Rank-based Hebbian learning in a multilayered neural network

James M. Vaccaro; D. Gourion; Manuel Samuelides; S. Thorpe

doi:10.1117/12.343048

22 March 1999 Rank-based Hebbian learning in a multilayered neural network

James M. Vaccaro, D. Gourion, Manuel Samuelides, S. Thorpe

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343048
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

Abstract

Recent work on biologically motivated networks have shown that the visual system can process a natural scene more quickly by encoding the order of neural firing rather than the frequency of firing. This `order of firing' encoding scheme has led to a rank-based approach which converts activation energy into a time-dependent pulse code. This paper focuses towards the contribution of unsupervised learning to the training of integrate and fire neurons within multi-layer networks. First, we propose an unsupervised learning algorithm and we test it on a simple recognition task. Then, we propose a multilayer architecture of integrate and fire neurons to solve a more complex vision task. This architecture is efficiently trained by an algorithm combining supervised and unsupervised rank-based hebbian learning. Further improvements are proposed in the final discussion.

Citation Download Citation

James M. Vaccaro, D. Gourion, Manuel Samuelides, and S. Thorpe "Rank-based Hebbian learning in a multilayered neural network", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343048

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