Simulation of Intelligent Computational Models in Biological Systems

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 electrophysiological properties of spiking neurons, various network structures of neurons ha...

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Bibliographic Details
Main Authors: Wu, Qing-Xiang, Mcginnity, Martin, Maguire, Liam, Belatreche, Ammar, Glackin, Brendan
Format: Conference Proceeding
Language:English
Subjects:
Adaptive learning
Biological system modeling
Biological systems
Biology computing
Brain modeling
Circuit simulation
Computational intelligence
Computational model
Computational modeling
Computer networks
Humans
Neurons
Spiking neural network
Spiking time dependent plasticity
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Summary: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 electrophysiological 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.
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370470