Neurodynamical Systems for Cognition and Target Identification

摘要

Our study of cognitive automated target recognition based on the neural paradigmfor information processing reveals that inclusion of bifurcation and synchronicity (phase-locking) in network dynamics can markedly improve the performance of ATR systems. This gave impetus to our study of how synchronicity could arise in cortical networks when it is known the brain has no central clock. Raising this question has led us, through analysis of models of biological neurons employing the tools of nonlinear dynamics, to the development of the bifurcating neuron concept and model. This spiking neuron model combines functional complexity comparable to that of biological neurons with structural simplicity and low power consumption when implemented electronically or optoelectronically. These attributes make the bifurcating neuron ideally suited for use as building block of a new generation of spiking neural networks that employ phase-locking, bifurcation and chaos, on the single processing element level, to emulate higher-level cortical functions such as feature-binding and cognition that are essential for advanced ATR systems, and other operations like separation of object from background, inferencing and rudimentary reasoning.