neuronal ensemble decoding using a dynamical maximum entropy model
Clicks: 110
ID: 233464
2014
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
110 views
10 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #165 of 357 articles by views in Chemico-biological interactions
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 357 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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.
| Reference Key |
sin2014journalneuronal
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Duho Sin;Jinsoo Kim;Jee Hyun Choi;Sung-Phil Kim |
| Journal | Chemico-biological interactions |
| Year | 2014 |
| DOI |
10.1155/2014/218373
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.