Computational approaches to motor control.

By Tamar Flash, Terrence J. Sejnowski. Current Opinion in Neurobiology, 2001

617 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/d08fa856b2ad74cdbedb06f83cc4d4c26a2c813b

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Citations in the corpus

Neurophysiological mechanisms underlying the understanding and imitation of action (2001, result): “The idea that any time we generate an action we produce a sensory copy of it is consistent with forward models of different brain function”
eview hat is that little voice inside my head ? Inner speech henomenology , its role in cognitive performance , and ts relation to self-monitoring (2014, methodology): “In this framework, it s claimed that the brain uses forward modelling to predict the conequences of action [134,135].”
Is imitation learning the route to humanoid robots? (1999, background): “and Wolpert, D.M. (1996) Forward models for physiological”
Cognitive Developmental Robotics: A Survey (2009, background): “, [46]) has focused almost exclusively on discrete movements, essentially assuming similar neural circuitry for rhythmic tasks.”
The free energy principle for action and perception: A mathematical review (2017, background): “To evaluate this the brainmust have a inversemodel (Wolpert, 1997) of how sensory data change with action (Friston et al., 2010).”
Action to Language via the Mirror Neuron System: The role of vocal tract gestural action units in understanding the evolution of phonology (2006, background): “Reviews of the use of point-attractor models for skilled movement can be found in Shadmehr (1995), Mussa-Ivaldi (1995), and Flash and Sejnowski (2001). In other words, we view the control of these units of action in speech to be no different from that involved in controlling skilled movements genera”
The elastic phrase: modeling the dynamics of boundary-adjacent lengthening (2003, background): “Point attractor models with varying degrees of complexity have been used to characterize many skilled human movements, for example, reaching (for reviews see, e.g., Shadmehr, 1995; Mussa-Ivaldi, 1995; Flash & Sejnowski, 2001).”
The Sense of Control and the Sense of Agency (2007, background): “According to a very influential theoretical framework, motor control is achieved through the use of internal models (Jordan & Wolpert, 1999; Wolpert et al., 1995; Wolpert & Ghahramani, 2000; Wolpert, 1997; Jeannerod, 1997; Frith et al., 2000).”
Cerebellar premotor output neurons collateralize to innervate the cerebellar cortex (2015, neutral): “…improves movements by computing feedforward signals in which internal feedback, and specifically corollary discharge, is used to guide motor output (Kawato and Gomi, 1992; Miall et al., 1993; Wolpert, 1997; Wolpert et al., 1998; Ohyama et al., 2003; Shadmehr and Krakauer, 2008; Lisberger, 2009).”
Speech Production as State Feedback Control (2011, background): “As Figure 5 indicates, the combination of vocal tract  plus this feedback-based correction process is called an observer (Jacobs, 1993; Stengel, 1994; Wolpert, 1997; Tin and Poon, 2005), which in this case, because it includes allowances for feedback delays, is also a variant of a Smith Predictor (”
What Do Mirror Neurons Contribute to Human Social Cognition (2008, methodology): “…compilation © 2008 Blackwell Publishing Ltd What computational theorists of action ( Blakemore and Decety, 2001; Blakemore et al. , 2000; Miall, 2003; Wolpert, 1997; Wolpert and Ghahramani, 2000; Wolpert et al. , 2001 ) call ‘ internal models ’ of action allow us to make some relevant…”
Towards a dynamic theory of intentions (2009, background): “…illustration, when you move your eye using your eye muscles, the sensory consequences of this movement are anticipated and the displacement 6 See, for instance, Wolpert (1997), Jordan & Wolpert (1999) and Wolpert & Ghahramani (2000) for a review of neurocomputational approaches to motor control.”
Models of human movement: Trajectory planning and inverse kinematics studies (2013, positive): “Thus, specifying a pattern of behavior at any level completely specifies the patterns at the level below it (many-to-one) but the pattern will be consistent with many possible patterns and solutions at the levels above (one-to-many) [1].”
Dynamics systems vs. optimal control--a unifying view. (2007, background): “…formulation is, however, quite suitable for motor control if we conceive of this dynamic system as a kinematic policy that creates kinematic target values (e.g., positions, velocities, accelerations), which subsequently are converted to motor commands by an appropriate controller (Wolpert, 1997).”
Neural Correlates of Reach Errors (2005, positive): “It has been suggested that the computations underlying the control of reaching differentiate between kinematics and dynamics (Atkeson, 1989; Flash and Sejnowski, 2001; Shadmehr and Wise, 2004).”