Almani, M. N. | Search bioRxiv

bioRxiv2025

Optimal feedback solutions recapitulate key features of motorcortical population dynamics

Neural populations display complex response patterns with marked transitions between distinct underlying computational strategies on very short timescales during motor tasks. Such complex-yet-structured dynamical strategies may reflect computational needs of neural systems, shaped by optimal feedback and autonomous mechanisms, in addition to biological constraints. Are there overarching computational principles that govern complex dynamical strategies exhibited by the neural population response? Here, we explore the hypothesis that computational strategies underlying neural population response represent optimal feedback solutions to the control of musculoskeletal dynamics through space for a goal. To validate this hypothesis, we develop a procedure called neural optimization using dynamical systems (NODS) learning to modify synaptic strengths within a recurrent network for locally-optimal feedback control of anatomically accurate musculoskeletal models during complex sensorimotor tasks. NODS learning works even when the objective function to be minimized is highly non-linear or the muscle model is very complex. The dynamical strategies underlying the neural network response constructed using NODS learning recapitulate key features of recorded population response. Importantly, optimal feedback solutions using NODS learning suggest that feedback mechanisms are essential for neural populations to flexibly transit between complex-yet-structured strategies. We further show that this framework provides theoretical foundations for why the solutions obtained using deep reinforcement learning algorithms extensively used to model sensorimotor tasks may explain the dynamical strategies underlying recorded population response. In summary, we develop novel methods and approaches suggesting that neural dynamics may be more strongly modulated by optimal feedback mechanisms, in addition to autonomous mechanisms, than previously appreciated.

neuroscience↗

bioRxiv2024

μSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling

How does the motor cortex (MC) produce purposeful and generalizable movements with the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate a model of MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory feedback from biologically accurate musculoskeletal models. Given this real-time simulated feedback implemented in advanced physics simulation engines, we use deep reinforcement learning to train the RNN to execute desired movements under specified neural and musculoskeletal constraints. For general use, we provide a modular computational framework that allows the flexible integration of user-defined musculoskeletal models, training algorithms, tasks and constraints. We also provide a combination of modules to analyze and quantify the dynamical alignment and similarity of the trained RNN with the recorded neural data on the population and single-unit level. Using these modules, we find that the activity of the trained RNN can accurately decode experimentally recorded neural population dynamics and single-unit MC activity, while generalizing well to testing conditions significantly different from training. Finally, we also provide perturbation modules to generate insights about neural dynamics for perturbed conditions different from training, and show that this framework unveils computational principles of how such neural dynamics enable flexible control of movement.

neuroscience↗