Adaptive Learning Procedure for a Network of Spiking Neurons and Visual Pattern Recognition

This paper presents a novel on-line learning procedure to be used in biologically realistic networks of integrate-and-fire neurons. The on-line adaptation is based on synaptic plasticity and changes in the network structure. Event driven computation optimizes processing speed in order to simulate networks with large number of neurons. The learning method is demonstrated on a visual recognition task and can be expanded to other data types. Preliminary experiments on face image data show the same performance as the optimized off-line method and promising generalization properties.

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