The Cortical Column

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En provenance du cours de Hebrew University of Jerusalem

Synapses, Neurons and Brains

697 notes

Hebrew University of Jerusalem

Synapses, Neurons and Brains

697 notes

These are very unique times for brain research. The aperitif for the course will thus highlight the present “brain-excitements” worldwide. You will then become intimately acquainted with the operational principles of neuronal “life-ware” (synapses, neurons and the networks that they form) and consequently, on how neurons behave as computational microchips and how they plastically and constantly change - a process that underlies learning and memory. Recent heroic attempts to realistically simulate large cortical networks in the computer will be highlighted (e.g., “the Blue Brain Project”) and processes related to perception, cognition and emotions in the brain will be discussed. For dessert we will deliberate on the future of brain research, including the questions of “brain and art”, consciousness and free will. For more information see the course promo below and read “About the course.”

À partir de la leçon

Cortical Networks - Out of the Blue Project

This module is based on what you have learned in modules 3 to 6: how single cells function, how they are connected via (plastic) synapses to each other and how they might perform specific computations. Here we actually connect a network of neurons (using the “blue machine super computer”) so that we can simulate mathematically the activity of a large network (the “Blue Brain Project” BBP centered in EPFL, Lausanne, Switzerland). We know how to simulate a single neuron (the Hodgkin and Huxley model) and how to simulate synapses and dendritic cable (Wilfrid Rall model) so we can connect neuron models and build realistic networks in the computer. We started with simulating the mammalian neocortex, a relatively new structure in evolution (200 million years old). In each cubic mm of the neocortex (e.g., of rat), there are about 100,000 cells, 4 Km of wires (dendrites and axons) and about 100 million synapses. We can today integrate experimental data (anatomical and physiological) and mathematical methods, and come close to simulating the electrical and synaptic activity in several cubic mm of the neocortex. We will learn about the neocortex - including the neuron types it consists of - and how we go about simulating such a huge neural circuit. We will next discuss what could we learn from it and what are we aiming at next. We will end by describing the recently announced, EU Flagship Project – the “Human Brain Project” (HBP). The BBP served as a seed for the HBP, but the latter is much broader and even more ambitious, aiming at developing new approaches for treatments of brain diseases (so urgently needed) and advancing the future of neuroscience and of brain-inspired computing and robotics.

Mega Projects for the Brain12:14

The Blue Brain Project - The Start12:55

The Cortical Column12:53

A Cortical Column Networks11:57

Blue Brain Simulations9:44

From Mouse to Human5:49

The Human Brain Project12:03

Rencontrer les enseignants

Idan Segev
Professor
Computational Neuroscience

Okay. [COUGH] So let's go from here, from this

general introduction to speak more about a particular piece of, of the brain that

we would like to simulate in details, at the levels of cells and synapses.

The level of neurons and synapses, okay, the simulation of a, of a circuit.

So, we would like to, to choose a circuit, some circuit, and reconstruct it

in details, anatomically, also physiologically.

Put all these things together under a modeling framework.

And simulate it in both health and disease, this particular network.

So the question is what, what, what, what should we choose, what network should we

choose? You already saw this little cartoon,

which I'm going to dwell about more just now.

Showing that if you look at the mammalian brain, the mammalian neocortex just below

your skull, you see that this neocortex is of course, built from neurons, from,

from many neurons. A lot of neurons, but if you look at the

skull, so this is the skull side, and this is deep inside the skull, about two

millimeters deep. Depth into the skull, you see these

layers and layers of cells. We will discuss these cells in a second,

we already said something about these cells just to give you a ballpark.

If you look in the cortical, in a cortical circuit of human, of mouse, of a

cat, of a monkey, doesn't really matter, you see somewhat similar things.

Of course, there are differences between one species to another.

But, in general, just to give you ballpark number, so if you look at the

cubic millimeter on the order of cubic millimeter.

Yes, from the surface deep inside. About two, two millimeter deep, one

millimeter this direction, and one millimeter into the board.

So about two, cubic millimeter, one cubic millimeter, you get on the order.

Again, there are some variations between species, on the order of let's say,

30,000 cells. Okay, so this is a, a chunk, this is a

piece of a brain. In the particular region, the cortical

column as well call it. We'll talk later about why is it a

column. But we can talk about the column that has

this size, and it has about 30,000 cells and maybe about 100 million connections

within this. And about 100 million synapses, it's a

huge number. Huge number of cells, huge number of

synapses. So, it's a rather big piece of a brain.

It's small relative to the whole brain but within itself it's, these are big

numbers. Looking at it differently, and actually

people looked at this. Long time ago, early anatomist,

including, including Ramon Y. Cajal and others, but they didn't call it

cortical column. They look at the brain, they thought it

maybe built from some kind of building blocks, maybe not.

And then they started to draw, people like the Hungarians, Santiago Ty and

other people related to cortex, trying to reconstruct pieces and trying to

understand the circuitry. The elements that build up this

particular circuitry in the cortex. So, I can bring you several nice

pictures, early pictures. Another one that is now classical, just

to give you a notion. A very, very rough, without details yet.

So this is the surface of the cortex. This is deep into the cortex about two

millimeters deep here. And you can see that there are many

different cell types. We discuss cell types.

Some of the cells are pyramidal cells, we discussed them.

Some of the cells are spiny stellar cells.

Some are inter-neurons. Some are inhibitory.

Some are excitatory. Many, many cell types within, a circuit

like this. And when I say type, we mentioned that

you can, classify neurons both by their anatomy, also by their physiology, also

by their synaptic properties, also by the genetic properties, there are many ways

to classify neurons, into types. So in this case of course I'm showing you

an anatomical type, because I don't show you activity here.

But you can see that there are these trees, different trees and different

types composing a network. And you can see also when you start to

study this network that this network receives an input okay, coming from these

blue axons, coming from elsewhere into the network from the thalamus into the

network. Or from other modestal regions and this

input is going to different layers. To different regions.

From the thalamus it mostly goes to layer four.

From other regions it mostly go to layer one and so forth.

And so you, you can start to make some kind of an order, some general order that

it's not all to all and not everybody is talking to everybody.

And the input doesn't go everywhere and so on.

In general there is some order, not that we understand all this ordering, but

there is some order. And so people in the last, I would say

hundred years, are starting to more and more, dwelling, learning, studying, this

particular network, because the cortex is something of course very interesting for

us, as mammalians, as humans, because in the cortex which is relative new in

evolution, about 200 million years old neocortex, this neocortex enabled the

mammalian world to generate new behaviors, including me, creating new

things maybe, help, with the help of my cortex, generating language, language and

so forth. Who knows what comes from this cortex,

it's relatively new in evolution, and, so, so, naturally we are interested in

that. And, so, there is a lot of study about

this cortex, and some of this studies, whatever we know, we would like to put

together, in this simulation based blue brain research.

For example, if you deeper into this cortex, this neocortex, this neocortex

below your skull, you can see that there are layers.

I already mentioned layers, but I didn't say what I mean by layers.

So, you can define layers in many ways. We think about six layers typically,

again this is only typical. Some regions have less, some regions have

more layers but say layer mean that the cell bodies of certain cells, it's dense.

You will see soon, in a second, the layering of the cortex.

You can think about layers. So this is some kind of order.

You make an order by saying I have layer here with cell bodies.

And dendrites emerging out and down. After down there is another layer, layer

five with cell bodies sitting here. I can, I can try to make some order in

this jungle but also speak about different cell types as I mentioned

before. So, this will be the blue dendrite of

layer five pyramidal cell and the blue axon.

Or the bluish axon here, will build layer five parameter cell axon.

You may subdivide layer five into two types, layer five a, layer five b, and so

forth. Though I don't want now of course to go

into elaborating into each type, but I, I'm trying to say that we are trying to

make an order with this piece and we know quite a lot about it.

We don't know also quite a lot about this, but we know a lot about these, cell

types and so, and so on, and recently because of new techniques new

technologies, you can see for example here, a column, or a cortex, with this

surface going down, and you can see dense cell bodies in layer two and cell dense

bodies, if you have good imagination you can see layer here less dense here, and

so forth. You can speak about layers.

So this would be layer six cells. This would be layer five cells.

This would be layer four and so on. Again this is you may, you may say don't

see exactly the layers. And so forth but I'm, I'm just showing

you that we are trying to make some anatomical order which has some

foundation because the input is not going everywhere.

Cell types are different in some sense from one layer to another.

And so we, we are trying to make order you can see and this order hopefully will

help us understand the function. Hopefully but this is only anatomical

order, and we also want to make other type of orders.

We also have today, beautiful, elaborate, intensive, really intensive work.

In this case from Kevin Martin lab trying to in details look at a particular cell

type. For example this cell.

Is the spiny cell itself. From cat v one, from the primary visual

cortex of the cat, in layer, in layer four, in this case layer four spiny cell

itself. And you can see, an attempt, to map out

all the synapses. The inputs into this particular cell.

So, for example, all the yellow dots, this dot, this dot, this dot, all the

yellow dots signify eh, an input coming from the thalamus.

So this is another region. Earlier region, in the visual system,

going into this cell, and making synapse here, here, here, here, all these yellow

dots. You can sink, you can think about all the

green dots, so here are the green dots, here is a green dot, here is a green,

another green dot, very hard maybe for you to see another green dot, another

green dot, many, many green dots. These green dots are coming from lateral

input from similar sets. So of course this is not the other spiny

stellate cells in this region. There are many many spiny stellate in

layer four and this spiny stellate cell talk to each other via axon in synopsis

so you can actually mark. Statistically at least where the inputs

from other nearby similar spiny stellate cells in pinged on you and so on.

So this is now a map at the level of single cell.

Of who, what is the synaptic environment around you?

What is the source of input? Who is the source of input?

Near you, from nearby cells, but also maybe from faraway cells.

From the thalmus, or from other, other regions.

So this is a synaptic mapping, a synaptic map, onto this cell, and you know now,

after the lesson we took before, that eventually all these synapses Together.

In some sense, this orchestra of synapses, inhibitory and excitatory

synapses, eventually may generate yes or no, a spike in this cell.

We don't know just by looking at it. We know the substra, the substance.

Who is talking to you, but who is talking well, and when he talks, what does he say

exactly? This doesn't give you.

So, it just gives you the map of synopsis with out any activity.

But this is a beginning of trying to decipher the activity of this particular

cell resulting from the activity of it's neighbors.

So, this, this is the thing we can do today.

And, there are of course, many, many, many other types of information I could

have shown you, that we are now starting to collect.

So, what I'm saying that we're trying to collect, to put together, all the

information available, I mean, the location of synapses.

The type of cells. Also the firing properties of this

particular cell. And, and, also the, the, the strength of

synapses, the type of synapses, is it inhibitory, is it excitatory, so as I

said we know some of this information, not all of this information, and

especially we don't know all this information while the animal behaves.

So I cannot tell you now, that when this cat, I don't know, looks at the, at the

orientation, oriented but this, and this, and this synapse will be active.

This, this I cannot tell you now. I can tell you, due to some results that

I mentioned last time, by our Arthur Conners and colleagues, whereby they can

record the input. They can record the activity of this

synapse or this synapse, while the animals viewing some relevant input, like

an oriented bar /g. But of course this is a static, this is a

static picture but it's the beginning of, of, of organization of data hopefully in

some systematic way we shall continue to talk about this in a second.

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