|Title||A neural network approach to hippocampal function in classical conditioning.|
|Publication Type||Journal Article|
|Year of Publication||1991|
|Authors||Schmajuk, NA, DiCarlo, JJ|
|Pagination||82 - 110|
|Keywords||Animals, Cerebellum, Classical, Computer Simulation, Conditioning, Extinction, Eyelid, Hippocampus, Models, Nerve Net, Neurological, Neurons, Psychological, Rabbits, Reaction Time|
Hippocampal participation in classical conditioning in terms of S. Grossberg's (1975) attentional theory is described. According to the theory, pairing of a conditioned stimulus/stimuli (CS) with an unconditioned stimulus/stimuli (UCS) causes both an association of the sensory representation of the CS with the UCS (conditioned reinforcement learning) and an association of the sensory representation of the CS with the drive representation of the UCS (incentive motivation learning). Sensory representations compete for a limited-capacity short-term memory (STM). The STM regulation hypothesis, which proposes that the hippocampus controls incentive motivation, self-excitation, and competition among sensory representations thereby regulating the contents of a limited capacity STM, is introduced. Under the STM regulation hypothesis, nodes and connections in Grossberg's neural network are mapped onto regional hippocampal-cerebellar circuits. The resulting neural model provides (a) a framework for understanding the dynamics of information processing and storage in the hippocampus and cerebellum during classical conditioning of the rabbit's nictitating membrane, (b) principles for understanding the effect of different hippocampal manipulations on classical conditioning, and (c) novel and testable predictions.
|Short Title||Behavioral Neuroscience|