A model for the peak-interval task based on neural oscillation-delimited states.
Specific mechanisms underlying how the brain keeps track of time are largely unknown. Several existing computational models of timing reproduce behavioral results obtained with experimental psychophysical tasks, but only a few tackle the underlying biological mechanisms, such as the synchronized neural activity that occurs throughout 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 - sets of neurons connected to the sequence of states. Later, during the peak-interval procedure, these downstream networks are reactivated by previously experienced neuronal ensembles, triggering behavioral responses 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, we provide a biological interpretation of the parameters of the model.
['Varella|Thiago T|TT|', 'Reyes|Marcelo Bussotti|MB|', 'Caetano|Marcelo S|MS|', 'de Camargo|Raphael Y|RY|']
D000200:Action Potentials D000818:Animals D001921:Brain D008297:Male D008959:Models, Neurological D009474:Neurons D051381:Rats D012054:Reinforcement, Psychology D013998:Time Perception
Timing Computational model Weber law Biological neural network Peak-procedure Scalar property