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The advent of large scale neural computational platforms has highlighted thelack of algorithms for synthesis of neural structures to perform predefinedcognitive tasks. The Neural Engineering Framework offers one such synthesis,but it is most effective for a spike rate representation of neural information,and it requires a large number of neurons to implement simple functions. Wedescribe a neural network synthesis method that generates synaptic connectivityfor neurons which process time-encoded neural signals, and which makes verysparse use of neurons. The method allows the user to specify, arbitrarily,neuronal characteristics such as axonal and dendritic delays, and synaptictransfer functions, and then solves for the optimal input-output relationshipusing computed dendritic weights. The method may be used for batch or onlinelearning and has an extremely fast optimization process. We demonstrate its usein generating a network to recognize speech which is sparsely encoded as spiketimes.

Synthesis of neural networks for spatio-temporal spike pattern recognition and processing