Animals have a remarkable capacity to learn new motor skills, but it remains an open question as to how learning changes neural population dynamics underlying movement 1 . Specifically, we asked whether changes in neural population dynamics relate purely to newly learned movements or if additional patterns are generated that facilitate learning without matching motor output. We trained rhesus monkeys to learn a curl force field 2 task that elicited new arm-movement kinetics for some but not all reach directions 3,4 . We found that along certain neural dimensions, preparatory activity in motor cortex reassociated existing activity patterns with new movements. These systematic changes were observed only for learning-altered reaches. Surprisingly, we also found prominent shifts of preparatory activity along a nearly orthogonal neural dimension. These changes in preparatory activity were observed uniformly for all reaches including those unaltered by learning. This uniform shift during learning implies formation of new neural activity patterns, which was not observed in other short-term learning contexts 5-8 . During a washout period when the curl field was removed, movement kinetics gradually reverted, but the learning-induced uniform shift of preparatory activity persisted and a second, orthogonal uniform shift occurred. This persistent shift may retain a motor memory of the learned field 9-11 , consistent with faster relearning of the same curl field observed behaviorally and neurally. When multiple different curl fields were learned sequentially, we found distinct uniform shifts, each reflecting the identity of the field applied and potentially separating the associated motor memories 12,13 . The neural geometry of these shifts in preparatory activity could serve to organize skill-specific changes in movement production, facilitating the acquisition and retention of a broad motor repertoire. Motor learning encompasses a wide range of phenomena, from relatively low-level mechanisms for calibrating movement parameters, to making high-level cognitive decisions about how to act in a novel environment 1 . Motor adaptation has been a long-standing and widely used paradigm for studying motor learning. Decades of behavioral studies have demonstrated many intriguing phenomena during motor adaptation, such as the error-driven calibration of movements, generalization of learned skills to a new context, savings (faster relearning) or memory retention, and interference between learning multiple skills 3,4,12,[14][15][16][17][18] . Yet their neural mechanisms, in particular the underlying neural population dynamics, remain largely unknown.In the field of motor control, neural population dynamics have provided foundational insight into activity patterns and computational principles not readily apparent at single-neuron resolution 19,20 . Recently, a dynamical system framework has started to help elucidate the neural basis of motor learning 5,8,21-23 . Collectively, these experiments have observed changes in neural population st...
The speed-accuracy tradeoff is a fundamental aspect of goal-directed motor behavior, empirically formalized by Fitts' law, which relates the movement duration to the movement amplitude and the width of the target. We introduce a computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements and is more biomechanically realistic than previous models. Simulations using this model revealed that the asymmetry in the velocity profile with movement speed can be explained with optimal control theory. Our model provides evidence at both the behavior and neural levels that Fitts' law arises not only from execution noise (as is commonly believed), but also as a consequence of motor planning variability. Significance StatementA long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. To study the relationship, we introduce a computational model of reaching movements based on optimal control theory using a realistic model of musculoskeletal dynamics. The model synthesizes three-dimensional point-topoint reaching movements that reproduce kinematics features reported in motor control studies and in our experimental kinematic data. Such high-fidelity modeling reveals that the speedaccuracy tradeoff as described by Fitts' law emerges even without the presence of motor noise, as is commonly held. This suggests an alternative theory based on suboptimal control solutions. The crux of this theory is that some features of human movement are attributable to planning variability rather than execution noise.
Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-andclick control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight during use, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial performed on-screen item selection tasks to assess iBCI-enabled cursor control. Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to highperformance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment. 1
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