Dendritic Computation Group
Project Review 19 July 2013
Projects
• Modelling dragonfly attention switching
• Dendritic auditory processing
– Mesgarani and Chang, in silicio
– The auditory pathway
• Processing images with spikes
• Dendritic computation with memristors
• Computation in RATSLAM
– Image processing
– SKIM on Spinnaker
• Dendritic computation on Nengo
• SKIM model on FPAA
• Spike based cross-correlation
Auditory Pathway
Audio Signal to Spikes
Poisson Spike Trains for Hair Cell Stimulated at 200Hz
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Neuron firing rate limited by
spike delay
Rectified by the volley
principle and phase-locking
Poisson spike train generated
for each fiber for hair cell
Promotes parallelism and
simplicity in processing
through stochastic
computation
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Dendritic computation with memristors
Jens Burger, Greg Cohen
Memristors for Alpha Functions
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Use tunable resistance of memristor to
control time constants for charging and
discharging of capacitor
Use memristor under 2 conditions
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With fixed resistances
With changing resistances caused by exceeding
threshold
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Matlab code rewritten in C++ and interfaced
to Ngspice
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Compute each synaptic function in Ngspice and
return data to C++ code
Use multi-threading to compute synaptic kernels
in parallel
Results
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Can reproduce results by using RC circuits as
alpha functions
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Worked with identical RC circtuits (resistive) and
different RC circuits (memristive)
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A lot of the computational power lies within the
mapping between inputs and synaptic kernels
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Requirements of synaptic kernels was rather low
and impact of different setups on overall
performance is hard to evaluate
Proof-of-Concept successful
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For parameter and setup exploration we need
more computational resources
Dendritic computation with Nengo
Daniel Rasmussen
FPAA Implementation for the SKIM
model
Suma George,
Georgia Institute of Technology
Atlanta
Replacing SKIM hidden layer neurons with a
dendrite
Spiking patterns for different Input delays
Spiking pattern for different patterns: Dendrite with varying diameter
Generating random weights
SKIM model hidden layer with a single ncompartment dendrite
Spiking pattern for random input weights
Stochastic Electronics:
cross-correlation with neurons
Tara Julia Hamilton, Jonathan Tapson, and others
Calibration with square wave inputs gives
phase delay in histogram i.e. it works!
Autocorrelation with a
single neuron
Block diagram of chip
Crossorrelation with
two neurons