A Biologically Inspired Training Algorithm for Spiking Neural Networks

John Wade, Liam McDaid, JA Santos, Heather Sayers

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


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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherInstitution of Engineering and Technology
Number of pages6
Publication statusPublished (in print/issue) - 13 Sept 2007
EventIrish Signals and Systems Conference - Derry, Ireland
Duration: 13 Sept 2007 → …


ConferenceIrish Signals and Systems Conference
Period13/09/07 → …


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