Robust neuronal dynamics in premotor cortex during motor planning

By Nuo Li, Kayvon Daie, Karel Svoboda, Shaul Druckmann. Nature, 2016

463 citations recorded by the source corpus.

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/8b3256aa1368ed582ea14a41d8461716ccafe13c

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Citations in the corpus

Motor cortex signals for each arm are mixed across hemispheres and neurons yet partitioned within the population response (2019, background): “A final possibility is that motor cortical computations are distributed across both hemispheres (Li et al., 2016).”
The Distributed Nature of Working Memory. (2017, background): “, redundant) side when one side was optogenetically silenced [152].”
Computation Through Neural Population Dynamics (2020, background): “Through these linear approximations we understand Rθ , and through it f. Models that switch between multiple LDSs (SLDS) have a rich history and also may serve as stand-alone models of f (e.g., Linderman et al. 2017, Petreska et al. 2011).”
Motor cortex embeds muscle-like commands in an untangled population response (2018, unknown): “…body of work seeks to understand neural responses in terms of signals that a recurrent or feedback-driven neural network would need to perform the relevant task (Hennequin et al., 2014; Li et al., 2016; Lillicrap and Scott, 2013; Mante et al., 2013; Michaels et al., 2016; Sussillo and Barak, 2013).”
Attractor and integrator networks in the brain (2021, background): “In a highly trained 2-alternative motor response task, ALM neurons exhibit persistent activity over a ⇠ 1s delay period, that evolves towards one of two discrete states that guide action at the end of the delay, Fig.”
Predictive Processing: A Canonical Cortical Computation (2018, unknown): “A number of plausible mechanisms have been proposed for the persistence of neural activity, including strong and selective recurrent excitation between coactive neuronal assemblies within the cortex (Cossell et al., 2015; Li et al., 2016; Perin et al., 2011; Song et al., 2005), or via thalamocortica”