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- W2062801103 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." @default.
- W2062801103 created "2016-06-24" @default.
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- W2062801103 date "2011-08-01" @default.
- W2062801103 modified "2023-09-26" @default.
- W2062801103 title "Synaptic weighting for physiological responses in recurrent spiking neural networks" @default.
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- W2062801103 doi "https://doi.org/10.1109/iembs.2011.6091039" @default.
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