Download Synaptic Plasticity

Spike Timing-Dependent
Plasticity
Presented by: Arash Ashari
Woodin MA, Ganguly K, and Poo MM. Coincident pre- and postsynaptic activity modifies
GABAergic synapses by postsynaptic changes in Cl- transporter activity, Neuron 39: 807–820,
2003.
Dan Y, Poo MM. Spike timing-dependent plasticity: from synapse to perception. Physiol Rev.
2006 Jul;86(3):1033-48. Review.
Rao, R. and Sejnowski, T. Spike-timing-dependent Hebbian plasticity as temporal difference
learning. Neural Comput, 13(10):2221–2237, 2001.
Slides mostly from: www.mbi.osu.edu
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Outline
• Neuron, Synapse, Depolarization and
Hyperpolarization
• Long-Term Potentiation and Depression
(LTP and LTD)
• Spike Timing-Dependent Plasticity (STDP)
• A Mathematical Model for STDP
• Discussion
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Dendrite: 1. Spatial Summation
2. Temporal Summation
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Neurotransmitters
• Amino Acids … Glutamate, GABA
• Biogenic Amines … Dopamine, Histamine
• Neuropeptides … LHRH, Proctolin
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Neurotransmitters
• Amino Acids … Glutamate, GABA
• Biogenic Amines … Dopamine, Histamine
• Neuropeptides … LHRH, Proctolin
• NMDA, GABAA are typical receptors
Glumatergic: Excitatory
GABAergic: Inhabitatory
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Excitatory (EPSP) or Inhibitory (IPSP)
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Depolarization: Influx of Na and Ca cations
This causes a spike (Action Potential – a pulse-like wave of voltage)
Hyperpolarization: Outflux of K cations or influx of Cl anion11
Long-Term Synaptic Enhancement
• Long-Term Potentiation (LTP) ~ Rapid and
sustained increase in synaptic efficacy
following a brief but potent stimulus
• Best studied in the hippocampus
• Induction of LTP occurs at the
postsynaptic site and requires the
conjunction of pre and post-synaptic
activity
• On the order of hours, days or longer
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Long-term Synaptic Enhancement
• The mechanisms underlying LTP remain
controversial
• The existence of Long Term Depression
(LTD)
• A possible way to study LTP might be
Spike Time Dependent Plasticity (STDP)
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Spike Time Dependent
Plasticity
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Synaptic Plasticity
 Hebb’s Postulate: When an axon
of cell A... excites cell B and
repeatedly or persistently takes
part in firing it, some growth
process or metabolic change
takes place in one or both cells so
that A's efficiency as one of the
cells firing B is increased.
 In standard Hebbian learning, a synaptic weight is increased if
presynaptic and postsynaptic neuron are `simultaneously' active. If
neurons communicate by spikes, the concept of simultaneity implies the
pre- and postsynaptic spikes occur within some time window. Theory
predicts that these time windows could have two phases corresponding to
an increase (potentitiation) or decrease (depresseion) of the synaptic
weight depending on the relative timing of pre- and postsynaptic spike.
Such asymmetric learning rules with two phases have been found in
recent experiments.
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Spike Time Dependent Plasticity
Weakening
Presynaptic Cell
Excitatory
Synapse
Strengthening
t
Postsynaptic Cell
t
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What do experiments show?
% Change in PostSynaptic Current
Presynaptic Cell
Excitatory
Synapse
Postsynaptic Cell
t = tpost - tpre
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Guo-qiang Bi and Mu-ming Poo, J. of Neuroscience, December 1998
So, what are the STDP rules?
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L. F. Abbott and S. B. Nelson, 2000 Nature Review
Similar results: Karmarkar
and Bunomano, 2002;
Abarbanel et. al. 2003;
Kitijima and Hara, 2000
(ms)
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0.1
I peak ( m M / ms)
.08
.06
LTP
t
t
B
N
.04
+/-
LTD
.02
0
-50
LTD
0
LTP
50
100
 t (ms)
150
LTD
200
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A Mathematical Model for STDP
Presynaptic Cell
V
'

Vrest  V  g ex  Eex  V 

tm
gex is
PLASTIC
Excitatory
Synapse
g ex   g ex / t ex
'
Postsynaptic Cell
V  Postsynaptic Membrane Potential
Vrest  Postsynaptic Membrane Resting Potential
gex  Excitatory Synaptic Conductance
Eex  Excitatory Synaptic Reversal Potential
tm  Membrane Potential decay time constant
tex  gex decay time constant
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Song, Miller, Abbott Model for STDP
 A exp  t / t   if t  0
F t    
 A exp t / t   if t  0
Strengthening
Weakening
t
t
t
t
F(%)
F(%)
A+
M '  M / t 
P'  P / t 
t = tpost - tpre
P(t)
t
t
M(t)
A-
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Updating P and M
When Presynaptic cell fires,
 Update P
When Postsynaptic cell fires,
 Update M
Presynaptic Cell
P  P  A
M  M  A
Excitatory
Synapse
F(%)
F(%)
Postsynaptic Cell
A+
P(t)
t
t
M(t)
A-
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Updating gex
Synaptic conductance gex is updated when there is a presynaptic
action potential at excitatory synapse
When Presynaptic cell fires
g a  g a  M  g max
When Postsynaptic cell fires
g a  g a  P  g max
gex  gex  g a
ga
Denotes the peak synaptic conductance (the synaptic conductance
immediately after an isolated presynaptic spike)
ADDITIVE RULE for Synaptic Modification
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Putting things into perspective

Vrest  V  g ex  Eex  V 
'
V 
g ' ex   g ex / t ex
tm
P'  P / t 
M '  M / t 
When Presynaptic cell fires,
When Postsynaptic cell fires,
1.
P  P  A
1.
M  M  A
2.
g a  g a  M  g max
2.
g a  g a  P  g max
3.
gex  gex  g a
Strengthening
Weakening
Presynaptic Cell
t
Postsynaptic Cell
t
Excitatory
Synapse
t
t
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In the real world
•Multiple Synapses
•1 postsynaptic neuron
•How about inhibitory synapses!!
Hence, the equation for the postsynaptic neuron changes to include
the inhibitory synapses
V 
'
Vrest
 V  g ex  Eex  V   g in  Ein  V 
tm
g ' in   g in / t in
gin  Inhibitory Synaptic Conductance
Ein  Inhibitory Synaptic Reversal Potential
tin  gin decay time constant
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1000 Excitatory
Synapses
200 Inhibitory
Synapses
1000 ‘P’ functions,
but only ONE ‘M’
function!!!
Postsynaptic Cell
Presynaptic Excitatory cell fires,
Postsynaptic cell fires,
1.
P[1... 1000]  P[ j ]  A
1.
2.
g [1... 1000]  g [ j ]  M  g max
2.
3.
gex  g ex  g [1... 1000]
M  M  A
g [1... 1000]  g [ j ]  P[ j ]  g max
Presynaptic Inhibitory cell fires,
1.
gin  gin  g in
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Discussion
 How LTP and LTD occur?
 Can STDP underlie Memory and
Learning? How?
 Correlated activity can occur purely
by chance, rather should be
learned than reflecting a causal
relationship that
 STDP as a Reinforcement
Learning/ Temporal Difference
Learning
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References
 Woodin MA, Ganguly K, and Poo MM. Coincident pre- and postsynaptic
activity modifies GABAergic synapses by postsynaptic changes in Cltransporter activity, Neuron 39: 807–820, 2003.
 Dan Y, Poo MM. Spike timing-dependent plasticity: from synapse to
perception. Physiol Rev. 2006 Jul;86(3):1033-48. Review.
 Rao, R. and Sejnowski, T. Spike-timing-dependent Hebbian plasticity as
temporal difference learning. Neural Comput, 13(10):2221–2237, 2001.
 Song, S., Miller, K. D., & Abbott, L. F. (2000). Competitive Hebbian learning
through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3,
919-926.
 http://en.wikipedia.org/wiki/
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Thank you
Any Questions?
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