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:10.7554/elife.67256

Cited by 56 publications

(77 citation statements)

References 112 publications

(215 reference statements)

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“…Perhaps what appear to be “neural dynamics” merely reflect incoming sensory feedback mixed with outgoing commands. A purely feedforward network could convert the former into the latter, and might appear to have rich dynamics simply because the arm does (Kalidindi et al 2021). While plausible, this hypothesis strikes us as unlikely.…”

Section: Discussionmentioning

confidence: 99%

Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

Saxena

Russo

Cunningham

et al. 2021

Preprint

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Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling, and yielded quantitative and qualitative predictions. To evaluate predictions, we recorded motor cortex population activity during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

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Section: Discussionmentioning

confidence: 99%

Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

Saxena

Russo

Cunningham

et al. 2021

Preprint

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“…More generally, the presence of sensory feedback complicates the interpretation of apparent "dynamics" in motor cortex. For example, a recent study demonstrated that neural population activity in the somatosensory cortex (Kalidindi et al, 2021) possesses many features attributed to dynamics in M1, in particular, rotations in neural state space (Russo et al, 2018). For these reasons, it is difficult to say what proportion of the dynamical behavior that we see in M1 is due to its own dynamics, versus dynamics "inherited" from other areas, including sensory input.…”

Section: Motor-potent Spaces Translate Neural Dynamics Into Muscle Activitymentioning

confidence: 96%

“…The brain can adjust feedback gains according to planning and context, thereby altering the nature of the transformation of state estimates into motor output. Most research in this field has been at the level of movement psychophysics (Mazzoni & Krakauer, 2006;Sha et al, 2006;Taylor et al, 2014), only infrequently examining the firing rates of single neurons (Cross et al, 2021;Kalidindi et al, 2021;Pruszynski, 2014;Pruszynski et al, 2011). OFC presents what might be considered an "algorithm-level" description of how the motor system controls movements but is largely agnostic to how this algorithm is implemented by neurons.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, common questions in NDS, such as "how do rotational dynamics contribute to the generation of motor output?" have rarely been posed in the framework of OFC (although see Kalidindi et al, 2021). To bridge this gap, we propose a hybrid model called Dynamical Feedback Control (DFC), that provides a unifying framework for algorithmic and implementation-level descriptions of the sensorimotor system; we propose that the algorithm of optimal feedback control is implemented by the dynamics of motor circuitry.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical Feedback Control: Motor Cortex as an Optimal Feedback Controller Based on Neural Dynamics

Versteeg¹,

Miller²

2022

Preprint

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Primates have an unparalleled ability to produce a wide range of dexterous movements, each sensitive to context and robust to perturbation. Recent progress in understanding the neural basis of movement generation comes from two largely independent ideas, “Optimal Feedback Control” and “Neural Dynamical Systems”. The optimal control framework was largely inspired by research programs from the ‘80s showing that the brain doesn’t seem to plan a simple desired movement trajectory, but instead produces movements by transforming sensory information into motor output that satisfies an optimality criterion. The more recent idea that the motor cortex acts as a dynamical system came about only as it became possible to analyze large numbers of simultaneously recorded single neurons. These two framings of the motor system have been largely incommensurate, neither able to contribute much to the understanding of the other. In this review, we reconcile these two views into a single model we call “Dynamical Feedback Control”. We propose that the dynamics in the motor cortex emerge from a sensorimotor transformation that couples the motor cortex to sensory input from the periphery, and to contextual inputs from other cortical and subcortical areas. Dynamics in motor cortex can be thought to approximate gains of a feedback controller, and by moving the neural state to different regions of state space, the motor system can rapidly alternate between different controllers. The DFC framework presents a new lens to interpret neural dynamics, and to understand how ensembles of neurons generate flexible and responsive patterns of muscle activity.

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“…Indeed, a group of researchers discovered that when they trained a recurrent artificial network to generate the motor activities needed for movement, the patterns resembled the rotational activity seen in the motor cortex ( Sussillo et al, 2015 ). Now, in eLife, Hari Teja Kalidindi (Scuola Superiore Sant'Anna), Kevin P Cross (Queen's University) and colleagues report that it is also possible to train a neural network to control an artificial arm without using any recurrent connections inside the network ( Kalidindi et al, 2021 ).…”

mentioning

confidence: 99%