>>: Good afternoon. My name is Alex. And today I have the pleasure to
introduce Sebastian Seung who is here to join us as part of the Microsoft
Research Visitor Speaker series.
Sebastian is Professor of Computational Neuroscience in the Department of
Brain and Cognitive Sciences and Department of Physics at MIT, as well as an
investigator at the Howard Hughes Medical Institute.
He's been Packard Fellow, Sloan Fellow, McKnight Scholar and PopTech
Science Fellow among many honors. And the last time I was so excited to
introduce someone was when I first heard about someone trying to map the
human genome. But that's been done already. So now move on to the next
thing.
And Sebastian is trying to work on Mapping the Human Connectome. And if you
don't know what that is, I hope that you will by the that Sebastian talk is done.
So please welcome me in welcoming in professor Sebastian Seung.
[applause].
>>: And I hope that you'll be interested in this of course.
>> Sebastian Seung: Thank you very much, Alex. And thank you all for coming.
As you saw, I've written a book. It's been published. There's something
paradoxical about writing a book, which is that you start out with the idea oh, I'm
going to share my ideas with the world. And then you spend the next few years
of your life in solitary confinement writing this book and suffering.
But now that's all past. So I'm just having a really good time sharing with you.
So thanks a lot for coming.
This is also special. It's my first trip to Microsoft. And of course Microsoft has
been a legendary force in the history of computing. And in particular, in the
personal computer revolution. And I think that computers have come to have a
kind of mythic significance for us, right, that -- in fact, people come to my -- come
to MIT, I guess, freshmen will come and say, you know, my dream is to build a
robot that thinks or a computer that is more intelligent than I am, right?
And, in fact, I just talked with Alex and his colleagues about artificial intelligence
research at Microsoft. And so this activity is still alive and well. It's been going
on since around -- I guess 1950, the whole field of AI. And as any kind of mythic
quest goes, sometimes people think about it in an apocalyptic way, they think
that maybe in the future computers will become so powerful that there will be no
place for us left in the world. So this is kind of larger than life significance to the
whole quest to find or to construct artificial intelligence.
So what I'm going to tell you about today can also be thought of as another
quest, a parallel quest, one that is turned upside down. And that is the quest to
deconstruct our own brains, to reverse engineer them, to figure out how they
work. And the connectome really is a radical vision of how -- it's not going to be
realized in a really short time, just like AI won't be realized in a short time. It's a
multi-generational quest to understand ourself, to know ourselves by
deconstructing it in our own brains.
Now, the connectome, I will first define what a connectome is, I guess. I will
show you the only animal for which an entire connectome has been mapped.
This is a worm, a one millimeter-long worm that lives in the soil. It's called C.
Elegans, it's one of the favorite organisms that's studied by biologists today.
I think it's kind of cute. I don't know about you. [laughter].
>>: Not so much.
>> Sebastian Seung: Not so much? All right. Well, here is its connectome. So I
don't know about you. But this always reminds me of the back paging of in-flight
magazines. [laughter]. Maybe I'm spending too much time on planes these
days. But imagine that every city in the airline route map is replaced by a
neuron. In this case the neurons all have words actually. And every word
between two cities is replaced by a connection between two neurons.
So this little worm has 300 neurons, just 300 neurons and 7,000 connections.
And it's already pretty implicated looking, right? Now, let's imagine your
connectome. Your connectome has the equivalent of 100 billion cities and
10,000 flights coming and going from each city. So this kind of gives you the
idea of what a connectome is. But it really doesn't measure up at all to the scale
what your connectome is like. It's an extremely complicated map.
You might ask me is this map universal for all worms? Well, all worms, all
normal worms have the same neurons. The neurons have the same names.
They occur in the same locations. They have the same shapes. And there are
some small differences in the connections. So there's a little bit of variability from
worm to worm in the connectome.
In the case of you, your connectome, your connectome and mine, the
connectomes of all of us are much more different. There's a lot more variability
than a worm connectome. And maybe that's why we like to think of ourselves as
more unique. Each of us is more unique than maybe a particular worm is. And
there's much more variation in your connectome -- in your connectomes than
between your genomes. You may know that the difference this is genomes
between people are really, really miniscule, right. Only a few letters out of the
entire -- a small percentage of letters out of the entire sequence.
Yes. So that's a connectome. And why should we -- why should we actually
study connectomes? So that's -- that is something that I try to address in the
book. So there's a lot of how to in the book. How are connectomes found, how
to scientists go about studying them? But more important than that, we have to
explain why to we have to study connectomes?
So there's an older tradition of studying the brain, which all of you are no doubt
familiar with, which is to take the brain and divide it into regions. You've heard
about left brain and right brain. And maybe you've heard about frontal lobe,
parietal lobe, temporal lobe, those names, right? And there's somewhat finer
subdivisions. And neuroscientists have ascribed various functions to these
regions. And you can interpret the results of systems of brain injury in terms of
these kinds of maps and so on. So we've learned a lot about the brain in this
way.
But the regional approach to understanding the brain is never going to answer
the questions that we real care about, in my opinion. So what are those
questions? So one is what makes a brain region work well? Let's say in the
case of a genius? Or badly, in the case of someone with a mental disorder?
What happens to a brain region when we learn something? There's a kind of
primitive answer, right? People have tried to look for differences in size. The
19th Century neuroscientist thought that studying was like working out, right,
maybe your brain gets bigger when you study. And surprisingly there is some
evidence that on average parts of brains can enlarge when you're studying. But
you can only see these differences by averages over large populations of people.
And the same thing holds with certain mental disorders. So let's talk about
disorders like -- well, first let's talk about disorders like Alzheimer's. With
Alzheimer's disease or Parkinson's disease, we know that neurons degenerate
and die. If you look at the brains of these people at autopsy, after they're dead,
there's clearly something wrong on the brain. There's junk accumulating the
neurons. There are dying neurons, so on and so forth.
But if you look at the brains of people who had autism or schizophrenia, in
general their neurons look healthy.
People have looked hard to fine brain abnormalities that are clear and consistent
in the case of these mental disorders and again they resort to thinks like crude
measures like size. On average autistic kids, two year old autistic kids, have
larger brains than those of typical kids.
But if you took every kid with a big head and said that kids autistic, you would be
woefully inaccurate. And that's because studying brain regions or the size of the
brain or its regions is just too crude.
So in order to answer the questions that we really care about, why do brains
work well, why to they work badly sometimes and what happens when we learn,
we have to study the brain at a level that's smaller than regions. We have to
subdivide regions and study their parts, which are neurons.
And in order to figure out how regions work, it's important to figure out how those
parts are organized. And that's why we're trying to find connectomes.
You guys are all engineers, so you would be familiar with the idea that an
electronic device, in order to understand how it works, we might want to find the
wiring diagram. So that's another way of thinking about it.
Okay. So why are connectomes important? Well, more specifically, the second
part of the book makes two claims. One of them is that connectomes are not
static but they change -- they change from -- during -- they change as the result
of experiences.
So when you have an experience and you now store a memory of that
experience, there's some indirect evidence that your connectome gets
rearranged. The connections change in some way. And that's how you store a
memory of an experience.
The second reason that connectomes are important is to understand the
relationship between genes and the mind. Genetic factors are also important.
So we all know, right, that some -- you know, usually people are kind of like their
parents. And, in fact, one of the important stages of life is when you you come to
the realization I can't believe I'm behaving like my parents. [laughter]. It
happens, right?
So is that the result of your upbringing? Is that the result of your genes? You get
both from your parents, right? And people have had holy wars about this. They
get really mad at each other. They call each other names. Maybe even fist
fights, right? But we have to move beyond that. Because I think everyone now
realizes, at least all scientists agree that of course both genes and experiences
are important. They both shape the mind. To move beyond that, we have to
understand exactly how do they shape the mind.
How do experiences change the brain? Well, maybe they change the
connectome. How do genes shape the brain? There's a number of ways in
which they do so. But one important way is by guiding the process by which the
connectome forms during development.
So as the brain grows, many steps of to happen. A hundred billion neurons have
to be born. Then they migrate to their proper positions inside the brain in a
complicated dance. When they get there, they send out branches, these long
branches. You've seen pictures of neurons before. They have these incredibly
beautiful branchs. The branches intertwine with each other. And at the points
where they contact, they can make little junctions calls synapses. Synapses are
where two neurons talk to each other.
So that's how a connectome gets formed when you're -- when you're growing up.
And genes are important for all of those processes. So that's an important way
that genes influence the brain.
So I would argue that the connectome is where nature meets nurture. That's the
meeting point. And if we want to understand our own personal identities, we
have to understand connectomes. If we want to understand how to change
ourselves, we need to understand connectomes.
So you can argue that -- that every important transformation in your life is about
changing your brain, right? Going to school and learning is something. Falling in
love. Being a more compassionate spouse. Healing from a brain injury. All
these things are changes in your brain. And I'm arguing in the book that they're
fundamentally about changing this map.
So that's the motivation for looking for connectomes. How do we go about
finding them? Well, I should first say that this map took over a dozen years to
find. It was a heroic effort in the 1970s and '80s. So given that that took a dozen
years, then how could we even hope to find connectomes of human brains or -let alone mouse -- let's say mouse brains. Mouse brains would be smaller, right?
How could we hope to find connectomes of brains that are more like our own, not
as primitive as that of this worm?
So the hope is that our technologies have advanced far beyond what we had in
the '70s and '80s. A lot of it is advances in computers. And, in fact, the hope is
that computers are what are going to enable us to study the brain.
So I made this joke earlier as I was talking with Alex and his colleagues, which is
that AI researchers have a long look to the brain for inspiration to make
computers smarter. Right? If we copy the brain, maybe we can make computers
smarter.
But we've reached a new stage where computers have become very smart.
They've improved a lot in the last decades. How can we use computers to help
us figure out how the brain works? That's what my lab is all about right now. So
my joke is ask not what the brain can do for the computer, ask what the computer
can do for the brain. [laughter].
So I'm going to show you the process by which this is done and the technologies
that are being developed in order to speed up this process. One important step
is imaging the brain. So you take a piece of brain, and you put it in the high-tech
deli slicer, fitted with the world's sharpest knife, a diamond knife. And you cut it
into slices that are a thousand times thinner than a hair. And you array all the
slices on a silicon platter and you put them in a scanning electron microscope.
And I'll show you images that were taken in the laboratory of Jeff Lichtman at
Harvard University, who actually has -- who collaborated with researchers at
Microsoft and got some funding from Microsoft in the early stages.
So we're going to zoom in on one slice of a brain by a factor of 100,000 times.
And you see round circles right now. Those are cell bodies. They're the round -round boring part of the neuron that has the DNA and the nucleus. And then
we've zoomed into the spaces between the cell bodies. Those are the entangled
branchs of neurons and synapses. Although you may not recognize that yet.
Now, if we take many images of slices and stack them up together we get a
three-dimensional image. And still you may not really see anything that looks like
a neuron. So what we'll do is color in the cross-sections of one branch of a
neuron through the slices. Slice after slice. And if we do that we get a
three-dimensional reconstruction of what this will little segment of a branch looks
like. That's -- the red is a branch called the dendrite, and the green is a branch
called an axon. And the axon -- the green touches the red at two locations.
Those are the synapses.
And we're zooming in on one synapse. And if you look at the interior of the green
neuron you'll see little circles. So those circles are vesicles. There's little bags
containing molecules called neurotransmitters. You've probably heard of
neurotransmitters before, right, serotonin, dopamine, glutamate, these things.
They were abstractions when you heard about them. Now you can actually see
where neurotransmitters are kept inside neurons.
So this green neuron has sequestered the neurotransmitter molecules. And
when it wants to send a message to the red neuron it expels the contents of one
of those vesicles into the cleft between the two neurons. Of this pen on the other
side, that red neuron has receptor molecules which sets the neurotransmitter
molecules and there by receive the message.
So that's an account of what's called synaptic transmission. That's how neurons
communicate. And you could read about that in text books. But to drive the
point home, let me ask you to reflect on what this means.
So every time that you are thinking or feeling even the most refined ethereal
thoughts, effectively your neurons are spitting on each other. [laughter]. Right?
That thinking is an untold number of microscopic secretions. In and, in fact, there
was a French philosopher who wrote a long time ago, centuries ago that as the
liver secretes bile, the brain secretes thoughts. [laughter]. And amazingly, he
turned out to be right.
Okay. So chemical signals transfer messages between neurons. Electrical
signals are traveling inside neurons along the branches. That's the two -- that's
kind of a basic means of communication inside the brain.
So you've seen how to find a synapse. Now, how do we find a connectome?
Well, if you take that entire cube, imagine coloring it in like a three-dimensional
coloring book? Which is quite a laborious task because there's lots of objects
besides those two I just showed you. I showed you those but, in fact, that whole
volume is packed by branches of neurons.
And the embarrassing thing is that this looks like a fancy computer-generated
animation, which it is, but it was created by a person, Daniel Berger, a post-doc
in my lab who painstakingly colored in, slice by slice, every branch in there. And
it took him about 250 hours of work.
And to give you an idea of the scale, that's really pretty small compared to an
entire neuron. That has only small pieces of branches. And that neuron is pretty
small compared to an entire brain. And that's just a mouse brain. [laughter].
So as you can see, we're very far from finding the connectome of an entire
human brain. Or even a mouse brain. We'd be happy with maybe a cubic
millimeter piece of mouse brain or human brain. That would be a lot of work. If
Daniel were to color that in, it would take him somewhere between 100,000 and
a million years, if he never took a nap. [laughter]. Right?
And this is a problem that's -- people are encountering throughout our society.
Our ability to generate data is far outstripping our ability to analyze it.
At Harvard, they'll be taking delivery from Zeiss, of a very fancy new microscope.
It has 61 beams that scan in parallel. And it will image a cubic millimeter of brain
every two weeks, generating one petabyte of data. That's going to arrive, I think,
in a year and a half.
There's no way Daniel can keep up. [laughter]. Right? So what can we do?
Well, there's two things. One is the obvious one of artificial intelligence. Let's
make computers that are smart enough to do what Daniel does. Computers that
can see.
My lab has worked on that. And other labs around -- there's a few other labs
around the world that have worked hard on that. We've made some progress.
We used the machine learning approach. That's a little bit new in this area. It's a
problem known as image segmentation. And so we use Daniel's coloring as the
training set. So we trained the computer to emulate Daniel on that cube. And
we've gotten good results. But still error rate is still high. It's not anywhere
nearly as accurate as we'd like it to be.
So that's a problem -- that's natural, right? We know that robots in real life don't
see as well as they do in the movies. That's just the way it is right now. AI is not
perfect.
So what we have then is we developed human computer interface that allows a
human user to interact with the AI and fix the errors. And that semi-automated
system can be up to a hundred times faster than Daniel coloring all by himself.
But that's still not enough, right? I told you 100,000 years -- 100,000 person
years to get a cubic millimeter. So we have to make the AI smarter.
And the other thing we can do is we can try to recruit more people. [laughter].
So here is this new website that we are -- we've launched. It's called eyewire.
Eyewire.org. A bunch of students at MIT actually helped us launch this. We
have a special January term, a four-week term that's between the semesters
when the wild and crazy classes are offered. And so I recruited a bunch of
undergrads to help us launch this website.
So you can go there, and you see this invitation. Welcome to eyewire, where
you can help make discoveries about the neural structure of the retina. So the
retina is the sheet of neural tissue at the back of the eye. And we want to kind
the connectome of the retina. We want people to help us discover it. Right? So
the great thing about this site is you can come and you can click on these various
tabs. You can learn about neuroscience. And then you can proceed to the next
tab and actually do some neuroscience. You can contribute to what's known
about the retina.
So it's a game. And the game is something like what you used to do as a kid
with a coloring book. I hope this will appear. It is actually a little bit slow, I guess.
>>: [inaudible].
>> Sebastian Seung: There it is. Okay. So on the right-hand side what you see
is one slice through a little cube. Right? So we take the entire image of -- that
we have, which is a very large image. We divide it up into little cubes. Those
cubes are roughly of the same size that you just saw. And we can display one
slice at a time on the web browser.
On the left-hand side here you see a 3D rendering. Here's the cube. And the
yellow right here indicates which slice of the cube we're looking at on the right.
And the AI has colored in one branch of a neuron starting at that point. Its 3D
shape is shown right here. It's rendered like this. It may still be loading this
image. Let's see.
All right. I'm going to have to keep on talking because I think this image is
loading very slowly, unfortunately. I guess it's there.
Oh, there's one thing. I apologize that what I'm showing is in Chrome. And we
don't support Internet Explorer. [laughter]. And there's a reason, which is we
don't have web GL support. So I want to talk to somebody [inaudible] who I can
persuade to Web GL so that we can make this run in IE.
Yeah, unfortunately the demo is not working too well here because it's taking too
much time to download those images.
Yeah. So this works well on a -- right now, a 10 megabit per second connection.
But you can see the -- you can see what the AI has colored in and try to move
down. And find a place where the computer has failed to color. So you could
see right here the AI has failed to continue coloring. So right here the AI should
have kept on coloring. And we could try to correct that by simply clicking. Okay?
So we just tell the AI keep on going. And it colors in there. And it should be
coloring in the adjacent slices too. All right. I'll come back to that. I'll run it after
the cube is cached.
All right. So the basic idea, then, is that -- and I should say this is a tutorial task.
So here an expert has already done this task. So the right answer is known.
And the new user does this and gets feedback. If the user clicks in the wrong
location and colors empty wrong object, then that will show up in red. Well,
maybe not.
All right. Well, I'll let it -- I'll let this cube cache. So you get -- the person in the
training gets negative feedback if they color in the wrong thing. And they also get
a progress bar right here that tells them how far they've gotten.
But eventually once they get good enough at this, they will get cubes that no
expert has done before. And then they can actually help to reconstruct, trace the
wires of the retina and help us discover how the neurons are connected.
Okay. So we've gotten a lot of response, a lot of enthusiastic response from this,
especially from educators, too. So high school students are going to be able to
learn about neuroscience and do neuroscience at the same time. So we think
this will be a very powerful model.
We have also again exploring partnerships with patient advocacy groups
because this could be a powerful way of mobilizing people to study brain
disorders. And which brain disorders could we hope to do? Well, I told that you
in the cases of autism and schizophrenia, clear and consistent neuropathologies
have not been found.
One really intriguing hypothesis is that the brains are miswired, that the neurons
are all healthy, but they're connected in ways that are abnormal. And no one has
been able -- no one -- we've never had the technology to see such miswirings
before. But we are planning to image brains and compare regular brains with
autistic or schizophrenic brains and see whether we can find differences in the
wiring between those brains. So those are called -- there's a new jargon which
we say is connectopathy. A connectopathy is a pathological pattern of
connectivity that's associated with a mental disorder.
So that's going to be very powerful model. And I think that finding a
connectopathy would be a huge advance in our understanding of mental
disorders. Because if you can't see something wrong, I think it's much more
difficult to find a treatment or a cure for it.
In the case of infectious diseases, imagine not having a microscope and not
being able to see bacteria. That's a problem, right? If you don't see the bacterial
cause of an infectious disease, all you see are the symptoms. But once you see
what's wrong, that doesn't cure it by itself, but that helps you find a cure because
you know what you're trying to get rid of.
And in the same way, I believe that finding a physical basis for mental disorders
in the brain is extremely important.
Let's see. Is it working? A little better. All right. Well, too bad about that demo.
Okay. So let me just -- I've shown you the technology that we're using to find
connectomes. It's including artificial intelligence and now using the mechanisms
of social computing to bring people together, to collectively work with artificial
intelligence.
And the beauty of that of course is that when we take human judgments we can
use that for machine learning to make the AI smarter, progressively smarter. So
we're trying to create a system that collects the human examples of these tasks.
And it's going to get more and more powerful over time.
Now, I've portrayed the study of connectomes as really the way we want to come
to grips with the notion of change, personal change and a really arrive as a
science of personal change that is a rigorous and real one, not like the ones you
find in self-help books. Self-help books, these days they all have a lot of
neuroscience talk, but the reality is that we don't know what happens in the brain
when we change.
The last part of my book discusses the most extreme dreams of change, which
are those that belong to the transhumanists. So you may have heard of their two
biggest dreams, which are cryonics and uploading. So cryonics is the practice of
freezing your brain after you die in the hope that some advanced future
civilization will resurrect you.
And uploading is even more extreme. It says let's get rid of our bodies all
together, let's just transfer our minds to computers. Let's simulate our computer
-- our brains in computers, and then we can live forever inside this electronic
heaven.
Now, these are science fiction dreams. And for the most part dialog about them
is either I believe this and I'm signing up for it or these people are loony.
[laughter]. And there's not really any kind of rational debate.
So is in this book I argue that if we use the notion of the connectome we can -we can, interestingly, subject some of these claims to scientific tests. How is
that? Well, if you believe that you are your connectome, that your memories are
encoded in your connectome, then the critical questions for cryonics is whether
their freezing procedure preserves the connectome. We know that brains start to
disintegrate, deteriorate as soon as you die.
Are the wires falling apart? Are the synapses falling apart? If that's the case
then our advanced future civilization might be able to resurrect your body but it
couldn't recover your memories. It wouldn't be able to resurrect your mind. And
so that's the critical question for cryonics. We have no idea. They say their
procedure is going to preserve brains but the really critical test is to see if it's
preserving the connectomes in those brains.
And I think that's science because we have the potential of falsifying the claims of
cryonics. Any time you can falsify -- a potential for falsifying something, you're
starting to enter the domain of science rather than philosophy. Philosophy you
can never prove anything wrong. And in science you can't -- it's hard to prove
anything right. But you can prove that things are wrong.
Now, what about uploading? Why is the connectome important for that? Well,
the only plausible proposal I've heard about uploading is based on the
connectome. We take you -- let's say you're my customer for uploading. You
give me your brain. We find your connectome. And we use that information to
simulate the network of neurons inside your brain.
And, in fact, you can imagine that if we're optimistic, if Moore's law continues and
computer technology improves at the same rate it's improved for the last 50
years, we can imagine that by the end of the century we could have computers
powerful enough to both find human connectomes and to simulate an entire
human brain's neural network.
So the crucial question is, are you really your connectome? Is that really all the
information that's needed. Or could there be other information in your brain that's
not in the connectome that is critical for your personal identity? And so I
discussed those issues. I discussed the possible pitfalls. What extra information
might be needed in order to create one of these simulations?
And so the problem is that if a lot of extra information is needed, then those
computer simulations become completely impractical. There's no way we would
ever do them, even after a hundred more years of computer progress.
Okay. So that's the summary of the book. I know that books -- you read books
partially because of ideas. But you also read them for fun. So I think I will just
give you a flavor of the writing. I'll read a little bit from the book.
I can't decide which chapter to read. Okay. I'll read the chapter that discusses
the imaging technology. It's called Seeing is Believing. Smelling whets the
appetite and listening saves relationships but seeing is believing. More than any
other sense we trust our eyes to tell us what is real. Is this just biological
accident, the result of the particular way in which our sense, organs and brains
happen to evolve?
If our dogs could share their thoughts by more than a bark or a wagon of a tail,
would they tell us that smelling is believing?
As a bat dines on an insect captured in the darkness of night by following the
echos of electronic chirps, does it pause to think that hearing is believing is this.
Or perhaps our preference for vision is more fundamental than biology, based
instead on the laws of physics. The straight lines of light rays bent in an orderly
fashion by a lens preserves spatial relationships between the parts of an object,
and images contain so much information that until the development of
computers, they could not easily be manipulated to create forgeries.
Whatever the reason, seeing has always been certainly to our beliefs. In the
lives of many Christian saints, visions of God, apocalyptic or serene also
triggered the conversion of pagans into believers. Unlike religion, science is
supposed to employ a method based on the formulation and empirical testing of
hypotheses. But science, too, can be propelled by visual revelations: The
sudden and simple sight of something amazing. Sometimes science is just
seeing.
So why don't I take a few questions now.
>>: Can you tell me again the name of the book?
>> Sebastian Seung: Oh, the name of the book is just Connectome.
Connectome.
Yes, question?
>>: You have the connectome of a C. Elegan. What have we learned from it?
>> Sebastian Seung: So, if you talk to C. Elegans researchers, in fact,
everything we know about the C El began's nervous system, the functioning of it,
really depends on this diagram.
On the other hand, you could argue that we haven't -- we've learned
disappointingly little about how it works, right? So another way of asking this is
what's missing? And so what I would say is missing is that there's almost no
measurements of normal signals inside C. Elegans. Because of a strain -because of technical reasons it's easier to measure neural signals inside the
brain of a mouse or a or even a human -- the signals of a single neuron than it is
to -- than it is to measure them in the worm.
So the worm was actually an unfortunate choice, it turned out to be. And so you
need both. You need both signals, the neural activity, and you need the
connections.
>>: So you have the connectome and you don't know why -- what genes does it
use for its mobility or sensing or eating or ->> Sebastian Seung: Oh, no. A lot of those things are known because you know
that -- those neurons are -- many of them are classified as sensory neurons and
we know which sense they, you know, touch or smell or whatever they
correspond to.
There's motor neurons so we know which muscles they actually control. And a
lot of reflexes, stimulus response behaviors have been worked out because you
can find the path way from the sensory neuron to the motor neuron.
But at the same time, there's a lot of things that haven't been completely
understood. And the big stumbling block has been the black of measurements of
activity, and you need both.
So there's -- there's three things you have to understand, three kinds of
quantities you have to understand in neuroscience. That's genes, connections,
and activity.
>>: In terms of [inaudible] extrapolation you might have the capacity to
understand a connectome at some point because you've got all that [inaudible]
power available. But you have to ask the questions and set the model up and so
on and so forth. So how is that going to work?
>> Sebastian Seung: Oh, well, so I'm just predicting -- so the model would be
built by finding a connectome, right? Taking an entire human brain and slicing it
up and imaging it at really high resolution and extracting the connectome. And
I'm extrapolating that using -- I'm assuming that that's primarily computationally
limited task.
>>: Right.
>> Sebastian Seung: Yeah. That's right. So both the simulation and the finding
of the connectome are going to ride Moore's law. Or if Moore's law falters will -those efforts will falter too.
>>: So comparing connectome versus the genome, right? So once you get the
genome out you still have to do a lot of work to find the genes from that
[inaudible] functions.
Now, given that your connectome is found already, what else do you want to do?
>> Sebastian Seung: Wait.
>>: Do you want to look at more functional ->> Sebastian Seung: So you're asking if I had a connectome, what would I do
with it?
>>: Yes.
>> Sebastian Seung: All right. [laughter]. So as long as we're engaged in
wishful thinking. [laughter]. So I say that there's -- I actually have four chapters
in the book for that. I have carving, code breaking, comparing, and changing.
So carving is kind of like -- you take the genome and you divide it up into genes,
right? So carve the connectome would be to divide up this graph into brain
regions and groups of neurons that are called cell types. So that's one thing that
we would do.
Code breaking is to try to read memories from connectomes. If memories are
really encoded in these patterns of connections we have to figure out how to
decode them.
And comparing. That's the obvious one, right? Healthy brain, brain with mental
disorder. We have to compare those two.
And then the final one to change -- to change -- to have better technologies for
changing brains.
>>: In addition to [inaudible] results for chemistry [inaudible] maybe too much
depends on them, even if you know all connectome, it's possible that [inaudible].
>> Sebastian Seung: Yeah. So remember I said genes ->>: What was the question?
>> Sebastian Seung: So the question -- this really goes back to genes -- this
triad of genes, connection, and activity, right? So genes are the shorthand for
the detailed identity of molecules that are found inside neurons. And so in order
to understand -- in order to model or understand how a network functions, I might
need to know exactly what kind of molecules are in each synapse. Exactly what
kind of molecules are distributed throughout the dendrite of the neuron, so on
and so forth.
So you're adding to that, or are you -- okay. Let me just -- let me just finish that.
So you're right. But hopefully here we can use the intermediate step of cell
types. So neurons, you could think about them as trees in the forest. They fall
into different species. And ideally, neurons of the same species, which
anatomists call cell type, they will have similar molecular properties. And there'll
be a finite number of them. A relatively small number of them.
So if we just characterize the molecules that are in each type of neuron, that may
be enough. If not, then one might have to have more advanced imaging
technologies that would really see not just a gray scale image like this, but really
figure out exactly what molecules are where. And if all that information is really
necessary, that's when the uploading people I think are really -- they have to give
up. Right? Then it becomes really complicated to simulate. Yeah?
>>: I was wondering if you see any evidence so far that -- as you're trying to
build these models of connectomes that are fractal like repeating patterns, or is
everything like not like everything else.
>> Sebastian Seung: Are there repeating patterns?
>>: Yes.
>> Sebastian Seung: Well, we haven't seen enough of the -- well, okay, so let's
just -- there's obviously repeating patterns in certain structures like the retina,
right? So the retina is a tissue and one part over here looks very similar to the
other part over here.
Now on the connectional level, you can see repeating patterns but they're too
small to be really that interesting. Let's say a group of three neurons that have a
certain pattern of connection between them. That kind of repeating pattern has
been observed. But ->>: Too micro, too local?
>> Sebastian Seung: Maybe it's too local to be really that interesting. So the
challenge here is to find larger patterns that repeat.
>>: If you have multiple neurotransmitters in the center, is the information past
like a byte where the amplitude of the neurotransmitters in that synapse is the
signal of each ->> Sebastian Seung: So you're asking about whether a synapse has some kind
of analog strength? Is that the question?
>>: Well, yeah. Whether the transfer across that, depending on the information
that it's transferring, has analog strength in the synapse.
>> Sebastian Seung: Yes. So a synapse when -- so when a neuron signals
another neuron through a chemical synapse.
>>: Right.
>> Sebastian Seung: The effect of that on the receiving neuron has some -- it's
an electrical effect in the end and that electrical effect has an amplitude. And
that's often called the strength of the synapse.
>>: But there's more than one neurotransmitter type. Do ->> Sebastian Seung: Usually ->>: Transfer different information? Or are they all transferring the same?
>> Sebastian Seung: Well, they're transferring different kinds of information. So
for example most excitatory synapses, the positive synapses in the cerebral
cortex are glutamate synapses. And the one that are inhibitory, synapses with
negative sign, are gaba. That's another molecule that's similar to glutamate.
So, yes, different neurotransmitters have different effects. But a single neuron
typically secretes only one neurotransmitter.
>>: That's what I [inaudible].
>> Sebastian Seung: Yeah. The one in the back.
>>: So online is there any type of [inaudible] connectome without slicing our
brain [inaudible] [laughter].
>> Sebastian Seung: So you may have heard of the NIH funded Human
Connectome Project, which uses MRI -- is using MRI to find the so-called human
connectome. But that's a very different scale than what I'm talking about right
here.
So they're trying to find the map of connections between brain regions. And they
can do that with non-invasive imaging in live brains. But that's really simple
compared to the neural connectome. We don't know of any technology that
could do that in a live brain.
Or we can -- okay. We can't maybe imagine some technologies that might do
that in a live brain but maybe it wouldn't make any sense because it would take
so long -- it would take longer than your lifetime to achieve it. So the
connectome would be changing during that whole time when you're imaging it.
But the basic answer is no. We don't know of any. On the other hand we don't
know of any law of physics that tells us it's impossible. So I can't rule it out that
someone will invent one of the future. But right now we take dead brains and we
slice them up. [laughter]. Too bad.
>>: Can you use -- for instance, can you use MRIs from live people to inform
how you would go looking for the connectome or really connectome issues; for
instance if you had abnormal lab results is it feasible to say [inaudible] on
purpose, get MRI stuff on the mouse ->> Sebastian Seung: MRI is useful fore telling us where to look.
>>: Yes.
>> Sebastian Seung: So MRI will tell us look at this location in the brain. And
with this high resolution method we can only do little pieces anyway, so we need
a lot of guidance about where to look. That's one way in which these two -- that's
typical of maps, right? You've got zoomed out views and zoomed in views. And
you use the zoomed out view to tell you where to look.
>>: Does your model include the strength of the synapses. And if so, can the
imaging determine that?
>> Sebastian Seung: So there's a lot of evidence that the strengths of the
synapses is well correlated with the size. But that relationship remains -- it leads
to more investigation. And so it may be -- and more than that, the -- it may be
that the strength of synapse, it doesn't even make sense to summarize it by
single number. Synapses are more complicated than that. They have more
complicated dynamics.
So we may need imaging methods that have sort of more molecular resolution
like this gentleman asked about before in order to really try to extract all the
functional properties of a synapse from its appearance. Remains to be seen.
So the strength of a synapse, just to fill this in. So a synapse has a connection
has a -- an efficacy of transmitting information, which is analog. And so some
people believe that memories are stored by changes in that strength. Other
people argue that memory storage also involves creation and elimination of
synapse, not just changes in the strength of the existing ones.
>>: Is the structure of connectome fully encoded in the genome?
>> Sebastian Seung: No, it's not.
>>: [inaudible].
>> Sebastian Seung: Because experience is also -- experience is also ->>: No, no. The experience [inaudible] talking about, but the original structure
doesn't go from genome, it goes also from something else, right?
>> Sebastian Seung: Well, you could think about the original -- the starting point
-- experience starts working on a structure that is created by genes and
randomness. Right? So there's really not just genes of experiences, there's also
genes and randomness.
But probably -- it's not like there's one stage in your life where genes are doing all
the work and then all of a sudden it switches over to experiences. I'm not sure
there's any way of separating those two into separate phases of life. Genes and
experiences are always working together.
Even in the womb there's still some kind of experience taking place. Yes?
>>: You mentioned that they're using machine learning to automate the coloring
process or nerve tracing. Can you comment on other algorithms that you may be
looking in their application area or is that the primary one that you're up to?
>> Sebastian Seung: You mean ->>: Are you or colleagues in your [inaudible] working on algorithms for
understanding the structures as [inaudible].
>> Sebastian Seung: Well, so you're asking about whether we're doing things
that are not learning based, or whether we're using supervised versus
unsupervised training?
>>: It's a more broad question. Besides the one case you gave, what would be
another example? Are they illustrated in the book?
>> Sebastian Seung: I say much about them in the book. You'll have to go to
our review articles. So the major thing that we've done in image segmentation
has been supervised learning algorithms that are based on metrics of -- true
metrics of segmentation performance.
So the classic I guess not even classic but the 10-year-old machine learning
approach for image segmentation focuses on deciding whether a pixel is either a
boundary or not a boundary. But the problem with that is that pixel error is a very
naive error. If a human tracer traces the boundaries of an object, there's a lot of
jitter in the placement of the human judgments that we really don't care about.
What we care about are differences that cause top logical consequences, like
merging two objects or breaking an object into two. And so we have a machine
learning framework in which that's what's penalized as opposed to small jitter in
boundaries. That's just one thing that's needed. But we -- the challenge right
now is really moving to larger and larger context. So the general problem with
computer vision always is that -- is that your judgment about what happens right
here can depend on things that are happening really far away. And so how do
you integrate this contextual information into local decisions.
We have one question here.
>>: Can you say something about the relationship of connectome to [inaudible]
try to understand how a brain works. Is it -- is this the only approach, or there
may be some other approach?
>> Sebastian Seung: Well, there were -- I said there was -- one way of thinking
about it is activity -- genes, connections and activity. Neuroscientists generally
study one of those three things. And another approach we can talk about is
observation versus manipulation. So the connectome is just an observation. We
don't actually -- we don't necessarily -- we're not manipulating connections.
We're just observing what they are. But there are efforts -- there's optogenetics,
which involves manipulating activity by light. And one can imagine a whole other
class of techniques that would involve manipulating connections, destroying a
class of connections to see what consequence that has for function. Yes?
>>: [inaudible] small volume of [inaudible].
>> Sebastian Seung: Well, what we would do is ideally what we would do is we
would measure activity in that cube, in the behaving animal. So then the activity
related to behavior. And then we take that cube out and then find its
connections. So that's sort after fourth, right? There's genes, connections,
activity and behavior. And we can apply them all to that one cube.
>>: Just one more question.
>>: So is -- I mean, is there any notion that there's some computational codes
such that there's something [inaudible] software in the brain when you get to a
higher level?
>> Sebastian Seung: I mean, a hardware/software distinction?
>>: Yeah.
>> Sebastian Seung: Well, at a high enough level, yes. So if you were asked to
simulate a compiler, then you would probably -- you would be working at sort
after software level, right? But there's a lot of reasons to believe that the brain is
not like a general purpose computer, one of them being that particular brain
regions are specialized for particular functions. Right? So in the universal
computer we have one CPU which does all functions. But, in fact, our brains
have different regions which are specialized as separate functions.
>>: Well, wouldn't that be equivalent to like a graphics chip and such that at
some point you have to get up to the level where these things can be encoded ->> Sebastian Seung: Yes. Yes. So at the level of the things that we don't do
very well, like abstract thought, those might be more like a software -- a software
thing. But, you know, most of the functioning of the brain probably isn't like that.
>>: There's lots of [inaudible].
>> Sebastian Seung: What's that?
>>: In the heterogenous computing world we call that fixed function.
>> Sebastian Seung: Fixed function.
>>: Inside a GPU you've got some programs and some [inaudible] heart beat is
probably not program, it's probably fixed [inaudible].
>> Sebastian Seung: All right. Let me -- I'll just read one last excerpt if you guys
don't mind. Do we have a little time?
So this is the beginning of the book. And I'll give you an image to go away with,
a mental image. No road, no trail can penetrate this forest. The long and
delicate branches of its trees lie everywhere choking space with their exuberant
growth.
No sunbeam can fly a path torturous enough to navigate the narrow spaces
between these entangled branches. All the trees of this dark forest grew from
100 billion seeds planted together. And all in one day every tree is destined to
die.
This forest is majestic but also comic and even tragic. It is all of these things.
Indeed, sometimes I think it is everything: Every novel and every symphony,
every cruel murder and every act of mercy, every love fair and every quarrel,
every joke and every sorrow. All these things come from the forest.
You may be surprised to hear that it fits in a container less than one foot in
diameter and that there are 7 billion on this earth. You happen to be the
caretaker of one, the forest that lives inside your skull. The trees of which I
speak are those special cells called neurons. The mission of neuroscience is to
explore their enchanted branches. To tame the jungle of the mind.
Thank you, all, for coming.
[applause]