Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning

Perich, Matthew G.; Miller, Lee E.

doi:10.1007/s00221-017-4997-1

Cited by 26 publications

(33 citation statements)

References 59 publications

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“…At the onset of Force, the firing rate patterns of most neurons changed throughout adaptation along with the movements (Figure S1, S2a,b). Similar changes in firing rate have been previously described in the motor cortex during CF adaptation 24,25,28 . With this study, we explored several competing hypotheses, described above, to explain the underlying cause of these diverse adaptation-related changes in single neuron firing within M1 and PMd.…”

Section: Curl Field Adaptation Involves Widespread Complex Changes Isupporting

confidence: 84%

“…Hence our results suggest that functional connectivity changes within PMd or M1 play at most a minimal role on the time scale of a single experimental session. Our lab has previously found that the relation between evolving M1 activity and the dynamics of the motor output remains unchanged during CF adaptation 28 , with no evidence for adaptive changes in spatial tuning having a time course like that of behavioral adaptation 25 . Therefore, we hypothesized that CF adaptation must be mediated by changes in recruitment of M1 by upstream areas, including PMd.…”

Section: Relation Between Short-and Long-term Learningmentioning

confidence: 94%

“…In our experiment, we recorded simultaneously from electrode arrays implanted in both M1 and PMd as macaque monkeys learned to make accurate reaching movements that were perturbed either by "curl field" (CF) forces applied to the hand [23][24][25] , or by a rotation of the visual feedback (VR) 26,27 . We developed an analytical approach based on functional connectivity to understand adaptation-related changes in the activity of M1 and PMd and distinguish the role of the null and potent subspaces within PMd throughout this adaptation process.…”

Section: Possible Mechanisms Underlying Adaptive Neural Activity Chanmentioning

confidence: 99%

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A neural population mechanism for rapid learning

Gallego¹,

Miller²

2017

Preprint

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112

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The brain's ability to adapt behavior is crucial to survival. Long-term learning of dexterous motor skills likely requires plastic changes in cortex 1 , but we can also learn even from errors in single movements 2 . Such trial-to-trial adaptation likely requires a faster mechanism. Here, we show how the brain can adapt behavior by redeploying existing population activity patterns without altering the functional structure of the cortex. We recorded from both primary motor cortex (M1) and dorsal premotor (PMd) cortex in macaque monkeys during motor learning. We trained computational models to predict single neuron spiking based on the activity of the surrounding neural population in order to study the functional relationships between neurons within the two areas 3 . Intriguingly, the functional structure within each area was preserved throughout learning, suggesting that the underlying neural circuitry remained unaltered 4,5 . To study the interaction between the areas, we separated the PMd activity 6 into two sets of components: "potent" components that captured activity that mapped onto M1, and "null" components that captured activity patterns with effects only within PMd 7 . As was true within each area, the activity of the potent components consistently predicted M1 spiking throughout its learning-related changes. In stark contrast, the mapping from the null components gradually changed with learning. These results show that, at a population level, PMd develops new motor plans (reflected in the null components) that are transmitted to M1 through an unchanged functional mapping (captured by the potent components) between the two areas. Use of the PMd-to-M1 null space as a neural scratch pad for the gradual development of new motor plans is a powerful mechanism that may explain a variety of rapid learning processes throughout the brain.In order to make skilled movements, sensory input is combined with internal state variables and transformed into a plan executed by the motor cortices 8 . This transformation may be mediated in part by an "internal inverse model" that maps a desired motor action to the required low-level muscle commands. The dorsal premotor (PMd) and primary motor (M1) cortices, together with the cerebellum, are prime candidate locations for such an inverse model. PMd is involved in movement planning 9 , with diverse inputs and strong connectivity with M1 10 , while M1is the main cortical output to the spinal cord 11 . Correction of movement errors, such as those caused by external perturbations 12 , is thought to lead to alteration of the inverse model, leading to progressively more accurate movements even on a trial-by-trial basis 2 . The rapid speed of these changes seems incompatible with structural changes in synaptic connectivity 13,14 . Furthermore, monkeys using brain-machine interfaces have great difficulty learning mappings between brain activity and cursor movement that require the normal pattern of covariation among recorded cortical neurons to be altered 15 . Given that neural covari...

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Section: Curl Field Adaptation Involves Widespread Complex Changes Isupporting

confidence: 84%

Section: Relation Between Short-and Long-term Learningmentioning

confidence: 94%

Section: Possible Mechanisms Underlying Adaptive Neural Activity Chanmentioning

confidence: 99%

See 1 more Smart Citation

A neural population mechanism for rapid learning

Gallego¹,

Miller²

2017

Preprint

Self Cite

112

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show abstract

“…For example, neural population correlates of motor memory (one consequence of learning) have yet to be identified. Through the lens of single-neuron activity, some studies found "memory" neurons-identified by persistent changes in their tuning properties following a washout period 9,24,25 -while other studies with similar but not identical tasks did not 26,27 . These results suggest that neural activity during learning can change in a way independent of movement parameters though what computational roles this type of changes could serve remains unclear.Here we asked if all changes in neural population dynamics solely reflect the newly learned movements or, alternatively, if there are also dynamics that facilitate learning but are not tightly linked to motor output.…”

mentioning

confidence: 99%

Skill-specific changes in cortical preparatory activity during motor learning

Sun

Golub

et al. 2020

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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...

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“…Several studies have demonstrated that M1’s representation of the contralateral limb remains stable across time for a given behaviour (Scott and Kalaska, 1997; Chestek et al, 2007; Stevenson et al, 2011). M1 also maintains this representation when adapting to a novel environment (Cherian et al, 2013; Yakovenko and Drew, 2015; Perich and Miller, 2017; Perich et al, 2018; Vyas et al, 2018) and when performing various forms of reaching (Gribble and Scott, 2002; Gallego et al, 2018; Lara et al, 2018). In contrast, large changes in the neural representation have been observed across behavoural tasks (Cheney and Fetz, 1980; Muir and Lemon, 1983; Drew et al, 1996).…”

Section: Discussionmentioning

confidence: 99%

Maintained representations of the ipsilateral and contralateral limbs during bimanual control in primary motor cortex

Cross

Heming

Cook

et al. 2020

Preprint

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223/250 words) 3 3 Primary motor cortex (M1) almost exclusively controls the contralateral side of the body. 4However, M1 activity is also modulated during ipsilateral body movements. Previous work has 3 5shown that M1 activity related to the ipsilateral arm is independent of the M1 activity related to 3 6 the contralateral arm. How do these patterns of activity interact when both arms move 3 7 simultaneously? We explored this problem by training two monkeys (male, Macaca mulatta) in a 3 8 postural perturbation task while recording from M1. Loads were applied to one arm at a time 3 9

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Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning

Cited by 26 publications

References 59 publications

A neural population mechanism for rapid learning

A neural population mechanism for rapid learning

Skill-specific changes in cortical preparatory activity during motor learning

Maintained representations of the ipsilateral and contralateral limbs during bimanual control in primary motor cortex

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