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Reconstructing the functional connectivity of multiple spike trains using Hawkes models

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Date
2018
Link to item file
https://hal.archives-ouvertes.fr/hal-01585986
Dewey
Sciences connexes (physique, astrophysique)
Sujet
connectivity; spike train analysis; neuron correlation; lasso penalization; least-square estimation; Hawkes processes
Journal issue
Journal of Neuroscience Methods
Volume
297
Number
1 March 2018
Publication date
2018
Article pages
9-21
DOI
http://dx.doi.org/10.1016/j.jneumeth.2017.12.026
URI
https://basepub.dauphine.fr/handle/123456789/17535
Collections
  • CEREMADE : Publications
Metadata
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Author
Lambert, Régis
542136 Neurosciences Paris Seine [NPS]
Tuleau-Malot, Christine
199970 Laboratoire Jean Alexandre Dieudonné [JAD]
Bessaih, Thomas
542136 Neurosciences Paris Seine [NPS]
Rivoirard, Vincent
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Bouret, Yann
268400 Laboratoire de Physique de la Matière Condensée [LPMC]
Leresche, Nathalie
542136 Neurosciences Paris Seine [NPS]
Reynaud-Bouret, Patricia
199970 Laboratoire Jean Alexandre Dieudonné [JAD]
Type
Article accepté pour publication ou publié
Abstract (EN)
Background: Statistical models that predict neuron spike occurrence from the earlier spiking activity of the whole recorded network are promising tools to reconstruct functional connectivity graphs. Some of the previously used methods were in the general statistical framework of the multivariate Hawkes processes but they often required huge amount of data, prior knowledge about the recorded network, and may generate non stationary models that could not be directly used in simulation. New Method: Here, we present a method, based on least-square estimators and LASSO penalty criteria, optimizing Hawkes models that can be used for simulation. Results: Challenging our method to multiple Integrate and Fire models of neuron networks demonstrated that it eciently detects both excitatory and inhibitory connections. The few errors that occasionally occurred with complex networks including common inputs, weak and chained connections, could easily be discarded based on objective criteria. Conclusions: The present method is robust, stable, applicable with an experimentally realistic amount of data, and does not require any prior knowledge of the studied network. Therefore, it can be used on a personal computer as a turn-key procedure to infer connectivity graphs and generate simulation models from simultaneous spike train recordings

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