A forecast-based biologically-plausible
STDP learning rule
Sergio Davies, Alexander Rast, Francesco Galluppi
and Steve Furber
APT group
The University of Manchester
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Overview of topics
Standard STDP learning rule
Description of the new approach
Statistical details involved
The STDP-TTS
Test environment
Learning features
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Spike Timing Dependent Plasticity
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Implementation
Triggering the STDP algorithm
The usual way:
STDP is triggered on:
The SpiNNaker way:
STDP is triggered only on pre-synaptic
spike arrival (LTD and LTP)
Pre-synaptic spike arrival (LTD)
Post-synaptic spike emission (LTP)
Weights are available only at presynaptic spike arrival.
Since LTP needs future information, the
algorithm needs to be deferred until the
time window is filled
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
New approach
Is it possible to simplify the STDP model so that its
implementation on SpiNNaker is more performant
(from both memory and computational points of view)?
To avoid the Deferred Event Model, we need to have
statistics that tell us when a neuron is going to fire in
the future (at least with some probability).
Spike Forecast
Current simulation time
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Statistical approach - 1
The idea is: the higher the membrane potential of a
neuron (that receives a spike), the sooner it is likely to
emit an action potential.
Starting with a random network
of Izhikevich neurons,
fed with input to random
neurons with random delays;
We store all the activity in the
network (especially membrane
potential evolution).
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Statistical approach - 2
Raster plot in Matlab (fixed−point)
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IJCNN 2011
S. Jose, CA, USA
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Sergio Davies
A forecast-based biologically-plausible STDP learning rule
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Statistical approach - 3
Outgoing spikes
We evaluate all the
couples (membrane
potential; time-to-spike)
Membrane potential
Membrane reset potential
Firing threshold
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Results of the statistical approach
Representation of all the couples computed before
Sliding window (512 samples)
filtered representation
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Membrane potential (mV)
IJCNN 2011
S. Jose, CA, USA
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Time-To-Spike forecast (msec)
Time-To-Spike forecast (msec)
Raw representation
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Sergio Davies
A forecast-based biologically-plausible STDP learning rule
40
The STDP-TTS
The wider the STDP time window, the greater the
uncertainty of the forecast of the time-to-spike. We limit
the STDP time window to 32 msec.
Time-To-Spike forecast
(msec)
Forecast limited to
The learning window
512
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Membrane potential (mV)
-65mV
-40mV
“L” mV
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
30mV
Input provided
Neuron ID
Can you identify any pattern in this raster plot?
Simulation time (msec)
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Input provided
Neuron ID
Solution: in red the pattern
Simulation time (msec)
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Testing the features
To test this forecast learning rule we use as a
benchmark the tests ran by Masquelier et al. in 2008
and 2009.
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Results of the tests - 1
STDP-TTS (with forecast)
Standard STDP
L = -60mV
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Delay after pattern input (msec)
L = -65mV
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IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
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Results of the tests - 2
Two output neurons – one input pattern
Standard STDP
Neuron 1
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STDP with forecast
Neuron 1
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Neuron 2
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IJCNN 2011
S. Jose, CA, USA
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Sergio Davies
A forecast-based biologically-plausible STDP learning rule
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Results of the tests - 3
Four output neurons – two input patterns
standard STDP
Pattern 1
Neuron 1
Neuron 2
Neuron 3
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Pattern 2
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IJCNN 2011
S. Jose, CA, USA
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Sergio Davies
A forecast-based biologically-plausible STDP learning rule
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Results of the tests - 4
Four output neurons – two input patterns
STDP with forecast
Neuron 1
Neuron 2
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Neuron 3
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Neuron 4
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Pattern 1
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Pattern 2
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IJCNN 2011
S. Jose, CA, USA
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Sergio Davies
A forecast-based biologically-plausible STDP learning rule
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Thank you!!!
Questions???
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Backup slides
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Limitations of the new rule
Outgoing spikes
STDP with forecast
cannot tune
to the earliest
spikes
The forecast
function is related
to the type of
Izhikevich
neuron
Synaptic plasticity threshold
Membrane potential
Membrane reset potential
IJCNN 2011
Sergio Davies
Firing
threshold
S. Jose, CA, USA
A forecast-based biologically-plausible STDP learning rule
The Izhikevich phase space
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule
Implementation on SpiNNaker
LTP 1
LTP 2
LTD 1
LTD 2
Pre
time
Post
time
LTP 1
LTD 1
Incoming spike
Incoming spike delayed by synapse
Synaptic delay
Pre
time
Post
time
Forecast based on the current
neuron membrane potential
Forecasted spike
Real outgoing spike
IJCNN 2011
S. Jose, CA, USA
Sergio Davies
A forecast-based biologically-plausible STDP learning rule