Deterministic coincidence detection and adaptation via delayed inputs

Yang, Zhijun, Murray, Alan and Huo, Juan (2008) Deterministic coincidence detection and adaptation via delayed inputs. In: 18th International Conference on Artificial Neural Networks (ICANN 2008), 3-6 September 2008, Prague, Czech Republic.

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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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 9922
Useful Links:
Depositing User: Zhijun Yang
Date Deposited: 31 Jan 2013 06:43
Last Modified: 13 Oct 2016 14:25
URI: https://eprints.mdx.ac.uk/id/eprint/9922

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