Characterising information correlation in a stochastic Izhikevich neuron

Yang, Zhijun ORCID logoORCID: https://orcid.org/0000-0003-2615-4297, Gandhi, Vaibhav ORCID logoORCID: https://orcid.org/0000-0003-1121-7419, Karamanoglu, Mehmet ORCID logoORCID: https://orcid.org/0000-0002-5049-2993 and Graham, Bruce (2015) Characterising information correlation in a stochastic Izhikevich neuron. 2015 International Joint Conference on Neural Networks (IJCNN). In: International Joint Conference on Neural Networks (IJCNN 2015), 12-17 Jul 2015, Killarney, Republic of Ireland. e-ISBN 9781479919604, e-ISBN 9781479919598. ISSN 2161-4393 [Conference or Workshop Item]

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Abstract

The Izhikevich spiking neuron model is a relatively new mathematical framework which is able to represent many observed spiking neuron behaviors, excitatory or inhibitory, by simply adjusting a set of four model parameters. This model is deterministic in nature and has achieved wide applications in analytical and numerical analysis of biological neurons due largely to its biological plausibility and computational efficiency. In this work we present a stochastic version of the Izhikevich neuron, and measure its performance in transmitting information in a range of biological frequencies. The work reveals that the deterministic Izhikevich model has a wide information transmission range and is generally better in transmitting information than its stochastic counterpart.

Item Type: Conference or Workshop Item (Poster)
Research Areas: A. > School of Science and Technology
A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 17370
Notes on copyright: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Vaibhav Gandhi
Date Deposited: 12 Aug 2015 09:45
Last Modified: 29 Nov 2022 22:38
URI: https://eprints.mdx.ac.uk/id/eprint/17370

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