Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model

As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiri...

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Bibliographic Details
Published in:Journal of Applied Mathematics 2014-01, Vol.2014 (2014), p.385-394-136
Main Authors: Kim, Sung Phil, Choi, Jee Hyun, Kim, Jinsoo, Sin, Duho
Format: Article
Language:English
Subjects:
Blends
Brain research
Computer simulation
Decoders
Decoding
Dynamical systems
Entropy
Entropy (Information theory)
Mathematical models
Maximum entropy
Methods
Neural circuitry
Neural networks
Neurons
Probability
R&D
Research & development
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Summary:As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks.
ISSN:1110-757X
1687-0042
DOI:10.1155/2014/218373