title: Synaptic weighting for physiological responses in recurrent spiking neural networks.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53233, USA. firstname.lastname@marquette.edu
authors.forename: David J
authors.initials: DJ
authors.lastname: Herzfeld
authors.forename: Scott A
authors.initials: SA
authors.lastname: Beardsley
mesh_terms: D000200:Action Potentials; D006801:Humans; D008962:Models, Theoretical; D009415:Nerve Net; D013569:Synapses / Q000502:physiology*