Synaptic weighting for physiological responses in recurrent spiking neural networks

Recurrently connected neural networks have been used extensively in the literature to describe various neuro-physiological phenomena, such as coordinate transformations during sensorimotor integration. Due to the directed cycles that can exist in recurrent networks, there is no well-known way to a priori specify synaptic weights to elicit neuron spiking responses to stimuli based on available neurophysiology. Using a common mean field assumption in which synaptic inputs are uncorrelated for sufficiently large populations of neurons, we show that the connection topology and a neuron´s response characteristics can be decoupled. This allows specification of neuron steady-state responses independent of the connection topology. We provide evidence from two case studies which serve to validate this synaptic weighting approach.