ArticleNpj complexity2026
Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys.
Article in Npj complexity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EEG microstate transition cost correlates with task demands.PLoS computational biology · 2024Article
- Using network control theory to study the dynamics of the structural connectome.bioRxiv : the preprint server for biology · 2023Article
- Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study.Molecular psychiatry · 2023Article
- Topological insights into the neural basis of flexible behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Quantifying brain state transition cost via Schrödinger Bridge.Network neuroscience (Cambridge, Mass.) · 2022Article
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8 authors.
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Abstract
Primates utilize distributed neural circuits to learn habits in uncertain environments, but the underlying mechanisms remain poorly understood. We propose a formal theory of network energetics explaining how brain states influence sequential behavior. We test our theory on multi-unit recordings from the caudate nucleus and cortical regions of macaques performing a motor habit task. The theory predicts the energy required to transition between brain states represented by trial-specific firing rates across channels, assuming activity spreads through effective connections. We hypothesized that habit formation would correlate with lower control energy. Consistent with this, we observed smaller energy requirements for transitions between similar saccade patterns and those of intermediate complexity, and sessions exploiting fewer patterns. Simulations ruled out confounds from neurons' directional tuning. Finally, virtual lesioning demonstrated the robustness of observed relationships between control energy and behavior. This work paves the way for examining how behavior arises from changing activity in distributed circuitry.
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