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A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

1
Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
2
KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON M5G 2A2, Canada
3
Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(8), 880; https://doi.org/10.3390/e22080880
Received: 24 May 2020 / Revised: 4 August 2020 / Accepted: 6 August 2020 / Published: 11 August 2020
The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is 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 that 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 at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code. View Full-Text
Keywords: information-theoretic measure; rate code; temporal code; multiplexed code; asynchronous and synchronous spikes information-theoretic measure; rate code; temporal code; multiplexed code; asynchronous and synchronous spikes
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Rezaei, M.R.; Popovic, M.R.; Lankarany, M. A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble. Entropy 2020, 22, 880.

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