Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

Abstract

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.

Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets

“…The model proposes a tight relationship between initial state and subsequent oculomotor dynamics, and interestingly, mounting evidence demonstrates a similar phenomenon in motor cortex, whereby the initial neural state is predictive of an ensuing arm movement and of the trajectories of the underlying neural signals over time ( Churchland et al, 2006 , 2010 , 2012 ; Afshar et al, 2011 ; Ames et al, 2014 ; Sheahan et al, 2016 ; Stavisky et al, 2017 ; Wang et al, 2018 ). In that case, the dynamics develop within a very high-dimensional space, such that the preparatory activity is only weakly related to specific kinematic parameters ( Churchland et al, 2006 , 2010 ).…”

Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum

“…In current models employed for decoding neuronal activity in the motor system, cells are placed into populations based on their patterns of spikes, for example, via tuning with respect to direction of movement 35 , or covariance of their activity with respect to a behavioral variable 36 . In contrast, our results suggest that in the cerebellum, P-cells form populations that may be difficult to infer based on their patterns of activity (simple spikes), but easy to organize based on their preference for error.…”

Augmenting intracortical brain-machine interface with neurally driven error detectors

“…In contrast, outcome error is a higher-level feedback about the end result of the movement [19,71,99,100]. It can indicate the need for changing the motor output on subsequent movements or updating the internal model of the effector [6,101,102], but has no immediate relevance to ongoing movement generation. Hence, one might have a higher expectation of finding execution and target errors in motor cortex, which itself is concerned with low-level details of muscle movements [103–106], and finding the more abstract outcome error signal to be restricted to motor areas believed to be associated with higher levels of movement control, such as supplementary motor area (SMA) [21,107], anterior cingulate cortex (ACC), basal ganglia [100,108–110] and cerebellum [6].…”