Cortical control of arm movements: a dynamical systems perspective.

By Krishna V. Shenoy, Maneesh Sahani, Mark M. Churchland. Annual Review of Neuroscience, 2013

810 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/504deefd8a93c789ad8036b945c497574d0ec2d1

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Neural population coding: combining insights from microscopic and mass signals (2015, result): “The above results are part of an emerging picture that a small-dimensional subspace of the experimentally measured activity sufﬁces to explain the population dynamics underling sensory processing [23] and motor behavior [24].”
Sequential and efficient neural-population coding of complex task information (2019, positive): “In many experiments, ours included (Figure 1A), the observed neural states appeared confined to a lower-dimensional region of the neural state space, termed the ‘‘neural manifold’’ (Aksay et al., 2003; Gallego et al., 2017; Ganguli and Sompolinsky, 2012; Hopfield and Tank, 1986; Jazayeri and Afraz, ”
Motor primitives in space and time via targeted gain modulation in cortical networks (2018, neutral): “This perspective has been effective for investigations of several cortical regions [5, 37, 7, 36, 47, 4, 48].”
Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, positive): “This is consistent with other planning tasks, in which premotor cortex activity precedes upcoming movements (Li et al., 2015; Shenoy et al., 2013).”
Closed-loop and activity-guided optogenetic control. (2015, positive): “…Pfau et al., 2013) are desirable for their consistency with current models of low-rank state evolution and common-input properties in observed brain dynamics (Shenoy et al., 2013; Mante et al., 2013; Kaufman et al., 2014; Churchland et al., 2012; Harvey et al., 2012; Yu et al., 2006; Sahani, 1999).”
Computation Through Neural Population Dynamics (2020, methodology): “Sohn et al. (2019) used a time-interval reproduction task and found evidence for Bayesian integration in neural circuits.”
Motor cortex embeds muscle-like commands in an untangled population response (2018, neutral): “Alternatively, it has been proposed that non-muscle-like response features reflect network or feedback dynamics (Churchland and Cunningham, 2014; Churchland et al., 2012; Kaufman et al., 2016; Lillicrap and Scott, 2013; Maier et al., 2005; Michaels et al., 2016; Rokni and Sompolinsky, 2012; Seely et”
Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics (2018, background): “This viewpoint has a strong basis in current theories of motor control that posit an implicit representation of kinematic information in motor cortical activity during movements (Churchland et al. 2010; Churchland et al. 2012; Chaisangmongkon et al. 2017; Fetz 1992; Shenoy et al. 2013; Michaels et a”
Machine Learning for Neural Decoding (2017, background): “It is also intriguing that the feedforward neural network did almost as well as the LSTM and better than the standard RNN, considering the recent attention to treating the brain as a dynamical system [47].”
Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning (2015, background): “Another neocortical population burst event with similarities to SPWs is the “rotational waves” recently described in the motor cortex of monkeys during movement preparation (Churchland et al., 2012; Shenoy et al., 2013).”
Dimensionality reduction for large-scale neural recordings (2014, background): “Population structure hypotheses have been actively pursued in several different systems, including the prefrontal cortex 4,8,15,16,40 , the motor system 5,11,41–43 and the olfactory system 6,13,14,28,44–48 .”