Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys
Year: 2026
Type: article
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.
Source: npj Complexity
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: National Science Foundation, John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, Institute for Scientific Interchange, National Institutes of Health, Multidisciplinary University Research Initiative, National Institute on Alcohol Abuse and Alcoholism, National Institute of Mental Health, National Eye Institute, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Institute of Child Health and Human Development, Army Research Office, Army Research Laboratory
Awards: G1001159199, G1024130705, G1041393086, Multimodal brain maturation indices modulating psychopathology and neurocognition, G129985460, G1318417280, NCS-FO: Collaborative Research: Analysis, prediction, and control of synchronized neural activity, G1432373144, G1483473580, G1495680632, G1630141903, G1778482337, G1993459732, G2045641638, G2130352337, G2230306373, Virtual Resection to Treat Epilepsy, G2247336289, G235261508, G2425642435, G2519463293, G2901702611, G2977132040, G343582157, G3690479056, G3784296261, G4069802742, CAREER: Testing the neural representation of sequences in nonhuman primate frontal cortex using fMRI-localized electrophysiology, G4548301804, G4823423002, G5253651465, G5760707814, NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control, CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, Extrapyramidal Systems, G6409978989, G656215873, G657448715, G6671297155, G6752185412, Linguistic and Nonlinguistic Functions of Frontal Cortex, G6981911394, Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence, G7452299184, G7530591242, G7538535954, G7594130654, G7881329, G8164210364, CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, G8554419822, G9000002492, G9000026147, G9000056496, G9000084299, G9000117015, G9000279655, Metabolic control of cue reactivity during alcohol withdrawal, G90128336, G91481811, G916315028, G970350945
OpenAlex ID: W7125394967