Abstract
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 language | English |
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Title of host publication | Unknown Host Publication |
Publisher | Institution of Engineering and Technology |
Pages | 7-12 |
Number of pages | 6 |
Publication status | Published (in print/issue) - 13 Sept 2007 |
Event | Irish Signals and Systems Conference - Derry, Ireland Duration: 13 Sept 2007 → … |
Conference
Conference | Irish Signals and Systems Conference |
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Period | 13/09/07 → … |