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Metastable attractors explain the variable timing of stable behavioral action sequences

View ORCID ProfileStefano Recanatesi, Ulises Pereira, View ORCID ProfileMasayoshi Murakami, Zachary Mainen, Luca Mazzucato
doi: https://doi.org/10.1101/2020.01.24.919217
Stefano Recanatesi
University of Washington, Center for Computational Neuroscience and Swartz Center, SeattleInstitute of Neuroscience, University of Oregon, Eugene
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  • ORCID record for Stefano Recanatesi
Ulises Pereira
Department of Statistics, The University of Chicago, ChicagoCenter for Neural Science, New York University, New York
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Masayoshi Murakami
Department of Neurophysiology, University of Yamanashi, JapanChampalimaud Centre for the Unknown, Lisbon, Portugal
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  • ORCID record for Masayoshi Murakami
Zachary Mainen
Champalimaud Centre for the Unknown, Lisbon, Portugal
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  • For correspondence: zmainen@neuro.fchampalimaud.org
Luca Mazzucato
Institute of Neuroscience, University of Oregon, EugeneDepartments of Biology and Mathematics, University of Oregon, Eugene
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  • For correspondence: zmainen@neuro.fchampalimaud.org
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ABSTRACT

Natural animal behavior displays rich lexical and temporal dynamics, even in a stable environment. This implies that behavioral variability arises from sources within the brain, but the origin and mechanics of these processes remain largely unknown. Here, we focus on the observation that the timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action timing fluctuations. Using hidden Markov models we established a robust and accurate dictionary between ensemble activity patterns and actions. We then showed that metastable attractors, representing activity patterns with the requisite combination of reliable sequential structure and high transition timing variability, could be produced by reciprocally coupling a high dimensional recurrent network and a low dimensional feedforward one. Transitions between attractors were generated by correlated variability arising from the feedback loop between the two networks. This mechanism predicted a specific structure of low-dimensional noise correlations that were empirically verified in M2 ensemble dynamics. This work suggests a robust network motif as a novel mechanism to support critical aspects of animal behavior and establishes a framework for investigating its circuit origins via correlated variability.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 25, 2020.
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Metastable attractors explain the variable timing of stable behavioral action sequences
Stefano Recanatesi, Ulises Pereira, Masayoshi Murakami, Zachary Mainen, Luca Mazzucato
bioRxiv 2020.01.24.919217; doi: https://doi.org/10.1101/2020.01.24.919217
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Metastable attractors explain the variable timing of stable behavioral action sequences
Stefano Recanatesi, Ulises Pereira, Masayoshi Murakami, Zachary Mainen, Luca Mazzucato
bioRxiv 2020.01.24.919217; doi: https://doi.org/10.1101/2020.01.24.919217

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