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A Model for the Peak-Interval Task Based on Neural Oscillation Delimited States

Timing mechanisms in the brain are still an open issue. Several existing computational models for timing can reproduce properties of experimental psychophysical responses. Still, only a few consider the underlying biological mechanisms, such as the synchronized neural activity that occurs in several brain areas. In this paper, we introduce a model for the peak-interval task based on neuronal network properties. We consider that Local Field Potential (LFP) oscillation cycles specify a sequence of states, represented as neuronal ensembles. Repeated presentation of time intervals during training reinforces the connections of specific ensembles to downstream networks. Later, during the peak-interval procedure, these downstream networks are reactivated by previously experienced neuronal ensembles, triggering actions at the learned time intervals. The model reproduces experimental response patterns from individual rats in the peak-interval procedure, satisfying relevant properties such as the Weber law. Finally, the model provides a biological interpretation of its parameters.