synaptic hypothesis

8/12/2019 Synaptic Hypothesis

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Strong claim:Synaptic plasticity is the only game in town.

Weak Claim:

Synaptic plasticity is a game in town.


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The cortex has ~109

neurons.

Each Neuron has up to 104

synapses


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Central Hypothesis

Changes in synapses underlie the basis of

learning, memory and some aspects ofdevelopment.

What is the connection between these seemingly very

different phenomena?

Do we have experimental evidence for thishypothesis

A cellular correlate of Learning, memory-

receptive field plasticity


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Classical Conditioning Hebbs rule

When an axon in cell is near enough to excite cell Bandrepeatedly and persistently takes part in firing it, somegrowth process or metabolic change takes place in oneor both cells such that s efficacy in firing Bis increased

Ear

Tongue

Nose

B

D. O. Hebb (1949)


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Two examples of Machine learning based on

synaptic plasticity

1.The Perceptron (Rosenblatt 1962)

2. Associative memory

(We will talk about these next week)


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Synaptic plasticity evoked artificially

Examples of Long term potentiation (LTP)and long term depression (LTD).

LTP First demonstrated by Bliss and Lomo in

1973. Since then induced in many different ways,usually in slice.

LTD, robustly shown by Dudek and Bear in 1992,

in Hippocampal slice.


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Artificially induced synaptic plasticity.

Presynaptic rate-based induction

Bear et. al. 94


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Feldman, 2000

Depolarization based induction


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Spike timing dependent plasticity

Markram et. al. 1997


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But how do we know that synaptic

plasticity as observed on the cellular level

has any connection to learning and memory?

What types of criterions can we use to answer

this question?

At this level we know much about the cellular

and molecular basis of synaptic plasticity.


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Assessment criterions for the synaptic hypothesis:

(From Martin and Morris 2002)

1. DETECTABILITY: If an animal displays memory

of some previous experience (or has learnt a new

task), a change in synaptic efficacy should be

detectable somewhere in its nervous system.

2. MIMICRY: If it were possible to induce the

appropriate pattern of synaptic weight changes

artificially, the animal should display apparentmemory for some past experience which did not

in practice occur.


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3. ANTEROGRADE ALTERATION: Interventions

that prevent the induction of synaptic weight

changes during a learning experience should impair

the animals memory of that experience (or prevent

the learning).

4. RETROGRADE ALTERATION: Interventions that

alter the spatial distribution of synaptic weight

changes induced by a prior learning experience

(see detectability) should alter the animals memoryof that experience (or alter the learning).


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Detectability

Example from Rioult-Pedotti - 1998


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Example: Inhibitory avoidance

Fast

Depends on Hippocampus

Whitlock et. al. 2006


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Occlusion of LTP in

trained hemisphere

More LTD in trained

hemisphere

(Riolt-Pedoti 2000)


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Mimicry: Generate a false memory, teach a

skill by directly altering the synaptic

connections.

This is the ultimate test, and at this point in

time it is science fiction.


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ANTEROGRADE ALTERATION:

Interventions that prevent the induction of synaptic

weight changes during a learning experienceshould impair the animals memory of that

experience (or prevent the learning).

This is the most common approach. It relieson utilizing the known properties of synaptic

plasticity as induced artificially.


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Example: Spatial learning is impaired by block of

NMDA receptors (Morris, 1989)

Morris water mazerat

platform


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4. RETROGRADE ALTERATION: Interventions that

alter the spatial distribution of synaptic weight changes

induced by a prior learning experience should alter the

animals memory of that experience (or alter the

learning).

Lacuna memory control
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Receptive field plasticity is a cellular

correlate of learning.

What is a receptive field?

First describedsomatosensory receptive

fields (Mountcastle)

Best known examplevisual receptive fields


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Visual Pathway

Area

17LGN

Visual Cortex

Retina

light electrical signals

Monocular

RadiallySymmetric

Binocular

OrientationSelective

Receptive fields are:

Receptive fields are:


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Left Right

Tuning curves

0 180 36090 270

RightLeft


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Tuning curves and receptive fields

A feed forward model of

orientation selective cells

in visual cortex.

(Hubel and Wiesel model

of simple cell)


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Receptive field plasticity is a correlate of learning

An imaginary example

Learning to discriminate between similar lines

G li ti f th i f RF d S l ti it


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Generalization of the meaning of RF and Selectivity

First described in somatosensory cortex (Mountcastle)

Retinal cell RFs

Simple cell RF in primary Visual cortex (VC)

Complex cell in VC

Motion selective cells in area MT

Selective cells in Auditory areas

Is there another form of representation?

Receptive field plasticity can be induced by


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Receptive field plasticity can be induced by

changes in the visual environment

BinocularDeprivation

Normal

Adult

Eye-opening angleangle

Eye-opening

Adult


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Monocular

DeprivationNormal

Left Right

%o

fcells

group group

angleangle

1 2 3 4 5 6 7

10

20

1 2 3 4 5 6 7

30

15

Right

Left

Rittenhouse et. al.


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Receptive field Plasticity

Ocular Dominance

Plasticity (Mioche and Singer, 89)

Synaptic plasticity in Visual

Cortex (Kirkwood and Bear, 94 )

Left Eye Right EyeStimulate Record

30150-15

50

100

150

200

HFS

Time fromonsetofLFS (min)

4530150-15-3050

75

100

125

150

1 Hz


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Evidence that Ocular Dominance plasticity depends

on synaptic plasticity.

Bear et. al. 1990


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Similar experiment using Antisense for NR1 in Ferrets

Roberts et. al. 1998


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Blocking NMDAR

with Antisense

prevents the

development of

orientation selectivity

in Ferrets .

Ramoa et. al. 2001


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Heynen et. al. 2003


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Heynen et. al. 2003

LTD is the basis of Rat Ocular Dominance plasticity


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