Distributed Representation of Limb Motor Programs in Arrays of Adjustable Pattern Generators

Berthier, Neil E.; Singh, Sunaina; Barto, Andrew G.; Houk, James C.

doi:10.1162/jocn.1993.5.1.56

Cited by 124 publications

(55 citation statements)

References 56 publications

(70 reference statements)

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“…Adjustable pattern generator (APG) models (Berthier et al 1993;Fagg et al 1997): We do not use the positive feedback loops to build up nuclear activity, thus do not require the bistable PCs to shut down the activity. Instead, we learn a continuous, real-valued output in the cerebellar cortex and assume that MF activity, combined with a natural high baseline ®ring rate will provide the NC activity.…”

Section: Resultsmentioning

confidence: 99%

Cerebellar learning of accurate predictive control for fast-reaching movements

Spoelstra

Schweighofer

Arbib

2000

Biological Cybernetics

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Abstract. Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between e erent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.

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Section: Resultsmentioning

confidence: 99%

Cerebellar learning of accurate predictive control for fast-reaching movements

Spoelstra

Schweighofer

Arbib

2000

Biological Cybernetics

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“…Whatever the mechanism, the crucial supposition is that the neural pattern of a motor skill ["motor engram" (17)] is stored somewhere in toto so that, when activated, it unfolds in time as a skilled motor act. Since movements are the result of interactions among neurons in the brain, it is reasonable to hypothesize that the motor engram could be stored in the set ofconnections and synaptic strengths between interacting neurons within and among various sensorimotor areas (18). This distributed representation ofthe motor skill could account for the elusiveness of the nature and the site of its motor engram (17).…”

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confidence: 99%

Overlapping neural networks for multiple motor engrams.

Lukashin

Wilcox

Georgopoulos

1994

Proc. Natl. Acad. Sci. U.S.A.

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The hypothesis was tested that learned movement trajectories of different shapes can be stored in, and generated by, largely overlapping neural networks. Indeed, it was possible to train a massively interconnected neural network to generate different shapes of internally stored, dynamically evolving movement trajectories using a general-purpose core part, common to all networks, and a special-purpose part, specific for a particular trajectory. The weights of connections between the core units do not carry any information about trajectories. The core network alone could generate externally instructed trajectories but not internally stored ones, for which both the core and the trajectory-specific part were needed. All information about the movements is stored in the weights of connections between the core part and the specialized units and between the specialized units themselves. Due to these connections the core part reveals specific dynamical behavior for a particular trajectory and, as the result, discriminates different tasks. The percentage of trajectory-specific units needed to generate a certain trajectory was small (2-5%), and the total output of the network is almost entirely provided by the core part, whereas the role of the small specialized parts is to drive the dynamical behavior. These results suggest an efficient and effective mechanism for storing learned motor patterns i, and reproducing them by, overlapping neural networks and are in accord with neurophysiological findings of trajectory-specific cells and with neurological observations ofloss ofspecific motor skills in the presence of otherwise intact motor control.Although a wealth of knowledge has accumulated concerning the neural mechanisms of visually guided reaching (1-7) and tracing (8) movements, and the design and performance of artificial neural networks for similar movements (9-14), our knowledge is largely unknown concerning the generation and performance from memory of explicitly defined, learned movement trajectories, such as drawing a circle. Certain brain lesions can result in apparently specific loss of particular motor skills ["apraxia" (15, 16)], such as dressing or buttoning a garment, without affecting other motor skills (e.g., driving a car) or simple movements (e.g., reaching to a target). It is generally assumed that information concerning the performance of the lost motor skill is stored in the lesioned areas (commonly in the posterior parietal cortex) or that these areas are unique in triggering the appropriate motor action, the motor pattern of which is stored elsewhere. Whatever the mechanism, the crucial supposition is that the neural pattern of a motor skill ["motor engram" (17)] is stored somewhere in toto so that, when activated, it unfolds in time as a skilled motor act. Since movements are the result of interactions among neurons in the brain, it is reasonable to hypothesize that the motor engram could be stored in the set ofconnections and synaptic strengths between interacting neurons within and among v...

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“…The essential features of cortical-cerebellar signal processing were enunciated by Berthier et al [5] in their adjustable pattern generator (APG) model of the CB. According to the APG model, motor programs are stored mainly in the cerebellar cortex in the weights of parallel fiber synapses onto Purkinje cells (PCs).…”

Section: Introductionmentioning

confidence: 99%

“…Because PCs were modeled as bistable devices, motor programs were initially executed in a feedforward manner. Berthier et al proposed that PCs detect when the endpoint of the movement is about to be achieved, whereupon they switch to activated states that inhibit positive feedback and thus terminate motor program execution [5].…”

Section: Introductionmentioning

confidence: 99%

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Reciprocity between the cerebellum and the cerebral cortex: Nonlinear dynamics in microscopic modules for generating voluntary motor commands

Wang

Dam

Yayilgan³

et al. 2008

Complexity

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The cerebellum and basal ganglia are reciprocally connected with the cerebral cortex, forming many loops that function as distributed processing modules. Here we present a detailed model of one microscopic loop between the motor cortex and the cerebellum, and we show how small arrays of these microscopic loops (CB modules) can be used to generate biologically plausible motor commands for controlling movement. A fundamental feature of CB modules is the presence of positive feedback loops between the cerebellar nucleus and the motor cortex. We use nonlinear dynamics to model one microscopic loop and to investigate its bistable properties. Simulations demonstrate an ability to program a motor command well in advance of command generation and an ability to vary command duration. However, control of command intensity is minimal, which could interfere with the control of movement velocity. To assess these hypotheses, we use a minimal nonlinear model of the neuromuscular (NM) system that translates motor commands into actual movements. Simulations of the combined CB-NM modular model indicate that movement duration is readily controlled, whereas velocity is poorly controlled. We then explore how an array of eight CB-NM modules can be used to control the direction and endpoint of a planar movement. In actuality, thousands of such microscopic loops function together as an array of adjustable pattern generators for programming and regulating the composite motor commands that control limb movements. We discuss the biological plausibility and limitations of the model. We also discuss ways in which an agent-based representation can take advantage of the modularity in order to model this complex system.

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Distributed Representation of Limb Motor Programs in Arrays of Adjustable Pattern Generators

Cited by 124 publications

References 56 publications

Cerebellar learning of accurate predictive control for fast-reaching movements

Cerebellar learning of accurate predictive control for fast-reaching movements

Overlapping neural networks for multiple motor engrams.

Reciprocity between the cerebellum and the cerebral cortex: Nonlinear dynamics in microscopic modules for generating voluntary motor commands

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