Identification of Connectiviity in Neural Networks.

dc.contributor.authorYang, X.en_US
dc.contributor.authorShamma, S.A.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:43:34Z
dc.date.available2007-05-23T09:43:34Z
dc.date.issued1989en_US
dc.description.abstractAnalytical and experimental methods are provided for estimating synaptic connectivities from simultaneous recordings of multiple neurons. The results are based on detailed, yet flexible neuron models in which spike trains are modeled as general doubly stochastic point processes. The expressions derived can be used with non-stationary or stationary records, and can be readily extended from pair-wise to multi-neuron estimates. Furthermore, we show analytically how the estimates are improved as more neurons are sampled, and derive the appropriate normalizations to eliminate stimulus-related correlations. Finally, we illustrate the use and interpretation of the analytical expressions on simulated spike trains and neural networks, and give explicit confidence measures on the estimates.en_US
dc.format.extent1293721 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/4884
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1989-36en_US
dc.titleIdentification of Connectiviity in Neural Networks.en_US
dc.typeTechnical Reporten_US

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