Assessing the Impacts of Correlated Variability with Dissociated Timescales.
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| Název: | Assessing the Impacts of Correlated Variability with Dissociated Timescales. |
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| Autoři: | Takahashi T; Department of Bioscience, School of Science and Technology, Kwansei Gakuin University, Hyogo 669-1337, Japan., Maruyama Y; National Institute of Technology, Hakodate College, Hokkaido 042-8501, Japan., Ito H; Faculty of Information Science and Engineering, Kyoto Sangyo University, Kyoto 603-8555, Japan., Miura K; Department of Bioscience, School of Science and Technology, Kwansei Gakuin University, Hyogo 669-1337, Japan. |
| Zdroj: | ENeuro [eNeuro] 2019 Mar 20; Vol. 6 (1). Date of Electronic Publication: 2019 Mar 20 (Print Publication: 2019). |
| Způsob vydávání: | Journal Article; Research Support, Non-U.S. Gov't |
| Jazyk: | English |
| Informace o časopise: | Publisher: Society for Neuroscience Country of Publication: United States NLM ID: 101647362 Publication Model: eCollection Cited Medium: Internet ISSN: 2373-2822 (Electronic) Linking ISSN: 23732822 NLM ISO Abbreviation: eNeuro Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Washington, DC] : Society for Neuroscience, [2014]- |
| Výrazy ze slovníku MeSH: | Models, Neurological*, Action Potentials/*physiology , Sensory Receptor Cells/*physiology, Animals ; Cats ; Male ; Photic Stimulation/methods ; Time Factors |
| Abstrakt: | Despite the profound influence on coding capacity of sensory neurons, the measurements of noise correlations have been inconsistent. This is, possibly, because nonstationarity, i.e., drifting baselines, engendered the spurious long-term correlations even if no actual short-term correlation existed. Although attempts to separate them have been made previously, they were ad hoc for specific cases or computationally too demanding. Here we proposed an information-geometric method to unbiasedly estimate pure short-term noise correlations irrespective of the background brain activities without demanding computational resources. First, the benchmark simulations demonstrated that the proposed estimator is more accurate and computationally efficient than the conventional correlograms and the residual correlations with Kalman filters or moving averages of length three or more, while the best moving average of length two coincided with the propose method regarding correlation estimates. Next, we analyzed the cat V1 neural responses to demonstrate that the statistical test accompanying the proposed method combined with the existing nonstationarity test enabled us to dissociate short-term and long-term noise correlations. When we excluded the spurious noise correlations of purely long-term nature, only a small fraction of neuron pairs showed significant short-term correlations, possibly reconciling the previous inconsistent observations on existence of significant noise correlations. The decoding accuracy was slightly improved by the short-term correlations. Although the long-term correlations deteriorated the generalizability, the generalizability was recovered by the decoder with trend removal, suggesting that brains could overcome nonstationarity. Thus, the proposed method enables us to elucidate the impacts of short-term and long-term noise correlations in a dissociated manner. |
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| Contributed Indexing: | Keywords: decoding analysis; information geometry; noise correlations; population codes; primary visual cortex; spontaneous activity |
| Entry Date(s): | Date Created: 20190326 Date Completed: 20190429 Latest Revision: 20200309 |
| Update Code: | 20250114 |
| PubMed Central ID: | PMC6428564 |
| DOI: | 10.1523/ENEURO.0395-18.2019 |
| PMID: | 30906854 |
| Databáze: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Despite the profound influence on coding capacity of sensory neurons, the measurements of noise correlations have been inconsistent. This is, possibly, because nonstationarity, i.e., drifting baselines, engendered the spurious long-term correlations even if no actual short-term correlation existed. Although attempts to separate them have been made previously, they were ad hoc for specific cases or computationally too demanding. Here we proposed an information-geometric method to unbiasedly estimate pure short-term noise correlations irrespective of the background brain activities without demanding computational resources. First, the benchmark simulations demonstrated that the proposed estimator is more accurate and computationally efficient than the conventional correlograms and the residual correlations with Kalman filters or moving averages of length three or more, while the best moving average of length two coincided with the propose method regarding correlation estimates. Next, we analyzed the cat V1 neural responses to demonstrate that the statistical test accompanying the proposed method combined with the existing nonstationarity test enabled us to dissociate short-term and long-term noise correlations. When we excluded the spurious noise correlations of purely long-term nature, only a small fraction of neuron pairs showed significant short-term correlations, possibly reconciling the previous inconsistent observations on existence of significant noise correlations. The decoding accuracy was slightly improved by the short-term correlations. Although the long-term correlations deteriorated the generalizability, the generalizability was recovered by the decoder with trend removal, suggesting that brains could overcome nonstationarity. Thus, the proposed method enables us to elucidate the impacts of short-term and long-term noise correlations in a dissociated manner. |
|---|---|
| ISSN: | 2373-2822 |
| DOI: | 10.1523/ENEURO.0395-18.2019 |