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.
Knopf
G K
GK
Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, Canada.
Gupta
M M
MM
D000465:Algorithms; D008570:Memory, Short-Term; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D015398:Signal Transduction; D013998:Time Perception; D014785:Vision, Ocular