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

Weak Claim: Synaptic plasticity is a game in town.

Theoretical Neuroscience II: Learning, Perception and Cognition

The synaptic Basis for Learning and Memory: a Theoretical approach

Harel Shouval

Phone: 713-500-5708Email: harel.shouval@uth.tmc.edu

Course web page: http://nba.uth.tmc.edu/homepage/shouval/teaching.htm

The cortex has ~109 neurons.

Each Neuron has up to 104

synapses

Central HypothesisChanges in synapses underlie the basis of

learning, memory and some aspects of development.

• What is the connection between these seemingly very different phenomena?

• Do we have experimental evidence for this hypothesis

A cellular correlate of Learning, memory- receptive field plasticity

Classical Conditioning Hebb’s rule

“When an axon in cell A is near enough to excite cell B and repeatedly and persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficacy in firing B is increased”

Ear

Tongue

Nose

A

B

D. O. Hebb (1949)

Two examples of Machine learning based on synaptic plasticity

1.The Perceptron (Rosenblatt 1962)

2. Associative memory

THE PERCEPTRON:(Classification)

00

01)()( 0 x

xxwherewxwO

i i Threshold unit:

where is the output for input pattern , are the synaptic weights and is the desired output

ox

iWy

o

1x

2x

3x

4x

5x

w1 w2 w3 w4 w5

o

1x

2x

x1 x2 y

1 1 1

1 0 0

0 1 0

0 0 0

AND

-1.5

1 1

0 1

1

Linearly seprable

05.111 12 xx

o

1x

2x

x1 x2 y

1 1 1

1 0 1

0 1 1

0 0 0

OR

-0.5

1 1

0 1

1

05.021 xx

Linearly separable

Perceptron learning rule:o

2x

3x

4x

5x

w1 w2 w3 w4 w5

ii xW

oy

)(

Associative memory:Famous images Names

Albert

Marilyn

Harel

.

.

.

.

.

.

44

34

24

14

43

33

23

13

42

32

22

12

41

31

21

11

xxxx

xxxx

xxxx

xxxx

44

34

24

14

43

33

23

13

42

32

22

12

41

31

21

11

yyyy

yyyy

yyyy

yyyy

Input desired output

1. Feed forward matrix networks

2. Attractor networks

Associative memory:

Hetero associative Auto associative

A

B

α

β

A

B

A

Bio

1x

2x

3x

4x

5x

1o

No

Hetero associative

Associative memory:

Matrix memory: associate vectors xi with vectors yi, where the upper index denotes the pattern number.

A simple way of forming a weight matrix is:

Or in vector form:€

W i, j = x iky j

k

k=1

P

∑

P

k

kk

1

yxW

Simplest case – orthonormal input vectors:

This procedure works quite well for non orthogonal patterns as well.

It can be improved by using other ways to set the weights, for example …

€

x l ⋅(xm )T = δl ,m

€

Om = xm ⋅W = (xm ⋅x k )y k = δm,nyk

k=1

P

∑k=1

P

∑ = ym

Why did I show you these examples?

These are examples in which changes in synaptic weights are the basis for learning (Perceptron) and memory (Associative memory).

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.

Artificially induced synaptic plasticity.

Presynaptic rate-based induction

Bear et. al. 94

Feldman, 2000

Depolarization based induction

Spike timing dependent plasticity

Markram et. al. 1997

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.

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 ‘apparent’ memory for some past experience which did not in practice occur.

3. ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal’s 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 memory of that experience (or alter the learning).

Detectability

Example from Rioult-Pedotti - 1998

Example: Inhibitory avoidance• Fast

• Depends on Hippocampus

Whitlock et. al. 2006

Occlusion of LTP in trained hemisphere

More LTD in trained hemisphere

(Riolt-Pedoti 2000)

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.

ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal’s memory of that experience (or prevent the learning).

This is the most common approach. It relies on utilizing the known properties of synaptic plasticity as induced artificially.

Example: Spatial learning is impaired by block of NMDA receptors (Morris, 1989)

Morris water maze rat

platform

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 TM

Receptive field plasticity is a cellular correlate of learning.

What is a receptive field?

First described – somatosensory receptive fields (Mountcastle)

Best known example – visual receptive fields

Summary -

Visual Pathway

Area17

LGN

Visual Cortex

Retinalight electrical signals

•Monocular•Radially Symmetric

•Binocular•Orientation Selective

Receptive fields are:

Receptive fields are:

Re s

pon

se (

s pik

e s/s

e c)

Left Right

Tuning curves

0 180 36090 270

RightLeft

Tuning curves and receptive fields

A feed forward model oforientation selective cells in visual cortex.

(Hubel and Wiesel model of simple cell)

Receptive field plasticity is a correlate of learningAn imaginary example

Learning to discriminate between similar lines

Before learning

After learning

Generalization of the meaning of RF and Selectivity

• First described in somatosensory cortex (Mountcastle)

• Retinal cell RF’s

• 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 changes in the visual environment

Binocular Binocular DeprivationDeprivation

NormalNormal

Adult

Eye-opening angle angle

Res

pon

se (

spik

es/s

ec)

Res

pon

se (

spik

es/s

ec)

Eye-opening

Adult

Monocular Monocular DeprivationDeprivation

NormalNormal

Left Right

% o

f ce

lls

group group

angleangleRes

pon

se (

spik

es/s

ec)

1 2 3 4 5 6 7

10

20

1 2 3 4 5 6 7

30

15

RightLeft

Rittenhouse et. al.

Receptive field PlasticityOcular Dominance Plasticity (Mioche and Singer, 89)

Synaptic plasticity in Visual Cortex (Kirkwood and Bear, 94 )

% o

f b

asel

ine

Visual Cortex Receptive Field PlasticityMioche and Singer, 1989

Monocular deprivation

Left eye response Right eye response

Initial state:

After 17 hours MD of left eye:

Reverse suture

Initial state (after prior MD of left eye):

After one day of RS:

After 2 days of RS:

Left eye response Right eye response

Left Eye Right EyeStim ulate Record

3 01 50-1 55 0

1 0 0

1 5 0

2 0 0

Time (min)

LTP

HFS

Time from onset of LFS (min)4 53 01 50-1 5-3 0

5 0

7 5

1 0 0

1 2 5

1 5 0

1 H z

% o

f b

asel

ine LTD

Evidence that Ocular Dominance plasticity depends on synaptic plasticity.

Bear et. al. 1990

Similar experiment using Antisense for NR1 in Ferrets

Roberts et. al. 1998

Blocking NMDAR with Antisense prevents the development of orientation selectivity in Ferrets .

Ramoa et. al. 2001

Heynen et. al. 2003

Heynen et. al. 2003

LTD is the basis of Rat Ocular Dominance plasticity

Summary