A model of sequential prediction in the brain using an oscillatory network

Abstract

The predictive brain is a term that is known because of the capability of our neural system to find a model of the environment and to use it for predicting the incoming stimulus. It has not been fully understood that how our neural system that consists of millions of oscillatory networks can create, update, and maintain a model for the sequential prediction. In the current paper, we have proposed an oscillatory network that its units connect to each other through synchronization mechanism. The network has been used to suggest a possible mechanism of extracting the regularities exist in a continuous performance test. The result of simulations has been compared with the recorded human experiment data. Outcomes showed that the proposed model can mimic the pattern of human behaviors. It can be concluded that brain may create and modified the model of the environment by updating the coupling weight or the level of synchronization between its different units. There are some parameters in the proposed network and the updating procedure that can be used to model different observations of normal or abnormal human behaviors in sequential prediction.