Structure and variability of delay activity in premotor cortex

Even-Chen, Nir; Sheffer, Blue; Vyas, Saurabh; Ryu, Stephen I.; Shenoy, Krishna V.

doi:10.1371/journal.pcbi.1006808

Cited by 21 publications

(35 citation statements)

References 53 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the preparatory state does not encode the whole reach trajectory, it does encode important parameters of the upcoming reach, such as distance, direction, speed, etc. (Messier & Kalaska 2000, Even-Chen et al, 2019. The results here and those by Churchland and colleagues point to preparatory activity as also being a central source of variability, rather than signal-dependent noise alone being the key factor.…”

Section: Discussionsupporting

confidence: 63%

“…D: Normalized variance of the preparatory neural state for the small (0.75cm targets, red) and large (2.00cm targets, cyan) targets. The variance in the preparatory state is computed by first taking trial-averaged firing rates for the last 200ms of preparation before the go-cue, performing principal components analysis, and finding the volume of the error ellipse in 3D space, which captures at least 80% of the total variance (similar to as described in Vyas et al, 2018 andEven-Chen et al, 2019). E: Proportion of movement variability explained by preparatory…”

Section: Fig 4 Neural Recordings and Analysis Amentioning

confidence: 99%

See 1 more Smart Citation

High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

Vyas

Shenoy

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 63%

Section: Fig 4 Neural Recordings and Analysis Amentioning

confidence: 99%

High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

Vyas

Shenoy

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We constructed this subspace by regressing preparatory neural activity against initial hand forces (generated in the first 50 ms following movement initiation), because initial hand forces directly reflect the feedforward control of the prepared movement before sensory feedback arrives to motor cortex 32 . In this TDR subspace, before-learning neural states radially organized as a ring according to reach targets (Figure 2a) as expected 33,34 . During learning, preparatory states for the trained target rotated, from trial to trial, evolving towards the preparatory state of its adjacent target opposite to the curl field direction (Figure 2a, top-right inset).…”

mentioning

confidence: 63%

Skill-specific changes in cortical preparatory activity during motor learning

Sun

Golub

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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...

show abstract

“… ( A ) Rhesus monkey reaches in 3D space using his right arm, while neural activity from 192 channels are recorded from two Utah arrays surgically implanted into contralateral dorsal premotor and primary motor cortex. The monkey’s arm position in space controlled the velocity of the cursor on the screen (methods described in Even-Chen et al, 2019 ). ( B ) (Left) the layout of the 40 targets to which the monkey reaches.…”

Section: Resultsmentioning

confidence: 99%

“…The MDs to the large and small targets are 461 ± 234 ms and 661 ± 258 ms. ( D ) Normalized variance of the preparatory neural state for the small (0.75 cm targets, red) and large (2.00 cm targets, cyan) targets. The variance in the preparatory state is computed by first taking trial-averaged firing rates for the last 200 ms of preparation before the go-cue, performing principal components analysis, and finding the volume of the error ellipse in 3D space, which captures at least 80% of the total variance (similar to as described in Vyas et al, 2018 and Even-Chen et al, 2019 ). Note that we do not spike sort or assign spikes to individual neurons ( Wood et al, 2004 ).…”

Section: Resultsmentioning

confidence: 99%

High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff

Borno

Vyas

Shenoy

et al. 2020

eLife

Self Cite

View full text Add to dashboard Cite

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts’ law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of ‘good’ control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.

show abstract

Structure and variability of delay activity in premotor cortex

Cited by 21 publications

References 53 publications

High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

Skill-specific changes in cortical preparatory activity during motor learning

High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff

Contact Info

Product

Resources

About