Combatting nonidentifiability to infer motor cortex inputs yields similar encoding of initial and corrective movements
Year: 2026
Type: article
Abstract: Primary motor cortex (M1) plays a central role in voluntary movement, but how it integrates sensory-driven corrective instructions is unclear. We analyzed population activity recorded from M1 of macaques during a sequential arm movement task with target updates requiring online adjustments to the motor plan. Using Latent Factor Analysis via Dynamical Systems (LFADS), we separated neural activity into two components: intrinsic dynamics and inferred external inputs influencing those dynamics. Inferred input timing was more strongly locked to target appearance than to movement onset, suggesting that variable reaction times reflect interactions between inputs and ongoing dynamics. Inferred inputs were tuned similarly for both initial and corrective movements, suggesting a shared input encoding across visually-instructed and corrective movements that was previously obscured by M1 dynamics. Because input inference can suffer from the challenge of nonidentifiability, where different models fit the data indistinguishably, we used ensembles of models with varied hyperparameters to diagnose when inputs are identifiable or nonidentifiable. In the monkey data, ensembles produced consistently similar results, suggesting that inputs could be meaningfully inferred and that their encoding was not simply a result of model bias. These results highlight the challenges of nonidentifiability and the potential of model ensembles to identify inputs in ongoing dynamics, at least in some cases.
Source: Nature Communications
Institutions: University of Chicago, U.S. National Science Foundation
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Science Foundation, Simons Foundation, Whitehall Foundation, University of Chicago, National Institutes of Health, Simons Institute for the Theory of Computing, University of California Berkeley, National Institute on Drug Abuse, National Eye Institute, National Institute of Neurological Disorders and Stroke
Awards: Coding of Action by Motor & Premotor Cortical Ensembles, NSF-Simons National Institute for Theory and Mathematics in Biology, CAREER: Interdependence of Cortical Circuitry and Emergent Dynamics, Thalamocortical interactions, Functional relationships between visual cortex and thalamus, G6671297155, Coding of Action by Motor & Premotor Cortical Ensembles, G9000011246, Training in Theory and Computation for Next Generation Neuroscientists, Mapping neural ensemble computations to biological circuitry in motor control and decision making - Resubmission - 1
OpenAlex ID: W7214389435