Dendritic Computation Group
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Dendritic Computation GroupProject Review 19 July 2013ProjectsModelling dragonfly attention switchingDendritic auditory processingMesgarani and Chang, in silicioThe auditory pathwayProcessing images with spikesDendritic computation with memristors Computation in RATSLAMImage processingSKIM on SpinnakerDendritic computation on NengoSKIM model on FPAASpike based cross-correlation
Auditory Pathway
3Audio Signal to Spikes
Neuron firing rate limited by spike delayRectified by the volley principle and phase-lockingPoisson spike train generated for each fiber for hair cellPromotes parallelism and simplicity in processing through stochastic computation 4Dendritic computation with memristorsJens Burger, Greg Cohen
Memristors for Alpha FunctionsUse tunable resistance of memristor to control time constants for charging and discharging of capacitorUse memristor under 2 conditionsWith fixed resistancesWith changing resistances caused by exceeding threshold
6ImplementationMatlab code rewritten in C++ and interfaced to NgspiceCompute each synaptic function in Ngspice and return data to C++ codeUse multi-threading to compute synaptic kernels in parallel
7ResultsCan reproduce results by using RC circuits as alpha functionsWorked with identical RC circtuits (resistive) and different RC circuits (memristive)
8A lot of the computational power lies within the mapping between inputs and synaptic kernelsRequirements of synaptic kernels was rather low and impact of different setups on overall performance is hard to evaluateProof-of-Concept successfulFor parameter and setup exploration we need more computational resourcesComments9Dendritic computation with NengoDaniel Rasmussen
FPAA Implementation for the SKIM model Suma George,Georgia Institute of TechnologyAtlanta
Replacing SKIM hidden layer neurons with a dendrite
Spiking patterns for different Input delays
Spiking pattern for different patterns: Dendrite with varying diameterGenerating random weights
SKIM model hidden layer with a single n-compartment dendrite
Spiking pattern for random input weights
Stochastic Electronics: cross-correlation with neuronsTara Julia Hamilton, Jonathan Tapson, and others
Autocorrelation with a single neuronCrossorrelation with two neuronsBlock diagram of chip
Calibration with square wave inputs gives phase delay in histogram i.e. it works!