This repository contains the codes corresponding to the "A-Time-varying-Information-Measure-for-Tracking-Dynamics-of-Neural-Codes-in-a-Neural-Ensemble" paper.

The amount of information that differentially correlated spikes in a neural ensemble carry are not the same; information of different types of spikes are associated with different features of the stimulus. By calculating a neural ensemble's information in response to a mixed stimulus comprising slow and fast signals, we show the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons over time. By applying TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales, which are explained by TVE. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate slow and fast features of the stimulus can be entirely reconstructed when this decoder applies to asynchronous and synchronous spikes, respectively.

• Run MainSolution.m in Matlab

Please use the following citation:

Mohammadreza Rezaei, Milos R. Popovic, and Milad Lankarany