Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model
Joint Authors
Choi, Jee Hyun
Kim, Jinsoo
Kim, Sung Phil
Sin, Duho
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sin, Duho& Kim, Jinsoo& Choi, Jee Hyun& Kim, Sung Phil. 2014. Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-455486
Modern Language Association (MLA)
Sin, Duho…[et al.]. Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model. Journal of Applied Mathematics No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-455486
American Medical Association (AMA)
Sin, Duho& Kim, Jinsoo& Choi, Jee Hyun& Kim, Sung Phil. Neuronal Ensemble Decoding Using a Dynamical Maximum Entropy Model. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-455486
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-455486