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  Hold your methods! How multineuronal firing ensembles can be studied using classical spike-train analysis techniques

Jurjuţ, O. F., Gheorghiu, M., Singer, W., Nikolić, D., & Mureşan, R. C. (2019). Hold your methods! How multineuronal firing ensembles can be studied using classical spike-train analysis techniques. Frontiers in Systems Neuroscience, 13: 21. doi:10.3389/fnsys.2019.00021.

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Copyright © 2019 Jurjuţ, Gheorghiu, Singer, Nikolić and Mureşan

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Jurjuţ, Ovidiu F., Author
Gheorghiu, Medorian, Author
Singer, Wolf1, 2, Author                 
Nikolić, Danko, Author
Mureşan, Raul C., Author
Affiliations:
1Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, Deutschordenstr. 46, 60528 Frankfurt, DE, ou_2074314              
2Singer Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, DE, ou_3381220              

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Free keywords: autocorrelation classical spike-train analysis cross-correlation ensembles multineuronal activity peri-stimulus time histogram tuning curve visual cortex
 Abstract: Responses of neuronal populations play an important role in the encoding of stimulus related information. However, the inherent multidimensionality required to describe population activity has imposed significant challenges and has limited the applicability of classical spike train analysis techniques. Here, we show that these limitations can be overcome. We first quantify the collective activity of neurons as multidimensional vectors (patterns). Then we characterize the behavior of these patterns by applying classical spike train analysis techniques: peri-stimulus time histograms, tuning curves and auto- and cross-correlation histograms. We find that patterns can exhibit a broad spectrum of properties, some resembling and others substantially differing from those of their component neurons. We show that in some cases pattern behavior cannot be intuitively inferred from the activity of component neurons. Importantly, silent neurons play a critical role in shaping pattern expression. By correlating pattern timing with local-field potentials, we show that the method can reveal fine temporal coordination of cortical circuits at the mesoscale. Because of its simplicity and reliance on well understood classical analysis methods the proposed approach is valuable for the study of neuronal population dynamics.

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 Dates: 2019-05-17
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fnsys.2019.00021
 Degree: -

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Title: Frontiers in Systems Neuroscience
  Abbreviation : Front Syst Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 13 Sequence Number: 21 Start / End Page: - Identifier: ISSN: 1662-5137
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5137