Perirhinal-amygdala circuit-level computational model of temporal encoding in fear conditioning

Abstract

Here we present a real-time model of fear conditioning in which the functional anatomy and neurophysiology of the lateral amygdala and perirhinal cortex provide a mechanism for temporal learning during Pavlovian conditioning. The model uses realistic neuronal and circuit dynamics to map time onto space and relies on a conventional Hebbian learning rule that requires strict temporal contiguity for synaptic modification. The input- output relationships of the model neurons simulate our physiological recordings with respect to latency to fire, firing frequency, and accommodation tendency. Chains of these neurons form a spectrum of activity windows delayed by various amounts from the conditioned stimulus onset. Simulations reveal that learning occurs only when the conditioned and unconditioned stimuli are explicitly paired, that the interstimulus interval (ISI) is accurately learned over a tune range from 0.5 to 16 sec, and that low-frequency noise causes the accuracy of temporal learning to decrease as the ISI increases, in accordance with a Weber-type law.