Functional Plasticity

Let's start with functional plasticity and we cannot avoid something that is

very fundamental for every neural biologist.

Everybody's interested in plasticity and learning in the brain, is the Hebb

hypothesis. So Donald Hebb, a Canadian, a

psychologist wrote in 49 a very 1949 a very influential book and there it said

the following. That's very important to understand what

he said. He said about plasticity and learning in

the brain, it was his hypothesis. He didn't know whether it exists or not.

He said, when an axon of cell A is near enough to excite cell B or repeatedly and

consistently takes part in firing it. So, cell A consistently is involved with

activating cell B, not on his own, but together with others, consistently, again

and again he says, some growth, or metabolic changes, takes place in one or

both cells A and B, such that A's efficiency.

is one of the sense firing b is increased all we like to call it today or to

summarize the heavy part fire together wire together what does it mean Hebb says

the following in simple words that if cell a Is involved with firing cell B.

Again and again so I'm cell A, I'm active, cell B is active following me.

Not only me, but with other friends, but again I am active cell B, B is active.

Active means firing a spike, i'm firing spike, cell B firing spike again And

again, sufficiently enough time, some changes between the cells takes place,

and eventually cell A becomes more effective in activating cell B.

It's an hypothesis. We don't know that it's true or not, but

it was a very formative hypothesis. Very, very important for the hypothesis.

Something about the causality that I am a cell A succeed causally to cause you to

fire cell B again and again, then we are becoming good friends and we strengthen

the synapse among us. So in, in the last 20 years a lot of

experiments have been done. And indeed we found out that Hebb's rule

or Hebb's hypothesis, is indeed implemented in some both hippocampal,

hippocampus, and also cortical sets. Showing that synapses, which connect cell

a and cell b, are very, very, very plastic.

So let me show you what we succeeded to show, of course, when I say we, is the

community of neuroscientists. So again, just to remind you, we know

that the axon of cell a. Files and action potential is spike all

or none remember the Hodgkin Huxley model we understand the spike very well cell b

the post synaptic responds with an EPSP, Excitatory Post Synaptic Potential.

Let's say that this is before learning. That's the basic efficacy of this

synapse. This spike, hundred million fold spike,

generate this little EPSP, let's say two millivolts EPSP.

After learning somehow we'll discuss how. Somehow Hebb says that when cell a, fires

cell b sufficiently timed, something about the connection is changed, and cell

b and the connection becomes more effective.

So after learning Hebb cells, after learning This spike increases the capa,

the probability of cell B to fire, how? Something becomes stronger, the synapse

becomes stronger, for the same spike you get a bigger EPSP.

It's stronger synapse, more dipolarization due to the same spike So

this is actually, in terms of synapses, spikes, and EPSPs, what Hebb is saying.

If this is active enough time, then the weak synapse becomes a stronger synapse.

I want to show you what I just said schematically, in the following way.

What could be a mechanism for such a thing?

Why, why, what could be the mechanism for making the synapse stronger?

We know today that one mechanism, one possible mechanism is that the synapse

becomes more effective for a given spike. Making more potential post-synaptically

due to the insertion of new receptors in the post-synaptic membrane.

So you remember that there was a spike here.

There is a release of transmitter, and there is a receptor there binding the

transmitter, opening channels making it an EPSP here, okay.

So the spikes makes an EPSP. We know today that you too plasticity, of

the synapts, of this amazing plastic device.

The same synapts may have now post-synaptically more receptors.

And I'll show you what is the mechanism for it, more receptors post-synaptically

so again the same spike, the same pre-synaptic signal, generates

post-synaptically a larger EPSP. So I would call this a weaker synapse,

and I will call this a stronger synapse. And the mechanism for making the synapse

stronger is that for the same spike, the same transmitter release opens more ion

channels post-synaptically, thus generating a larger EPSP.

Okay, so this is a possible mechanism. I could give you another possible

mechanism. Presynaptically, presynaptically, the

same spike would release more transmitters.

Then for the same spike I would get transmitter release and this then would

make the synapse also more effective So this a post-synaptic mechanism to make

the synapses more effective. How do we study what really happens in

synapses, and this is the idea. The idea is that today, with new

techniques, we can record, both in the In Vivo case, in the whole brain, but

typically in slices. You can record from two cells in a brain

slice, for example, in the cortex. You implant an electrode here and here,

in these two cells. And you start to ask the question, what

makes this particular synapse, if they are connected synaptically between here

and here, what makes the synapse between these two cells more.

Or less effective. What could make a synapse more [UNKNOWN]?

So you really can stimulate the blue cell, you can record synaptic potential

in the red cells, and you can try to manipulate things until you find what

could be the mechanism for synaptic plasticity.

What are the conditions What are the conditions for the synapse to become

stronger, to have more receptors, to respond stronger to a spine?

What could be the conditions? Now, you can manipulate each sense

separately, and so you can control the condition and find out what are the

conditions. So this is what experimentalists do

today, they did about 12, 15 years ago and they found something very very

important, very very fundamental. A mechanism that is called Spike Timing

Dependent Synaptic Plasticity. Spike Timing Dependent Plasticity Or in

brief, STDP. They found that this particular

condition, that I will just show you in a second.

This particular condition makes the synapse, the connection, stronger.

So here is the condition. Here is your synapse, between the cell A

and the cell B. And you want to make the synapse

stronger. You want to generate a larger EPS speed.

And the post-synaptic side due to the spike in the pre-synaptic s, side.

And that's what you do, apparently. In order to make it stronger.

You activate first A pre-synaptic cell. You generate a spike there, you, with

your electrode. Spike here, and then with the other

electrode in the post-synaptic cell, you'll generate a spike there.

So there was a spike here and a spike there.

And again A spike here that you generated, and a spike there that you

generated. So pre-synaptic spike, post-synaptic

spike, pre-synaptic spike. Enough times, again and again, and

suddenly, after this repettion The synapse becomes stronger.

You get a larger LTP as I showed you before.

This is called Spike Timing Dependent Plasticity because it depends on the

timing between the presynaptic spike and the post synaptic spike, presynaptic

spike and the post synaptic spike. In this order, pre before, post you get

increase enhancement, what we called long-term potentiation.

The synapse is potentiated, becomes stronger, potentiated.

Long-term potentiation, LTP, for a long time.

Long, it could last minutes Hours, maybe days, maybe lifetime, but long.

So after this kind of pre-, post-, pre-, post-, enough time, you get an

enhancement of the synapse, letting me show you real experimental results.

So, in this case, or i should have mentioned here sorry i should have

mentioned here that if you do it in the reverse order of timing so you first

activate the post synaptic cell then the pre synaptic cell so post pre post pre

then the synapse becomes weaker so thats anti hebb because hebb said already in

one direction That when A is active enough to activate B then the synapse

becomes stronger but now we show you that the synapse may also become weaker, if

the reverse order in spike timing takes place.

So here is the experimental result, one of the experimental result, in the cortex

in this case. So here is the case where the

post-synaptic cell fires before the pre-synaptic cell.

The post-synaptic cell fires, the pre-synaptic, and this order makes the

synapse weak here. So this is our control strength, and you

can se that for this order of timing The synapse is less strong.

The EPSP is less than the normal. And in the reverse order of time, you see

here. This is pre before post.

Pre before post, again and again. The synapse becomes stronger.

stronger then control maybe twice then the control okay so this is the ltd long

term depression of the synapse the synapse becomes weaker and this is the

ltp long term potentiation the synapse becomes long adn more stronger stronger

Look at the time, at the time window for this mechanism.

It's a short time window, whereby if the pre-synaptic spike beg, starts before the

post-synaptic spike, you get LTP, long-term potentiation, about 40

milliseconds time window. If the difference in timing is longer, so

pre, and then you wait a minute, and then post, the synapse doesn't care.

It doesn't change. And also, the reverse.

If the postsynaptic cell fire before the presynaptic cell in this time window, the

synapse become weaker. So it's a very narrow time window for

these mechanisms to work. It doesn't explain many other things.

For example, the Pavlov case. Where the dog here's a bell, and then

sees the food. The difference between bell-food,

bell-food, bell-food, until the bell itself generated the saliva, could be

seconds. For this mechanism is really the second

mechanism, much, much briefer. And what we do and what we know about

learning for example this classical Pavlovian learning is much longer

timescale. This doesn't explain Pavlovian learning.

You still have to understand Long-term mechanism for plasticity.

How do I learn something from childhood and remember it when I'm adult.

This is, does not explain everything. But it is a very important mechanism for

rapid learning. Now the synapse becomes weaker or

stronger, on this time scale. It's a very important.

Time window. And today, because we have these

functions experimentally, we can write the equations that describe this LTP,

long-term potentiation, when A fires before B, or long-term depression When B,

the post-synaptic cell, fires before A, we can write down equation, mathematical

equation, that explains, that describes, this function.

We have mathematics, a model, for plasiticity of the synapse.

It's very important now because, now when I have a model, I can really, do theory.

Or even generate machines, that, follow this rule, the machine.

An maybe the machine would start to learn.

An we shall discuss the at the end, that today, what is called In computer

science. Machine learning, the whole aspect of

machine learning is inspired in many ways by the brain, because the brain is a

machine that learns, this is one mechanism that spike timing depending

plasticity. This causality relationship between pre

and both. Or vice versa, and since I can write an

equation, I can develop machines that follow this equation like my machine

follows this equation and then we can apparently learn.

Just to summarize this part about spike timing dependent plasticity, of course

one wants to really pin down and understand what is the mechanism.

In the synapse that is sensitive to the timing of the spike, pre and post.

Why when there is a spike in the presynaptic membrane and then there is a

spike in the follower cell, in the postsynaptic cell.

What happens really physically, what is the mechanism that enables this?

Very sophisticated machinery to become stronger or more efficient.

There is a lot of focus today on this very fundamental issue about the brain,

about synaptic plasticity. We know a lot about the synapses in the

pre-synaptic part. Not only the vesicles, but a lot of other

mechanisms are embedded here. And also in the post-synaptic part not

only the receptors plays a role, but all this recycling of receptors.

And all these other things that are really beyond this lecture.

But you should know, of course, because synapses are so important.

Probably the most amazing plastic mechanism in the world.

They learn, they change very fast. they have rules for learning and this

learning certainly underlines you capability now to learn something new so

we must understand what makes this fantastic synapse so dramatically get

them to change but also clinically we know of diseases like Alzheimers.

Whereby the synapses stop functioning correctly at early stages of the disease.

And so you start not to be able to learn and remember.

And so, we must understand the mechanisms at the level of synapse and the level of

spikes, and the level of post synaptic potential.

In order to understand these amazing plastic mechanisms of synapses.

So this was functional plasticity, and now I want to go to structural

plasticity.

Synapses, Neurons and Brains

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