Movement is governed by rotational population dynamics in spinal motor networks

Lindén, Henrik; Petersen, Peter; Vestergaard, Mikkel; Berg, Rune W.

doi:10.1101/2021.08.31.458405

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…These smaller off-ellipse features allow the network to generate non-sinusoidal activity patterns that differ across muscles. This is a natural strategy that allows a simple, stable, elliptical trajectory to generate multiple temporally structured outputs ( Russo et al, 2018 ; Lindén et al, 2021 ). Trajectories are stable only when the speed-instructing input is present; trajectories return to baseline (along with readouts) when that input is extinguished (not shown).…”

Section: Resultsmentioning

confidence: 99%

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

Saxena

Russo

Cunningham

et al. 2022

eLife

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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. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded 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: Resultsmentioning

confidence: 99%

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

Saxena

Russo

Cunningham

et al. 2022

eLife

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Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity

Zhou

Buonomano

2022

Preprint

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Neuromodulators such as dopamine have been shown to modulate short-term synaptic plasticity (STP). Here we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained RNNs that incorporated STP to produce complex motor trajectories-handwritten digits-with different temporal (speed) and spatial (size) scales. The learned dynamics underwent temporal and spatial scaling when higher synaptic release probabilities corresponded to higher speed/size. Neuromodulation of STP enhanced temporal or spatial generalization compared to weight modulation alone. The model accounted for the data of two experimental studies involving flexible sensorimotor timing. Our results address a long-standing debate regarding the role of dopamine in timing and predict novel mechanisms by which dopamine may slow down neural dynamics and thus slow 'clock' speed.

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Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

Durstewitz

Koppe

Thurm

2022

Preprint

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Mechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be trained directly on the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with the same temporal and geometrical properties as those observed. This is called dynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes a surrogate for the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.

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Movement is governed by rotational population dynamics in spinal motor networks

Cited by 3 publications

References 40 publications

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

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

Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity

Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

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