NEURAL MANIFOLDS FOR THE CONTROL OF MOVEMENT
By Juan Alvaro Gallego, M. Perich, Lee E. Miller, Sara A. Solla. 2017
655 citations recorded by the source corpus.
The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the “neural modes.” We discuss a model for neural control of movement in which the timedependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.
Subfields: Cognitive Neuroscience
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In Cognitive Neuroscience
- Srdjan Ostojic
- Mehrdad Jazayeri
- Krishna V. Shenoy
- Mikail Khona
- Saurabh Vyas
- Matthew D. Golub
- David Sussillo
- Ila Rani Fiete
Citations in the corpus
- Attractor and integrator networks in the brain (2021, background): “[252] Daniel W Moran and Andrew B Schwartz.”
- Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity (2021, background): “Electrophysiology experiments and neural network modeling suggest that the dynamics of population activity in the motor cortex provide a potential basis set for outgoing muscle-like commands [7,31–35].”
- Sequential and efficient neural-population coding of complex task information (2019, neutral): “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, ”
- Computation Through Neural Population Dynamics (2020, background): “A number of neuroscientific studies have derived insights into neural systems by leveraging manifold structures identified from neurophysiological data (e.g., Chaudhuri et al. 2019, Kim et al. 2017, Sadtler et al. 2014; for a review, see Gallego et al. 2017).”
- Speech synthesis from neural decoding of spoken sentences (2019, methodology): “Another aspect that may underlie this difference is that articulatory kinematics lie on a low-dimensional manifold that constrain the potential high-dimensionality of acoustic signals (Extended Data Figure 5) 6,7,23 .”