Optimal feedback control and the neural basis of volitional motor control

By Stephen H. Scott. 2004

1,109 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Neuroscience

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “Higher-level “dorsal” pathways control the lowest pathways by processing visual/auditory information used to interact with the environment (Scott, 2004).”
Bayesian decision theory in sensorimotor control. (2006, neutral): “The brain is the substrate that is being used to support Bayesian inference and optimal control [ 57 ,58].”
Structure learning in action (2010, background): “Such parametric control laws can be used to study movement generation within the framework of optimal feedback control, and have been successful in explaining a wide range of human motor behaviour [55–61].”
Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface (2005, background): “Our finding that after learning to control an artificial actuator using a BMI, neuronal ensembles represented the actuator velocity better than the velocity of the animal’s own arm appears to support the theory of optimal feedback control (Todorov and Jordan, 2002; Scott, 2004).”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, positive): “We have reported here on the stable latent dynamics in three cortical areas that are closely tied to the resulting behavior 28 .”
Coding and use of tactile signals from the fingertips in object manipulation tasks (2009, result): “Furthermore, the idea that control is centred on distinct movement goals is consistent with a recent computational model of sensorimotor control referred to as optimal feedback contro”
Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics (2018, positive): “For example, neural activity during a reaching movement is likely governed by both local recurrent interactions and distal inputs from time-varying and condition-dependent reafferent signals (Todorov and Jordan, 2002; Scott, 2004; Pruszynski et al., 2011).”
Neuromechanics of muscle synergies for posture and movement. (2007, background): “Muscle synergies may therefore represent the bottom of a hierarchal neural control structure in which higher neural centers operate on increasingly conceptual variables related to task-level motor performance [10–14].”
Navigating Into the Future or Driven by the Past (2013, background): “Empirically, errorbased learning of this kind can be seen, for example, in the optimization of eye motion (Soetedjo, Koyima, & Fuchs, 2008) and muscular-skeletal motion (Scott, 2004; Todorov & Jordan, 2002).”
If MaxEnt RL is the Answer, What is the Question? (2019, background): “While standard reinforcement learning is often used as a model for decision decision making [29, 44, 53], many animals, including humans, do not consistently make decisions that maximize expected utility.”
Cortical control of arm movements: a dynamical systems perspective. (2013, background): “Do they reflect sophisticated mechanisms of online control and feedback (e.g., Scott 2004, Todorov & Jordan 2002)?”
Neural population dynamics during reaching (2012, neutral): “4) there exist non-rotational components as well, perhaps reflecting the nonlinear dynamics necessary for initiating/terminating movement, for stability 37 , and for feedback control 16,38 .”
Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control (2007, result): “…(Todorov and Jordan, 2002a,b; Todorov, 2004, 2005; Todorov et al., 2005) and others (Meyer et al., 1988; Loeb et al., 1990; Hoff, 1992; Kuo, 1995; Scott, 2004) have pursued affords the flexibility apparent in behavioral data, in contrast with more traditional theories (Flash and Hogan, 1985; Uno…”
Patterned control of human locomotion (2012, background): “The organization of neural pathways in primates does permit the selective activation of single muscles, motor units or neurons, and there is evidence that the CNS learns to control individuated degrees of freedom with training (Fetz, 2007; Kutch et al. 2008).”