Theoretical models and computer simulations of neural learning systems.
It has been generally assumed for a long time that learning is accomplished in the central nervous system (CNS) by modifying strengths of ties between neurons. Various mechanisms may contribute to this process, but it is not known which are the specific mechanisms, and what are the rules by which they operate. Theoretical models, which are based on that general assumption are introduced. The purpose of the models is to suggest plausible ways by which learned information may be stored in the neural network, and be retrieved when it is needed. The networks in the models consist of four basic subunits, in accordance with identified units in the CNS: sensing, response, feeling, and control, plus association areas. The suggested operation rules are based on established operation rules of individual neurons, and assumed rules when neurons in groups are considered. Computer simulations are done, to check the consistency of the models, and to illustrate how they work. They simulate how an hypothetical kitten learns part of its environment, and show how relevant information may be stored and retrieved in its neuronal network. The suggested mechanisms could be examined in experiments, albeit not easy ones to conduct.
D000818:Animals; D001921:Brain / Q000502:physiology; D002415:Cats; D003201:Computers; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net; D013850:Thinking / Q000502:physiology