Dynamics Analysis of a Population Decoding Model

Jiali Yu, Huajin Tang, Haizhou Li

Abstract: Information processing in the nervous system involves the activity of large populations of neurons. It is difficult to extract information from these population codes because of the noise inherent in neuronal responses. We propose a divisive normalization model to read the population codes. The dynamics of the model are analyzed by continuous attractor theory. Under certain conditions, the model possesses continuous attractors. Moreover, the explicit expressions of the continuous attractors are provided. Simulations are employed to illustrate the theory.