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Synaptic weighting for physiological responses in recurrent spiking neural networks

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2 Author(s)
Herzfeld, D.J. ; Dept. of Biomed. Eng., Marquette Univ., Milwaukee, WI, USA ; Beardsley, S.A.

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.

Published in:

Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2011-Sept. 3 2011

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