Simulation of Intelligent Computational Models in Biological Systems

Qingxiang Wu, TM McGinnity, LP Maguire, Ammar Belatreche, Brendan Glackin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)
11 Downloads (Pure)

Abstract

The human brain can perform a range of complicated computations and logical reasoning using neural networks with a huge number of neurons. Since Hodgkin and Huxley proposed a set of equations to describe the electro- physiological properties of spiking neurons, various network structures of neurons have been developed through neuroscience research that can now be simulated by electronic circuits or computer programs. In this paper, an adaptive learning mechanism is simulated based on the biological property related to the spike time dependent plasticity of synapses. A demonstration shows that such spiking neurons are able to develop their specific receptive field for recognition of patterns. This mechanism can be used to explain some adaptive behaviours in biological systems. It is can also be applied to artificial intelligent systems.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1974-1978
Number of pages5
ISBN (Print)978-1-4244-0973-0
DOIs
Publication statusPublished - 1 Aug 2007
EventThe International Conference on Machine Learning and Cybernetics (ICMLC2007) -
Duration: 1 Aug 2007 → …

Conference

ConferenceThe International Conference on Machine Learning and Cybernetics (ICMLC2007)
Period1/08/07 → …

Bibliographical note

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[9] Q. X. Wu, T. M. McGinnity, L. P. Maguire, A. Belatreche and B. Glackin, “Adaptive Co-Ordinate Transformation Based on Spike Timing-Dependent Plasticity Learning Paradigm”, LNCS, Springer, Vol 3610, pp.420-429, 2005.
[10] SenseMaker Project (IST–2001-34712) funded by the European Union under the “Information Society Technologies” Programme, 2002-2006.
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Keywords

  • Spiking neural network
  • computational model
  • adaptive learning
  • spiking time dependent plasticity

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