and says, wait a minute, something is something is
crazy here to think about? Oh a does s? And
yet our textbooks still read that way because we don't
know how to teach in a way where we're saying, look,
start from square one, we're going to talk about dynamical systems.
So how would you think about revising the way we
(30:33):
teach neuroscience.
Speaker 2 (30:35):
That's a really important question. Back in the nineteen forties
and fifties, we used to have an ecology food chains,
and then at some point they became food webs because
we realized that these ecological systems. There are these complex
dynamical systems with these big feedback loops in them, and
(30:55):
so we started to teach starting from elementary school, we
started to teach ecology differently, and so, yeah, I very
much think that that's what we need to start doing
in brain research as well, is starting from the beginning
teaching about the brain as a system chuck full of
these feedback loops and what are all of the consequences
of that.
Speaker 1 (31:13):
Yeah, And even if dynamical systems science as we understand
it now turns out not to be the full picture,
at least we're getting closer.
Speaker 3 (31:21):
Absolutely.
Speaker 1 (31:22):
Yeah. And Nicole, despite the limitations and where neuroscience research
has gone, you're very optimistic.
Speaker 3 (31:28):
Tell us why absolutely.
Speaker 2 (31:31):
When I started to write this book, I actually wasn't
sure where it would lead, and I started from a
place of kind of confusion and even a little bit
of pessimism because I could see that there were these
certain conditions for which we were get a little bit stuck.
On the other side of writing the book, I'm unequivocally
(31:51):
optimistic about the future of our field for the conditions
like the psychiatric conditions and their degenerative conditions.
Speaker 1 (31:58):
And why it's.
Speaker 2 (31:59):
Because I see that the changes that need to happen
are already happening in our fields. Right we were oversimplifying
the brain. We were treating it like this chain of
causes that lead to effects, and it was just a
massive oversimplification of the most complex thing in the entire
known universe.
Speaker 3 (32:15):
But now researchers are starting to embrace.
Speaker 2 (32:17):
This important type of complexity that we can again for
the first time in history, because we have new biotechnology,
we have artificial intelligence. For the first time, we're really
to study the brain in this way, and I am
very excited about the idea that that will be the
key to unlocking progress for all of the millions, billions
(32:37):
actually of individuals who are suffering from these conditions.
Speaker 1 (32:45):
That was my interview with Nicole Rust. This conversation circled
around the idea that the brain may not be the
kind of object we once hoped it was. For a
long time, neuroscience advanced under a parsimonious assumption that if
we can you could just identify the right pieces, the
right links in the chain, the story would come into focus.
(33:07):
Genes lead to proteins. Proteins built cells, cells form circuits,
Circuits generate thoughts and motions and behavior fix the broken link,
and the system heals. Sometimes that strategy works, but there
are entire domains where it doesn't, where no single gene
or molecule or brain region carries the explanatory weight that
(33:29):
we wanted to. Gradually, it's become clear to us that
most disorders don't behave like oh, there's a broken part,
but instead you have altered states of a whole system.
That means you can't just swap out apart. You have
to figure out if it's possible to nudge a complex
landscape that realization slash. That admission changes a lot, because
(33:54):
it reveals that the brain is more like a dynamic
environment shaped by feedback loops and continual self adjustment. It's
a system that can settle into values of activity that
are hard to escape. And by the way, it's a
system whose behavior depends not just on what's out there
in front of it now, but often on many things
(34:17):
that have interacted with it throughout its lifetime. So this
reframing has consequences for how we do experiments, for one,
but also it explains why some breakthroughs arrive accidentally while
others require decades of effort. It sheds light on why
prediction is hard, why control is even harder, and why
(34:40):
treating brain disorders sometimes resembles influencing the weather in Nicole's analogy,
more than it resembles fixing an engine. But although this
might seem like a pessimistic story, it is in fact
an optimistic one because for the first time, we might
actually have the tools to take this complexity seriously. We
(35:01):
can measure vast populations of neurons that once, we can
model systems that evolve in time. We can leverage artificial
intelligence to help us see patterns that are invisible to
our intuition alone. In other words, neuroscience may finally be
growing into the kind of science the brain requires. Every
(35:24):
mature field eventually has to let go of its simplest metaphors.
Physics moved beyond clockwork, universes, ecology moved from food chains
to food webs, and now neuroscience may be moving beyond
linear causality towards something richer and stranger and closer to
(35:45):
the truth. The challenge ahead is about learning how to
think in dynamic landscapes instead of static links, and if
we get that right, the payoff is going to be
new ways of helping the millions of people whose lives
are shaped by brains that have settled into difficult states,
and that's where the next era of neuroscience is going
(36:08):
to really begin. Go to eagleman dot com slash podcast
for more information and to find further reading. Join the
weekly discussions on my substack and check out Subscribe to
Inner Cosmos on YouTube for videos of each episode and
(36:29):
to leave comments until next time. I'm David Eagleman and
this is Inner Cosmos.
Host
David Eagleman
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
Kingdom of Fraud
It’s the unlikeliest of criminal partnerships: a devout polygamist from an insular Utah sect joining forces with a shadowy Armenian tycoon from LA. The result - a billion dollar fraud conspiracy. In Kingdom of Fraud, investigative reporter Michele McPhee traces the origins of the extraordinary alliance between Jacob Kingston and Levon Termendzhyan. Together, the two men trigger the largest tax investigation in American history and weave around themselves a web of dirty cops, influential political relationships and transnational money laundering. All this is set against the backdrop of Jacob Kingston’s clan – The Order. A powerful and secretive polygamist organization in Salt Lake City. To whom Jacob is desperate to prove his worth. Kingdom of Fraud is produced by Novel for iHeart Podcasts. For more from Novel, visit https://novel.audio/. You can listen to new episodes of Kingdom of Fraud completely ad-free and 1 week early with an iHeart True Crime+ subscription, available exclusively on Apple Podcasts. Open your Apple Podcasts app, search for “iHeart True Crime+, and subscribe today!