Deterministic Coincidence Detection and Adaptation Via Delayed Inputs

Z. Yang, Alan Murray, Juan (Annie) Huo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A model of one integrate-and-firing (IF) neuron with two afferent excitatory synapses is studied analytically. This is to discuss the influence of different model parameters, i.e., synaptic efficacies, synaptic and membrane time constants, on the postsynaptic neuron activity. An activation window of the postsynaptic neuron, which is adjustable through spike-timing dependent synaptic adaptation rule, is shown to be associated with the coincidence level of the excitatory postsynaptic potentials (EPSPs) under several restrictions. This simplified model, which is intrinsically the deterministic coincidence detector, is hence capable of detecting the synchrony level between intercellular connections. A model based on the proposed coincidence detection is provided as an example to show its application on early vision processing.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (LNCS 3254) series
Pages6
Number of pages1
Publication statusPublished - 2008

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