Mainen, ZF

Mazucatto, L

Murakami, M

Pereira-Obilinovic, U

Recanatesi, S

English

arXiv

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 neural 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 dictionary between activity patterns and actions. We then showed that metastable attractors, representing activity patterns with a reliable sequential structure and large transition timing variability, could be produced by reciprocally coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors relied on correlated variability in this mesoscale feedback loop, predicting a specific structure of low-dimensional correlations that were empirically verified in M2 recordings. Our results suggest a novel mesoscale network motif based on correlated variability supporting naturalistic animal behavior

Recanatesi, Stefano

Obilinovic, Ulises Pereira

Murakami, Masayoshi

Mainen, Zachary

Mazzucato, Luca

PubMed Central

Metastable attractors explain the variable timing of stable behavioral action sequences

Repositório Comum

http://dx.doi.org/10.1016/j.neuron.2021.10.011

Metastable attractors explain the variable timing of stable behavioral action sequences

Abstract

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PubMed Central

Repositório Comum