A Connectionist Model of Septohippocampal Dynamics During Conditioning: Closing the Loop


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Abstract

Septohippocampal interactions determine how stimuli are encoded during conditioning. This study extends a previous neurocomputational model of corticohippocampal processing to incorporate hippocamposeptal feedback and examines how the presence or absence of such feedback affects learning in the model. The effects of septal modulation in conditioning were simulated by dynamically adjusting the hippocampal learning rate on the basis of how well the hippocampal system encoded stimuli. The model successfully accounts for changes in behavior and septohippocampal activity observed in studies of the acquisition, retention, and generalization of conditioned responses and accounts for the effects of septal disruption on conditioning. The model provides a computational, neurally based synthesis of prior learning theories that predicts changes in medial septal activity based on the novelty of stimulus events.

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