Rotational dynamics in motor cortex are consistent with a feedback controller

Kalidindi, Hari Teja; Cross, Kevin P.; Lillicrap, Timothy P.; Omrani, Mohsen; Falotico, Egidio; Sabes, Philip N.; Scott, Stephen H.

doi:10.1101/2020.11.17.387043

Cited by 4 publications

(4 citation statements)

References 104 publications

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“…The engineering term for this is rotational dynamics. Such activity has been found in primate motor cortex and it is produced by artificial neural networks trained to perform similar tasks; for recent results and review see ( Kalidindi et al, 2020 ).…”

Section: Good-enough Programs Vs Optimal Control Of Synergiesmentioning

confidence: 97%

Learning to Use Muscles

Loeb

2021

Journal of Human Kinetics

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The human musculoskeletal system is highly complex mechanically. Its neural control must deal successfully with this complexity to perform the diverse, efficient, robust and usually graceful behaviors of which humans are capable. Most of those behaviors might be performed by many different subsets of its myriad possible states, so how does the nervous system decide which subset to use? One solution that has received much attention over the past 50 years would be for the nervous system to be fundamentally limited in the patterns of muscle activation that it can access, a concept known as muscle synergies or movement primitives. Another solution, based on engineering control methodology, is for the nervous system to compute the single optimal pattern of muscle activation for each task according to a cost function. This review points out why neither appears to be the solution used by humans. There is a third solution that is based on trial-and-error learning, recall and interpolation of sensorimotor programs that are good-enough rather than limited or optimal. The solution set acquired by an individual during the protracted development of motor skills starting in infancy forms the basis of motor habits, which are inherently low-dimensional. Such habits give rise to muscle usage patterns that are consistent with synergies but do not reflect fundamental limitations of the nervous system and can be shaped by training or disability. This habit-based strategy provides a robust substrate for the control of new musculoskeletal structures during evolution as well as for efficient learning, athletic training and rehabilitation therapy.

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Section: Good-enough Programs Vs Optimal Control Of Synergiesmentioning

confidence: 97%

Learning to Use Muscles

Loeb

2021

Journal of Human Kinetics

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“…Primary motor cortex (M1) plays an important role in generating goal-directed corrections during motor actions. M1 receives rich sensory inputs from many brain regions involved in proprioceptive and visual processing including the parietal and frontal cortices (Jones et al, 1978; Zarzecki and Strick, 1978; Crammond and Kalaska, 1989; Porter and Lemon, 1993; Buneo et al, 2002; Pesaran et al, 2006; McGuire and Sabes, 2011; Bremner and Andersen, 2012; Dea et al, 2016; Omrani et al, 2016; Gamberini et al, 2017; Piserchia et al, 2017; Kalidindi et al, 2020; Takei et al, 2021), as well as input from cerebellum (Conrad et al, 1975; Vilis et al, 1976; Strick, 1983; Guo et al, 2020; Sauerbrei et al, 2020). M1 rapidly responds to proprioceptive feedback of the limb within ∼20-40ms of an applied mechanical load (Evarts and Tanji, 1976; Wolpaw, 1980; Lemon, 1981a; Suminski et al, 2009; Pruszynski et al, 2011, 2014; Omrani et al, 2014; Heming et al, 2019) and to visual feedback of the limb and goal within ∼70ms (Georgopoulos et al, 1983; Cisek and Kalaska, 2005; Ames et al, 2014; Stavisky et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Convergence of proprioceptive and visual feedback on neurons in primary motor cortex

Cross

Scott

2021

Preprint

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An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioural goal. Primary motor cortex (M1) is a key region involved in feedback processing, yet we know little about how different sources of feedback are processed by M1. We examined feedback-related activity in M1 to compare how different sources (visual versus proprioceptive) and types of information (limb versus goal) are represented. We found sensory feedback had a broad influence on M1 activity with ~73% of neurons responding to at least one of the feedback sources. Information was also organized such that limb and goal feedback targeted the same neurons and evoked similar responses at the single-neuron and population levels indicating a strong convergence of feedback sources in M1.

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“…Our model consistently underestimated the changes in trial-averaged activity observed during VR adaptation, despite closely matching the small covariance changes (Figure 2,4). This is to be expected, as the model only captures changes due to the motor adaptation process itself, whereas the actual neural activity contains signals related to other processes such as impulsivity/engagement [Cowley et al, 2020, Hennig et al, 2021 and feedback processing [Omrani et al, 2016, Stavisky et al, 2017, Kalidindi et al, 2020, Perich et al, 2020, Cross et al, 2021. In fact, the experimentally observed neural activity changes between the early and late trials of control reaching sessions with no perturbation were almost as large as the changes during adaptation in our model (Figure 2C black dots).…”

Section: Discussionmentioning

confidence: 99%

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

Feulner

Chowdhury

et al. 2021

Preprint

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Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

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Rotational dynamics in motor cortex are consistent with a feedback controller

Cited by 4 publications

References 104 publications

Learning to Use Muscles

Learning to Use Muscles

Convergence of proprioceptive and visual feedback on neurons in primary motor cortex

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

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