A neural cocktail-party processor
Biological Cybernetics
IEEE Transactions on Neural Networks
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Advances in understanding the neuronal code employed by cortical networksindicate that networks of parametrically coupled nonlinear iterative maps,each acting as a bifurcation processing element, furnish a potentiallypowerful tool for the modeling, simulation, and study of cortical networksand the host of higher-level processing and control functions they perform.Such functions are central to understanding and elucidating generalprinciples on which the design of biomorphic learning and intelligentsystems can be based. The networks concerned are dynamical in nature, in thesense that they “compute” not only with static (fixed-point)attractors but also with dynamic (periodic and chaotic) attractors. As such,they compute with diverse attractors, and utilize transitions (bifurcation)between attractors and transient chaos to carry out the functions theyperform. An example of a dynamical network, a parametrically coupled net oflogistic processing elements, is described and discussed together some ofits behavioural attributes that are relevant to elucidating the possiblerole for coherence, bifurcation, and chaos in higher-level brain functionscarried out by cortical networks.