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A Biologically Inspired Training Algorithm for Spiking Neural Networks

Wade, John, McDaid, Liam, Santos, JA and Sayers, Heather (2007) A Biologically Inspired Training Algorithm for Spiking Neural Networks. In: Irish Signals and Systems Conference, Derry, Ireland. IET. 6 pp. [Conference contribution]

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The work presented in this paper merges the Bienenstock-Cooper-Munro (BCM) learning rule with the Spike Timing Dependant Plasticity (STDP) rule to develop a training algorithm for a multi layer Spiking Neural Network (SNN), stimulated using spike trains. The BCM rule is utilised to modulate the height of the plasticity window, associated with STDP, as a function of the activity of the postsynaptic neurons, and in doing so introduces a correlation between the activity of the postsynaptic neurons and their associated weights. The induced correlation uses the activity of postsynaptic neurons to stabilise the weight values across a multi-layer network causing convergence during training. The training algorithm also includes both exhibitory and inhibitory facilitating dynamic synapses that create a frequency filtering mechanism allowing the information presented to the network to be routed to different neurons. A variable neuron threshold level simulates the refractory period.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:17769
Deposited By: Dr John Wade
Deposited On:01 Apr 2011 14:21
Last Modified:30 Apr 2015 10:06

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