Dynamics of antagonistic neural processing elements

Two coupled nonlinear first-order systems whose dynamic behavior reflects the neural states exhibited by a spatially localized population of excitatory and inhibitory nerve cells are described. The dynamics of each constituent neural subpopulation represents a fundamental neural information processing element (PE) of a complex neural system. Phase plane analysis is used in this paper to show how such antagonistic positive acting (excitatory) and negative acting (inhibitory) PEs can generate diverse steady-state and temporal phenomena when the nonlinear system parameters of the PEs are altered. By modifying a selected set of parameters, it is possible to program the positive and negative PEs to exhibit various dynamic attributes such as multiple stable states, transient response behavior and limit-cycle oscillations. These dynamic attributes may be used to perform a variety of useful computational tasks in signal processing and vision systems such as short-term memory (STM), temporal filtering (TF) and pulse frequency modulation (PFM). Computer simulations are presented throughout this paper in order to illustrate these dynamic attributes.