Characterising information correlation in a stochastic Izhikevich neuron

Yang, Zhijun ORCID logoORCID:, Gandhi, Vaibhav ORCID logoORCID:, Karamanoglu, Mehmet ORCID logoORCID: 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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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
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Depositing User: Vaibhav Gandhi
Date Deposited: 12 Aug 2015 09:45
Last Modified: 29 Nov 2022 22:38

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