Abstract
This paper presents an on-line training procedure for a hierarchical neural network of integrate-and-fire neurons. The training is done through 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 training procedure is applied to the face recognition task. Preliminary experiments on a public available face image dataset show the same performance as the optimized off-line method. A comparison with other classical methods of face recognition demonstrates the properties of the system.
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Wysoski, S.G., Benuskova, L., Kasabov, N. (2006). On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_7
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DOI: https://doi.org/10.1007/11840817_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38625-4
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