Cortical pattern generation during dexterous movement is input-driven

Sauerbrei, Britton; Guo, Jian-Zhong; Cohen, Jack B.; Mischiati, Matteo; Guo, Wendy; Kabra, Mayank; Verma, Nakul; Mensh, Brett D.; Branson, Kristin; Hantman, Adam W.

doi:10.1038/s41586-019-1869-9

Cited by 234 publications

(306 citation statements)

References 62 publications

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“…This ‘condition invariant signal’ (CIS) begins ~150 ms before movement onset 27,28,39,40 and is proposed to reflect a large input that triggers movement execution. Consistent with this, the dominant response component in mouse motor cortex during reaching depends upon thalamic inputs, and silencing those inputs interrupts execution 39,41 . Furthermore, the CIS is highly predictive of the exact moment of reach initiation 27 .…”

Section: Resultssupporting

confidence: 58%

“…Preparatory activity is proposed to seed execution-related activity and thus specify the identity of the executed reach. The transition from preparatory to execution-related activity coincides with a large condition-invariant change in the neural state, proposed to result from a movement-triggering input 27,39–41 . Traditionally it has been challenging to discriminate preparation-related activity from execution-related activity when they occur close in time 31 .…”

Section: Resultsmentioning

confidence: 91%

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Generation of Rapid Sequences by Motor Cortex

Zimnik

Churchland

2020

Preprint

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1 Rapid execution of motor sequences is believed to depend upon the fusing of movement elements into 2 cohesive units that are executed holistically. We sought to determine the contribution of motor cortex 3 activity to this ability. Two monkeys performed highly practiced two-reach sequences, interleaved with 4 matched reaches performed alone or separated by a delay. We partitioned neural population activity 5 into components pertaining to preparation, initiation, and execution. The hypothesis that movement 6 elements fuse makes specific predictions regarding all three forms of activity. We observed none of 7 these predicted effects. Instead, two-reach sequences involved the same set of neural events as 8 individual reaches, but with a remarkable temporal compression: preparation for the second reach 9 occurred as the first was in flight. Thus, at the level of motor cortex, skillfully executing a rapid sequence 10 depends not on fusing elements, but on the ability to perform two computations at the same time. 65first reach and did so without disrupting the first reach. Thus, at the level of motor and premotor cortex, 66 skilled performance depends not on fusing elements, but upon the ability to prepare one element while 67 executing another. 68 Results 69Task and Behavior 70We trained two rhesus macaques (monkeys B and H) to perform a modified delayed-reach task ( Fig. 71 1a,b). All trials began with a randomized (0-1000 ms) instructed delay period. Each trial required the 72 monkey to make either a single reach, two reaches separated by an instructed pause (delayed double-73 reach), or two reaches with no pause between them (compound reach). Target color indicated which 74 should be acquired first. A salient visual cue (the diameter of the colored portion of the first target) 75 indicated whether a pause was required. For monkey H, the pause between delayed double-reaches was 76 always 600 ms. For monkey B it was variable (100, 300, or 600 ms; the last is used for most analyses) and 77 indicated by the visual cue. Thus, all key information -target locations and any instructed pause -was 78 given during the instructed delay. Reach paths are illustrated ( Fig. 1a) for all single reaches, for three 79 compound reaches that began down-and-right, and for three compound reaches that began down-and-80 left (Extended Data Fig. 1 shows paths for all compound reaches). 81Compound reaches were performed briskly; the hand stayed on the first target only briefly before 82 moving to the second target. Median dwell times on the first target were 119 ms (monkey B) and 137 83 ms (monkey H). The median duration for the full two-reach sequence was 561 ms (monkey B) and 645 84 ms (monkey H). This rapid pace resulted from extensive training over months, with each sequence 85 performed tens of thousands of times. This pace is even faster than that reported in other motor 86 sequence tasks performed by non-human primates, where dwell times are typically >200 ms 10,11,29 . 87To enable comparisons at the neural level, every compound r...

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Section: Resultssupporting

confidence: 58%

Section: Resultsmentioning

confidence: 91%

Generation of Rapid Sequences by Motor Cortex

Zimnik

Churchland

2020

Preprint

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“…Our lesion results suggest that motor cortex could potentially act as an autonomous dynamical system during early movement, a question not resolved by previous work in reaching (Churchland et 18 al., 2012) . However, it has recently been shown in mice that the motor commands necessary for dextrous control cannot proceed without continuous input from the thalamus (Sauerbrei et al, 2020) . One possibility is that while motor cortex in primates may be able to act autonomously during early movement after receiving a go signal, activity may shift to being more input driven once sensory feedback related to the movement arrives in motor cortex, a critical question to be tested in future work.…”

Section: Discussionmentioning

confidence: 99%

A modular neural network model of grasp movement generation

Michaels

Schaffelhofer

Agudelo-Toro

et al. 2019

Preprint

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One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and using visual features to generate the required muscle dynamics to grasp objects would explain the neural and computational basis of the grasping circuit. Modular networks with object feature input and sparse inter-module connectivity outperformed other models at explaining neural data and the inter-area relationships present in the biological circuit, despite the absence of neural data during network training. Network dynamics were governed by simple rules, and targeted lesioning of modules produced deficits similar to those observed in lesion studies, providing a potential explanation for how grasping movements are generated.

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“…a system that is not strongly influenced by external feedback inputs) with dynamics that are inherently autonomous or self-driven. However, motor cortex requires constant input during the generation of behavior 16 and its activity is shaped by somatosensory feedback 1 . Why, then, do the motor cortical dynamics appear to be feedforward and not driven by feedback inputs?…”

Section: Introductionmentioning

confidence: 99%

Motor cortical dynamics are shaped by multiple distinct subspaces during naturalistic behavior

Conti

Badi

Bogaard

et al. 2020

Preprint

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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.

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Cortical pattern generation during dexterous movement is input-driven

Cited by 234 publications

References 62 publications

Generation of Rapid Sequences by Motor Cortex

Generation of Rapid Sequences by Motor Cortex

A modular neural network model of grasp movement generation

Motor cortical dynamics are shaped by multiple distinct subspaces during naturalistic behavior

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