An adaptive neuromorphic model of ocular dominance map using floating gate synapse
A novel analogue CMOS design of a cortical cell, that computes weighted sum of inputs, is presented. The cells feedback regime exploits the adaptation dynamics of floating gate p p FET synapse to perform competitive learning amongst input weights as time-staggered winner take all. A learning rate parameter regulates adaptation time and a bias enforces resource limitation by restricting the number of input branches and winners in a competition. When learning ends, the cells response favours one input pattern over others to exhibit feature selectivity. Embedded in a 2-D RC grid, these feature selective cells are capable of performing a symmetry breaking pattern formation, observed in some reactiondiffusion models of cortical feature map formation, e.g. ocular dominance. Close similarity with biological networks in terms of adaptability and long term memory indicates that the cells design is ideally suited for analogue VLSI implementation of Self-Organizing Feature Map (SOFM) models of cortical feature maps.
ocular dominance floating gate synapse feature selectivity reactiondiffusion cortical feature maps winner-take-all (wta)