Combatting nonidentifiability to infer motor cortex inputs yields similar encoding of initial and corrective movements
8 affiliations
- 1Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA
- 2Dept. of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA. [email protected]
- 3Dept. of Neurobiology, The University of Chicago, Chicago, IL, USA. [email protected]
- 4NSF-Simons National Institute for Theory and Mathematics in Biology, Chicago, IL, USA. [email protected]
- 5Neuroscience Institute, The University of Chicago, Chicago, IL, USA. [email protected]
- 6Dept. of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA. [email protected]
- 7NSF-Simons National Institute for Theory and Mathematics in Biology, Chicago, IL, USA. [email protected]
- 8Neuroscience Institute, The University of Chicago, Chicago, IL, USA. [email protected]
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.
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BibTeX
@article{malonis2026combatting,
title = {{Combatting nonidentifiability to infer motor cortex inputs yields similar encoding of initial and corrective movements}},
author = {Peter Malonis and Ankit Vishnubhotla and Nicholas G. Hatsopoulos and Jason N. MacLean and Matthew Tyler Kaufman},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77911-4},
url = {https://doi.org/10.1038/s41467-026-77911-4},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Combatting nonidentifiability to infer motor cortex inputs yields similar encoding of initial and corrective movements
AU - Peter Malonis
AU - Ankit Vishnubhotla
AU - Nicholas G. Hatsopoulos
AU - Jason N. MacLean
AU - Matthew Tyler Kaufman
PY - 2026
DA - 2026/09/25
JO - Nature Communications
DO - 10.1038/s41467-026-77911-4
UR - https://doi.org/10.1038/s41467-026-77911-4
AB - 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.
SN - 2041-1723
ER -