Combatting nonidentifiability to infer motor cortex inputs yields similar encoding of initial and corrective movements

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 male 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.

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 initial and corrective movements, suggesting shared input encoding across visually-instructed and corrective movements previously obscured by M1 dynamics. Because input inference can suffer from nonidentifiability, where different models fit 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. The authors thank Josh Coles, Zach Haga, Jignesh Joshi, Dawn Paulsen, Jacob Reimer, Aaron Suminski, and Dennis Tkach for originally collecting the data, and Chethan Pandarinath for assistance with LFADS.

This work was funded by R01 NS111982 (NGH), R01NS045853 (NGH) R01 EY022338 (JNM), NSF CAREER Grant 0952686 (JNM), R01-NS094184 (JNM), The Whitehall Foundation (MTK), a Simons Collaboration on the Global Brain Pilot Award (MTK), NIH R01 NS121535 (MTK), the NSF-Simons Institute for Theory and Mathematics in Biology (MTK), The University of Chicago (MTK), the Neuroscience Institute at the University of Chicago (MTK), and T90/R90 T90DA059109 (AV). These authors contributed equally: Peter J. Malonis, Ankit Vishnubhotla.

These authors jointly supervised this work: Jason N. Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA Dept. of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA Nicholas G. Kaufman Dept. of Neurobiology, The University of Chicago, Chicago, IL, USA Neuroscience Institute, The University of Chicago, Chicago, IL, USA NSF-Simons National Institute for Theory and Mathematics in Biology, Chicago, IL, USA Correspondence to Nicholas G.

NGH serves as a consultant for BlackRock Neurotech, Inc., the company that sells the multi-electrode arrays and acquisition system used in this study. The remaining authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.