A NEURAL MODEL FOR THE DETECTION OF TEMPORAL STRUCTURE
AbstractAccording Â to Â the Â temporal Â correlation Â hypothesis, Â synchronization Â of Â neural Â activity Â indifferent spatial maps solves the feature binding problem. Here, a new model of visual workingÂ memory is proposed which is able to group synchronous temporal events without neuralÂ synchronization. Instead, the model is based on the difference in the firing rate. The modelÂ integrates discrete inputs over time and compares activity in different integrators. When theÂ amplitude difference in integrators is large enough due to the different rates of evidenceÂ accumulation, temporal figure and background are distinguished in the working memory. ComputerÂ simulations showed that the model correctly groups events according to their deterministic orÂ stochastic temporal structure. The model is robust with respect to the temporal noise and to theÂ correlation between figure-ground events. Also, the model is able to explain visual prior entry andÂ perceptual asynchrony between colour, motion and orientation.