Coupling an aVLSI neuromorphic vision chip to a neurotrophic model of synaptic plasticity: the development of topography

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 cell's feedback regime exploits the adaptation dynamics of floating gate pFET '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 cell's 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 reaction-diffusion 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 cell's design is ideally suited for analogue VLSI implementation of Self-Organizing Feature Map (SOFM) models of cortical feature maps.

Developmental robotics: manifesto and application

We argue that all embodied organisms, whether robots or animals, face the same challenge: of adapting to bodies, brains and environments that undergo constant and inevitable change. After highlighting the evidence for the universal role of a class of molecular factors called neurotrophic factors in the response of animals to this challenge, we suggest that implementing models of neurotrophic interactions on robots may confer on them the adaptability and robustness exhibited by animals. We briefly review a mathematical model of neurotrophic interactions and then discuss its application in a robotic context. Finally, we examine the potential, or otherwise, of our approach to developmental robotics.