Behavior relies on continuous influx of sensory information about the body and the environment. In primates, cortex integrates somatic feedback to accurately reach and manipulate objects. Yet, in many experimental regimes motor cortex seems paradoxically to operate as a feedforward, rather than feedback-driven, system. Here, we recorded simultaneously from motor and somatosensory cortex as monkeys performed a naturalistic reaching and object interaction behavior. We studied how unexpected feedback from behavioral errors influences cortical dynamics. Motor cortex generally exhibited robust feedforward dynamics, yet displayed feedback-driven dynamics surrounding correction of behavioral errors. We then decomposed motor cortical activity into orthogonal subspaces capturing communication with somatosensory cortex or behavior-generating dynamics. During error correction, the communication subspace became feedback-driven, while the behavioral subspace maintained feedforward dynamics. We therefore demonstrate that cortical activity is compartmentalized within distinct subspaces that shape the population dynamics, enabling flexible integration of salient inputs with ongoing activity for robust behavior.
Objective. Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently rich repertoire of behavioral signals. Here, we developed a robotic platform that enables the monitoring of kinematics, interaction forces, and neurophysiological signals during user-defined upper limb tasks for monkeys. Approach. We configured the platform to position instrumented objects in a three-dimensional workspace and provide an interactive dynamic force-field. Main results. We show the relevance of our platform for fundamental and translational studies with three example applications. First, we study the kinematics of natural grasp in response to variable interaction forces. We then show simultaneous and independent encoding of kinematic and forces in single unit intra-cortical recordings from sensorimotor cortical areas. Lastly, we demonstrate the relevance of our platform to develop clinically relevant brain computer interfaces in a kinematically unconstrained motor task. Significance. Our versatile control structure does not depend on the specific robotic arm used and allows for the design and implementation of a variety of tasks that can support both fundamental and translational studies of motor control.
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