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Latency correction in sparse neuronal spike trains

BACKGROUND: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be correcte...

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Autores principales: Kreuz, Thomas, Senocrate, Federico, Cecchini, Gloria, Checcucci, Curzio, Mascaro, Anna Letizia Allegra, Conti, Emilia, Scaglione, Alessandro, Pavone, Francesco Saverio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier/North-Holland Biomedical Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554712/
https://www.ncbi.nlm.nih.gov/pubmed/36075286
http://dx.doi.org/10.1016/j.jneumeth.2022.109703
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author Kreuz, Thomas
Senocrate, Federico
Cecchini, Gloria
Checcucci, Curzio
Mascaro, Anna Letizia Allegra
Conti, Emilia
Scaglione, Alessandro
Pavone, Francesco Saverio
author_facet Kreuz, Thomas
Senocrate, Federico
Cecchini, Gloria
Checcucci, Curzio
Mascaro, Anna Letizia Allegra
Conti, Emilia
Scaglione, Alessandro
Pavone, Francesco Saverio
author_sort Kreuz, Thomas
collection PubMed
description BACKGROUND: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. NEW METHOD: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. RESULTS: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. COMPARISON WITH EXISTING METHOD(S): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. CONCLUSIONS: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.
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spelling pubmed-95547122022-11-01 Latency correction in sparse neuronal spike trains Kreuz, Thomas Senocrate, Federico Cecchini, Gloria Checcucci, Curzio Mascaro, Anna Letizia Allegra Conti, Emilia Scaglione, Alessandro Pavone, Francesco Saverio J Neurosci Methods Article BACKGROUND: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. NEW METHOD: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. RESULTS: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. COMPARISON WITH EXISTING METHOD(S): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. CONCLUSIONS: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available. Elsevier/North-Holland Biomedical Press 2022-11-01 /pmc/articles/PMC9554712/ /pubmed/36075286 http://dx.doi.org/10.1016/j.jneumeth.2022.109703 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Kreuz, Thomas
Senocrate, Federico
Cecchini, Gloria
Checcucci, Curzio
Mascaro, Anna Letizia Allegra
Conti, Emilia
Scaglione, Alessandro
Pavone, Francesco Saverio
Latency correction in sparse neuronal spike trains
title Latency correction in sparse neuronal spike trains
title_full Latency correction in sparse neuronal spike trains
title_fullStr Latency correction in sparse neuronal spike trains
title_full_unstemmed Latency correction in sparse neuronal spike trains
title_short Latency correction in sparse neuronal spike trains
title_sort latency correction in sparse neuronal spike trains
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554712/
https://www.ncbi.nlm.nih.gov/pubmed/36075286
http://dx.doi.org/10.1016/j.jneumeth.2022.109703
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