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.”
Functional Plasticity
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From the course by Hebrew University of Jerusalem
Synapses, Neurons and Brains
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From the lesson
Neurons as Plastic/Dynamic Devices
This module discusses "Neurons as plastic/changing devices". Probably the most unique aspect of the nervous tissue is its amazing capability to constantly and adaptively change in response to a challenging environment; this capability enables us to learn and to store memories. We start by a short discussion about the notion of learning in the brain and then highlight various mechanisms that support learning and memory – introducing the term ‘neuronal plasticity”. In particular “functional plasticity”, whereby the efficacy of existing synapses is changed as well as “structural plasticity”, whereby learning/memory processes are associated with anatomical changes - the formation of new synaptic connections and with neurogenesis – the birth of new nerve cells (yes, also in the adult brain). A mathematical model that captures some aspects of functional plasticity will also be introduced together with several new exciting experimental finding related to “neuronal plasticity”.
Meet the Instructors
Idan SegevProfessor
Computational Neuroscience
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.
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