Training Spiking Neuronal Networks With Applications in Engineering Tasks

In this paper, spiking neuronal models employing means, variances, and correlations for computation are introduced. We present two approaches in the design of spiking neuronal networks, both of which are applied to engineering tasks. In exploring the input-output relationship of integrate-and-fire (IF) neurons with Poisson inputs, we are able to define mathematically robust learning rules, which can be applied to multilayer and time-series networks. We show through experimental applications that it is possible to train spike-rate networks on function approximation problems and on the dynamic task of robot arm control.

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