Infinite Horizon Control With Nonlinear Dynamics Models Reproduces Temporal Modulation of Reaching Movements

Abstract. Movement duration, a fundamental aspect of motor control, is often viewed as a preprogrammed parameter requiring dedicated selection mechanisms. An alternative view posits that movement dura...

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biorxiv.org

Fall risk-aware adaptation explains suboptimal locomotor performance

Human locomotion requires balancing multiple biological objectives, such as metabolic energy efficiency, stability, and symmetry. While models based on optimization successfully predict how humans walk in familiar settings, they fail to explain why individuals adopt inefficient movement patterns in novel environments, even after extensive practice. Here, we show that such suboptimality in a novel environment arises from a fundamental prioritization of safety. We find that individuals do not simply fail to reach an optimal solution; instead, they navigate an environment-dependent risk landscape by mitigating the statistical probability of falling. We find that this risk-averse strategy is explained by adjusting internal learning parameters: specifically, the learning rate and the tradeoff between metabolic cost and symmetry, in a manner that lowers fall risk. To quantify this process, we developed an ‘inverse adaptation’ modeling framework; this approach works backwards from locomotor performance data to mathematically infer the underlying internal learning parameters and how they vary with fall risk. Our analysis reveals that the observed motor performance is explained by a global probabilistic fall risk rather than a local step-based measure of instability. Ultimately, these findings reveal that fall risk-aware adaptation explains suboptimal locomotor behavior, providing a new data-driven framework to understand the drivers of motor performance. ### Competing Interest Statement The authors have declared no competing interest.

doi.org

Feedback control of random networks as a model of flexible motor cortical dynamics across tasks

Kalidindi and Crevecoeur develop a computational framework linking feedback-controlled networks to limb dynamics. They demonstrate that optimal control of fixed network reproduces key motor cortical d...

cell.com

Staying stable

Scientists at MIT’s McGovern Institute have determined that animals with very different bodies likely use a shared strategy to balance themselves when they walk.

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Postdoc Position on Systems Neuroscience, Motor Control at UCLouvain, Belgium

Project Title: Multi-disciplinary, multi-lab investigations of the neural bases of human sensorimotor control Project Description:

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Sensory expectations shape neural population dynamics in motor circuits

Nature - Experiments with human volunteers and macaques show that expectations produced by probabilistic cueing of future sensory inputs shape motor circuit dynamics in order to increase the...

nature.com

Compositional neural dynamics during reaching

The complex mechanics of the arm make the neural control of reaching inherently posture dependent. Because previous reaching studies confound reach direction with final posture, it remains unknown how...

biorxiv.org

Interference between flexible and adaptive reaching control

Humans rapidly update the control of an ongoing movement following changes in contextual parameters. This involves adjusting the controller to exploit redundancy in the movement goal, such as when rea...

biorxiv.org

Neural Population Mechanisms for Flexible Sensorimotor Control

Modern large-scale recordings have revealed that motor cortex activity during reaching follows low-dimensional dynamics, thought to reflect sensorimotor computations. However, the origin of these patt...

biorxiv.org

String-pulling by the common marmoset | Journal of Neurophysiology | American Physiological Society

Coordinated hand movements used to grasp and manipulate objects are crucial for many daily activities, such as tying shoelaces or opening jars. Recently, the string-pulling task, which involves cyclic...

journals.physiology.org

A Context-Free Model of Savings in Motor Learning

Learning to adapt voluntary movements to an external perturbation, whether mechanical or visual, is faster during a second encounter than during the first. The mechanisms underlying this phenomenon, k...

biorxiv.org

Terms Are Tools in Biological Motor Control

"Terms Are Tools in Biological Motor Control" published on 23 Dec 2024 by Human Kinetics.

doi.org

Brain-like neural dynamics for behavioral control develop through reinforcement learning

During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produce neural dynamics that enable the rich repertoire of behavi...

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