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Theoretical Neuroscience II: Learning, Perception and
Cognition
The synaptic Basis for Learning and Memory:
a Theoretical approach
Harel Shouval
Phone: 713-500-5708
Email: [email protected]
Course web page: http://nba.uth.tmc.edu/homepage/shouval/teaching.htm
Strong claim:
Synaptic plasticity is the only game in town.
Weak Claim:
Synaptic plasticity is a game in town.
The cortex has ~109
neurons.
Each Neuron has up to 104
synapses
Central Hypothesis
Changes 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, memoryreceptive field plasticity
Classical Conditioning
Hebb’s rule
Ear
A
Nose
B
Tongue
“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”
D. O. Hebb (1949)
Two examples of Machine learning based on
synaptic plasticity
1.The Perceptron (Rosenblatt 1962)
2. Associative memory
THE
PERCEPTRON:
(Classification)
1 x  0
Threshold unit: O   (  wi x  w0 ) where  ( x )  
i
0 x  0



where o is the output for input pattern x ,

Wi are the synaptic weights and y is the desired output
o
w1 w2

x1

x2
w3 w4

x3

w5

x4

x5
AND
x1
1
1
0
0
x2
1
0
1
0
y
1
0
0
0
o
1
0
1
-1.5
1

x1
1 x2  1 x1  1.5  0
1

x2
Linearly seprable
OR
x1
1
1
0
0
x2
1
0
1
0
y
1
1
1
0
o
1
x1  x2  0.5  0
0
1
-0.5
1

x1
Linearly separable
1

x2
Perceptron learning rule:


o

  (y o )


Wi   xi
w1 w2 w3 w4

x2
x

3
w5

x4
x

5
Associative memory:
Famous images
Names
Albert
Input
Marilyn
.
.
.
.
.
.
 x11
 1
 x2
 x31
 1
 x4
x12 x13 x14 
2
3
4
x 2 x2 x 2 
x32 x33 x34 
2
3
4
x4 x4 x4 
desired output
 y11
 1
 y2
 y31
 1
 y4
y12 y13 y14 

y22 y23 y24 
y32 y33 y34 

y42 y43 y44 
1. Feed forward matrix networks
2. Attractor networks
Harel
Associative memory:
Hetero associative
Auto associative
A
α
A
A
B
β
B
B



oi
o1
oN
Hetero associative

x1

x2

x3

x4

x5
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:
P
W i, j   x ik y kj
k1
P
Or in vector form:

W  x y
k
k 1
k
Simplest case –
orthonormal input vectors:
P
x  (x )  l,m
l
m T
P
O  x W   (x  x)y  m,n y  y
m
m
m
k1
k
k
k
m
k1
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 …
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
Depolarization based induction
Feldman, 2000
Spike timing dependent plasticity
Markram et. al. 1997
At this level we know much about the cellular
and molecular basis of synaptic plasticity.
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?
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)
platform
Morris water maze
rat
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
Visual Cortex
Receptive fields are:
•Binocular
•Orientation
Selective
Area
17
LGN
Receptive fields are:
•Monocular
•Radially
Symmetric
Retina
light
electrical signals
Right
Left
Left
Tuning curves
0
90
180
270
360
Right
Tuning curves and receptive fields
A feed forward model of
orientation selective cells
in visual cortex.
(Hubel and Wiesel model
of simple cell)
Receptive field plasticity is a correlate of learning
An imaginary example
Learning to discriminate between similar lines
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
Normal
Binocular
Deprivation
Adult
Adult
Eye-opening angle
angle
Eye-opening
Monocular
Deprivation
Normal
Left
Right
Right
% of cells
angle
Left
angle
20
30
15
10
1 2
3 4
5
group
6
7
Rittenhouse et. al.
1 2
3 4
5
group
6
7
Receptive field Plasticity
Ocular Dominance
Plasticity (Mioche and Singer, 89)
Left Eye
Right Eye
Synaptic plasticity in Visual
Cortex (Kirkwood and Bear, 94 )
R ecord
S tim ulate
150
125
100
75
1 Hz
50
-3 0
-15
0
15
30
45
Tim e from onset of LFS (m in)
200
150
100
HFS
50
-1 5
0
15
30
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
LTD is the basis of Rat Ocular Dominance plasticity
Heynen et. al. 2003
Summary