Flexible neural control of motor units

Marshall, Najja J.; Glaser, Joshua I.; Trautmann, Eric M.; Amematsro, Elom A.; Perkins, Sean M.; Shadlen, Michael N.; Abbott, L. F.; Cunningham, John P.; Churchland, Mark M.

doi:10.1101/2021.05.05.442653

Cited by 17 publications

(23 citation statements)

References 129 publications

(291 reference statements)

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“…This model fits the data much more closely than previous dynamical systems models. These findings also agree with previous suggestions that motor cortex is higher dimensional than previously thought 43 , and with hints that the rotational plane may vary 15,44 . This view appears to challenge notions of fixed output dimensions to the muscles 20 .…”

Section: Discussionsupporting

confidence: 92%

Reach-dependent reorientation of rotational dynamics in motor cortex

Sabatini

Kaufman

2021

Preprint

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Controlling arm movements requires complex, time-varying patterns of muscle activity. Accordingly, the responses of neurons in motor cortex are complex, time-varying, and heterogeneous during reaching. When examined at the population level, patterns of neural activity evolve over time according to dynamical rules. During reaching, these rules have been argued to be "rotational" or variants thereof, containing coordinated oscillations in the spike rates of individual neurons. While these models capture key aspects of the neural responses, they fail to capture others -- accounting for only 20-50% of the neural response variance. Here, we consider a broader class of dynamical models. We find that motor cortex dynamics take an unexpected form: there were 3-4 rotations at fixed frequencies in M1 and PMd explaining more than 90% of neural responses, but these rotations occurred in different portions of state space when movements differ. These rotations appear to reflect a curved manifold of fixed points in state space, around which dynamics are locally rotational. These fixed-frequency rotations obeyed a simple relationship with movement: the orientation of rotations in motor cortex activity were related almost linearly to the movement the animal made, allowing linear decoding of reach kinematic time-courses on single trials. This model constitutes a fundamentally novel way to consider pattern generation: like placing a record player in a large bowl, the frequency of activity is fixed, but the location of motor cortex activity on a curved manifold sets the orientation of locally-rotational dynamics. This system simplifies motor control, helps reconcile conflicting frameworks for interpreting motor cortex, and enables greatly improved neural decoding.

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Section: Discussionsupporting

confidence: 92%

Reach-dependent reorientation of rotational dynamics in motor cortex

Sabatini

Kaufman

2021

Preprint

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“…Such variations in force directions ( ter Haar Romeny et al, 1984 ; Desnedt and Gidaux, 1981 ) but also other motor behavioural changes, including alternations in postures ( Nardone et al, 1989 ), and contraction speed ( Desmedt and Godaux, 1977a ), potentially have an impact on the recruitment order. Similar changes in an MU pool’s discharge activity imposed by such behavioural changes were recently suggested in non-human primates ( Marshall et al, 2021 ). Therefore, individual MU control may be triggered by small compensatory movements rather than being the result of a dedicated and volitionally controllable individual synaptic inputs.…”

Section: Discussionsupporting

confidence: 77%

“…During a progressive increase in force, recruitment depends only on the MU anatomy and the input received. If humans can learn to leverage potential structures in the CNS that allow selective inputs to MUs ( Marshall et al, 2021 ), changes in the recruitment order during this initial phase should be expected. However, it is important to underline that a conclusion of flexible control based on changes in MU recruitment cannot be drawn for time intervals that follow an activation of the MU.…”

Section: Discussionmentioning

confidence: 99%

“…Previous works have tried to challenge the perspective of single MUs only being activated in a predetermined fashion ( Formento et al, 2021 ; Basmajian, 1963 ). A recent study even provided evidence that indeed there is, to some degree, a neural substrate that would allow for the selective cortical control of MUs via descending pathways ( Marshall et al, 2021 ). However, so far, it is unclear whether humans can learn to leverage such a potential neural structure for selectively activating MUs by converting the common neural input received by an MU pool to independent inputs to individual MUs and thus change the original MU recruitment.…”

Section: Introductionmentioning

confidence: 99%

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The control and training of single motor units in isometric tasks are constrained by a common input signal

Bräcklein

Barsakcioglu

Ibáñez

et al. 2022

eLife

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Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces could provide a promising basis for human motor augmentation by extracting potentially high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurons to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs during the onset of neural activity. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect varying inputs or modulations in the neuron's intrinsic excitability.

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“…Moreover, recent results indicate that subjects may learn to change the motor unit recruitment order, thus, enabling 2D cursor control with three motor units from the same muscle [73]. However, subjects were instructed to perform multiple natural DoF movements to alter recruitment, which is a known way to change the recruitment order [74].…”

Section: True Augmentationmentioning

confidence: 99%

Human movement augmentation and how to make it a reality

Eden¹,

Bräcklein²,

Pereda³

et al. 2021

Preprint

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Augmenting the body with artificial limbs controlled concurrently to the natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this vision possible. By allowing individuals to achieve otherwise impossible actions, this movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with their environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning, and propose a vision for the field.

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Flexible neural control of motor units

Cited by 17 publications

References 129 publications

Reach-dependent reorientation of rotational dynamics in motor cortex

Reach-dependent reorientation of rotational dynamics in motor cortex

The control and training of single motor units in isometric tasks are constrained by a common input signal

Human movement augmentation and how to make it a reality

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