Development of on-off and off-on receptive fields using a semistochastic model
A model for ontogenetic development of receptive fields in the visual nervous system is presented. The model uses a semistochastic approach where random uncorrelated activity is generated in the input layer and propagated through the network. The evolution of the synaptic connections between two neurons are assumed to be a function of their activity, with two interpretations of the Hebb's rule: (a) the synaptic weight is modified proportional to the product of the activity of the two connected neurons; and (b) proportional to the statistical correlation of their activity. Both models explain the origin of either on-off and off-on receptive fields with symetric and non symetric forms. These results agree with previous models based on deterministic equations. The approach presented here has two main advantages. Firstly the lower computer time that allows the study of more complex architectures. And secondly, the possibility of the extension of this model to cover more complex behavior, for instance, the inclusion of time delay in the transmition of the activity between layers.
keywordsneural networks self-organization receptive fields unsupervised learning semistochastic models
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