Force field effects on cerebellar Purkinje cell discharge with implications for internal models

Pasalar, Siavash; Roitman, Alexander; Durfee, William K.; Ebner, Timothy J.

doi:10.1038/nn1783

Cited by 176 publications

(163 citation statements)

References 46 publications

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“…However, evidence is yet to be found. Besides, more recent findings (Pasalar et al, 2006) do not give support to the cerebellum as the site for inverse dynamics of the arm as suggested previously (Kawato, 1999). Our findings suggest that for the motor cortex, the shape of the generalization function arises from the cosine-tuned modulation of different subpopulations of directionally tuned neurons.…”

Section: A Neuronal Basis For Generalization Patterns In Force-field contrasting

confidence: 73%

Combined Adaptiveness of Specific Motor Cortical Ensembles Underlies Learning

Arce¹,

Novick²,

Mandelblat-Cerf³

et al. 2010

J. Neurosci.

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Learning motor skills entails adaptation of neural computations that can generate or modify associations between sensations and actions. Indeed, humans can use different strategies when adapting to dynamic loads depending on available sensory feedback. Here, we examined how neural activity in motor cortex was modified when monkeys made arm reaches to a visual target and locally adapted to curl force field with or without visual trajectory feedback. We found that firing rates of a large subpopulation of cells were consistently modulated depending on the distance of their preferred direction from the learned movement direction. The newly acquired activity followed a cosine-like function, with maximal increase in directions that opposed the perturbing force and decrease in opposite directions. As a result, the combined neuronal activity generated an adapted population vector. The results suggest that this could be achieved without changing the tuning properties of the cells. This population directional signal was however altered in the absence of visual feedback; while the cosine pattern of modulation was maintained, the population distributions of modulated cells differed across feedback consistent with the different trajectory shapes. Finally, we predicted generalization patterns of force-field learning based on the cosine-like modulation. These conformed to reported features of generalization in humans, suggesting that the generalization function was related to the observed rate modulations in the motor cortex. Overall, the findings suggest that the new combined activation of neuronal ensembles could underlie the change in the internal model of movement dynamics in a way that depends on available sensory feedback and chosen strategy.

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Section: A Neuronal Basis For Generalization Patterns In Force-field contrasting

confidence: 73%

Combined Adaptiveness of Specific Motor Cortical Ensembles Underlies Learning

Arce¹,

Novick²,

Mandelblat-Cerf³

et al. 2010

J. Neurosci.

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“…Synchronous complex spike firing of discrete subsets of Purkinje cells is thought to orchestrate coherent muscle contraction with high temporal precision (Welsh et al, 1995;Fukuda et al, 2001). More precisely, complex spikes have been posited to correct for ongoing adjustments of movement velocity during acute motor execution even after a task has been learned (Ebner et al, 2002;Pasalar et al, 2006). Aberrant movement velocity, such as the sudden acceleration seen in human patients with cerebellar pathology, could plausibly cause increased lateral deviation through increased exertion of force when each step is initiated and cause slips on the 1 cm beam.…”

Section: Perturbed Complex Spikes: Potential Impact On Deep Nuclear Nmentioning

confidence: 99%

Purkinje-Cell-Restricted Restoration of Kv3.3 Function Restores Complex Spikes and Rescues Motor Coordination inKcnc3Mutants

Hurlock

McMahon²,

Joho³

2008

J. Neurosci.

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The fast-activating/deactivating voltage-gated potassium channel Kv3.3 (Kcnc3) is expressed in various neuronal cell types involved in motor function, including cerebellar Purkinje cells. Spinocerebellar ataxia type 13 (SCA13) patients carrying dominant-negative mutations in Kcnc3 and Kcnc3-null mutant mice both display motor incoordination, suggested in mice by increased lateral deviation while ambulating and slips on a narrow beam. Motor skill learning, however, is spared. Mice lacking Kcnc3 also exhibit muscle twitches. In addition to broadened spikes, recordings of Kcnc3-null Purkinje cells revealed fewer spikelets in complex spikes and a lower intraburst frequency. Targeted reexpression of Kv3.3 channels exclusively in Purkinje cells in Kcnc3-null mice as well as in mice also heterozygous for Kv3.1 sufficed to restore simple spike brevity along with normal complex spikes and to rescue specifically coordination. Therefore, spike parameters requiring Kv3.3 function in Purkinje cells are involved in the ataxic null phenotype and motor coordination, but not motor learning.Key words: K channels; burst firing; motor dysfunction; cerebellum; Purkinje cell; complex spike IntroductionVoltage-gated potassium (Kv) channels of the Kv3 subfamily enable neurons to fire narrow action potentials at high frequencies beyond ϳ200 Hz. Four separate genes (Kcnc1-Kcnc4 ) encoding subunits Kv3.1-Kv3.4 are expressed in various combinations in CNS neurons in which they assemble into homotetrameric and heterotetrameric channels. Mice lacking the Kcnc3-encoded Kv3.3 subunit exhibit motor deficits manifested as increased lateral deviation while ambulating and increased slips while traversing a narrow beam (McMahon et al., 2004;Joho et al., 2006b). A role for Kv3.3 channels in motor coordination is also underscored by the recent finding that dominant-negative mutations found in the human KCNC3 gene correlate with the neurological disorder spinocerebellar ataxia 13 (Waters et al., 2006). Kv3-type channels are distinguished by exceptionally rapid kinetics of activation and deactivation, rendering them ideally suited to drive repolarization that is rapid, both in its onset and relief, so that the next action potential upstroke can occur with minimal delay in the absence of lingering afterhyperpolarization. High-frequency spiking is facilitated by both minimizing sodium channel inactivation through brief action potentials and by promoting sodium channel deinactivation through the excursion to negative potentials during the fast afterhyperpolarization. Indeed, the loss of Kv3-type channels results in broadened spikes accompanied by decelerated firing (Rudy and McBain, 2001). Unlike most other neuron types in which Kv3.3 subunits are expressed with other Kv3-type subunits, Purkinje cells express high levels of Kv3.3 and lower levels of Kv3.4 (Weiser et al., 1994;Martina et al., 2003). Loss of Kv3.3 results in a doubling of action potential duration (McMahon et al., 2004). Furthermore, blockade with tetraethylammonium (TEA) or BDS-I at a conc...

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“…This internal representation appears to be used for both feedforward (anticipatory) (Lackner and Dizio, 1994;Shadmehr and Mussa-Ivaldi, 1994;Flanagan and Wing, 1997;Krakauer et al, 1999;McIntyre et al, 2001;Singh and Scott, 2003) and feedback (Todorov and Jordan, 2002;Franklin et al, 2007;Kurtzer et al, 2008) control. Although there is some controversy as to whether the internal representation comprises inverse and/or forward models of the task dynamics (Ostry and Feldman, 2003;Pasalar et al, 2006;Yamamoto et al, 2007), it is clear that to ensure stable control when the environmental forces arise from unpredictability or mechanical instability it must also have the capacity to regulate mechanical impedance (Hogan, 1984;Burdet et al, 2001;Franklin et al, 2004Franklin et al, , 2007. Feedforward control of mechanical impedance is necessary in biological systems because neural delays preclude the use of feedback to compensate for instability in the environment (Mehta and Schaal, 2002).…”

Section: Introductionmentioning

confidence: 99%

CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

Franklin¹,

et al. 2008

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We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.

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Force field effects on cerebellar Purkinje cell discharge with implications for internal models

Cited by 176 publications

References 46 publications

Combined Adaptiveness of Specific Motor Cortical Ensembles Underlies Learning

Combined Adaptiveness of Specific Motor Cortical Ensembles Underlies Learning

Purkinje-Cell-Restricted Restoration of Kv3.3 Function Restores Complex Spikes and Rescues Motor Coordination inKcnc3Mutants

CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

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