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.
|Title of host publication||Unknown Host Publication|
|Publisher||Institution of Engineering and Technology|
|Number of pages||6|
|Publication status||Published - 13 Sep 2007|
|Event||Irish Signals and Systems Conference - Derry, Ireland|
Duration: 13 Sep 2007 → …
|Conference||Irish Signals and Systems Conference|
|Period||13/09/07 → …|
Wade, J., McDaid, L., Santos, JA., & Sayers, H. (2007). A Biologically Inspired Training Algorithm for Spiking Neural Networks. In Unknown Host Publication (pp. 7-12). Institution of Engineering and Technology.