A Learning Rule to Model the Development of Orientation Selectivity in Visual Cortex

This paper presents a learning rule, CBA, to develop oriented receptive fields similar to those founded in cat striate cortex. The inherent complexity of the development of selectivity in visual cortex has led most authors to test their models by using a restricted input environment. Only recently, some learning rules (the PCA and the BCM rules) have been studied in a realistic visual environment. For these rules, which are based upon Hebbian learning, single neuron models have been proposed in order to get a better understanding of their properties and dynamics. These models suffered from unbounded growing of synaptic strength, which is remedied by a normalization process. However, normalization seems biologically implausible, given the non-local nature of this process. A detailed stability analysis of the proposed rule proves that the CBA attains a stable state without any need for normalization. Also, a comparison among the results achieved in different types of visual environments by the PCA, the BCM and the CBA rules is provided. The final results show that the CBA rule is appropriate for studying the biological process of receptive field formation and its application in image processing and artificial vision tasks.