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Dendritic Computation Group

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

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Dendritic Computation Group

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  1. Dendritic Computation Group Project Review 19 July 2013

  2. Projects • Modellingdragonfly 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

  3. Auditory Pathway

  4. Audio Signal to Spikes • 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

  5. Dendritic computation with memristorsJens Burger, Greg Cohen

  6. Memristors for Alpha Functions • Use tunable resistance of memristor to control time constants for charging and discharging of capacitor • Use memristor under 2 conditions • With fixed resistances • With changing resistances caused by exceeding threshold

  7. Implementation • Matlab code rewritten in C++ and interfaced to Ngspice • Compute each synaptic function in Ngspice and return data to C++ code • Use multi-threading to compute synaptic kernels in parallel

  8. Results • Can reproduce results by using RC circuits as alpha functions • Worked with identical RC circtuits (resistive) and different RC circuits (memristive)

  9. Comments • A lot of the computational power lies within the mapping between inputs and synaptic kernels • Requirements of synaptic kernels was rather low and impact of different setups on overall performance is hard to evaluate • Proof-of-Concept successful • For parameter and setup exploration we need more computational resources

  10. Dendritic computation with NengoDaniel Rasmussen

  11. FPAA Implementation for the SKIM model Suma George, Georgia Institute of Technology Atlanta

  12. Replacing SKIM hidden layer neurons with a dendrite

  13. Spiking patterns for different Input delays Spiking pattern for different patterns: Dendrite with varying diameter

  14. Generating random weights

  15. SKIM model hidden layer with a single n-compartment dendrite

  16. Spiking pattern for random input weights

  17. 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

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