Emergence of tri-phasic muscle activation from the nonlinear interactions of central and spinal neural network circuits

Bullock, Daniel; Grossberg, Stephen

doi:10.1016/0167-9457(92)90057-i

Cited by 31 publications

(8 citation statements)

References 14 publications

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“…2, 3). This is a key distinction compared to previous models of spinal circuitry (Feldman, 1966;Bullock and Grossberg, 1992;Bullock et al, 1993;Bashor, 1998;van Heijst et al, 1998;Ostry and Feldman, 2003;Lan et al, 2005;Maier et al, 2005), but it is more consistent with the actual neuroanatomy. This also appears to be the first model of spinal cord circuitry to deal with the shifting patterns of synergistic and antagonistic muscle activity that necessarily accompany multimuscle, multi-degree-offreedom systems.…”

Section: Spinal Circuitry Modelmentioning

confidence: 86%

“…Over the past 20 years, there have been several attempts to model the lower sensorimotor system for voluntary movement, but they were usually restricted to a hinge joint operated by an antagonist muscle pair and they usually incorporated only a small subset of the known spinal circuitry (Feldman, 1966;Bullock and Grossberg, 1992;Bullock et al, 1993;Bashor, 1998;Feldman et al, 1998;van Heijst et al, 1998;Ostry and Feldman, 2003;Lan et al, 2005;Maier et al, 2005). Loeb et al (1990) introduced linear quadratic regulator design to find optimal solutions for reflex gains in a multisegment limb model (He et al, 1991), but there was no way to reconcile these gains with the actual interneuronal elements of the spinal cord.…”

Section: Introductionmentioning

confidence: 99%

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Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist

Raphael

Tsianos²,

Loeb³

2010

Journal of Neuroscience

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The performance of motor tasks requires the coordinated control and continuous adjustment of myriad individual muscles. The basic commands for the successful performance of a sensorimotor task originate in "higher" centers such as the motor cortex, but the actual muscle activation and resulting torques and motion are considerably shaped by the integrative function of the spinal interneurons. The relative contributions of brain and spinal cord are less clear for reaching movements than for automatic tasks such as locomotion. We have modeled a two-axis, four-muscle wrist joint with realistic musculoskeletal mechanics and proprioceptors and a network of regulatory circuitry based on the classical types of spinal interneurons (propriospinal, monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, and Ib-inhibitory pathways) and their supraspinal control (via biasing activity, presynaptic inhibition, and fusimotor gain). The modeled system has a very large number of control inputs, not unlike the real spinal cord that the brain must learn to control to produce desired behaviors. It was surprisingly easy to program this model to emulate actual performance in four very different but well described behaviors: (1) stabilizing responses to force perturbations; (2) rapid movement to position target; (3) isometric force to a target level; and (4) adaptation to viscous curl force fields. Our general hypothesis is that, despite its complexity, such regulatory circuitry substantially simplifies the tasks of learning and producing complex movements.

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Section: Spinal Circuitry Modelmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist

Raphael

Tsianos²,

Loeb³

2010

Journal of Neuroscience

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“…Figure 3e reveals that the amplitude of end-point oscillations is reduced when neurons receiving GTO excitation have a slightly elevated threshold (traces marked T=0.2). Figure :lf reveals that the circuit exhibits the characteristic tri-phasic burst pattern observ<1hle in primate EMG data (e.g., Lcstienne, 1979; see also Bullock & Grossberg, 1992). It ;Llso shows that the elevated threshold in GTO-excited cells reduces co-activi1t.ion during the launch phi1sc.…”

Section: Simulation Resultsmentioning

confidence: 99%

Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control

Bullock

Contreras-Vidal

Grossberg

1993

Neural Networks in Robotics

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One of the adva.ntages of biological skeleto-motor systems is the opponent muscle design, which in principle makes it possible to achieve facile independent control of joint angle and joint stiffness. Prior analysis of equilibrium states of a biologicallybased neural network for opponent muscle control, the FLETE model, revealed that such independent control requires specialized interneuronal circuitry to efficiently coordinate the opponent force generators. In this chapter, we refine the FLETE circuit variables specification and update the equilibrium analysis. We also incorporate additional neuronal circuitry that ensures efficient opponent force generation and velocity regulation during movement.

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“…The spinal recipient of my model is the FLETE (Factorization of LEngth and Tension) model [2,3,4,5,6]. Briefly, the FLETE model is an opponent processing muscle control model of how spinal circuits afford independent voluntary control of joint stiffness and joint position.…”

Section: Basis Of the Modelmentioning

confidence: 99%

“…In this paper, I integrate experimental data on the anatomy and neurophysiology of the globus pallidus internal segment [19], the cortex [10] and the spinal cord structures, as well as data on PD psychophysics [16,17,18,20] to extend a neural model of basal ganglia-cortex-spinal cord interactions during movement production [2,3,4,5,6,7,8]. Computer simulations show that disruptions of the BG output and of the SNc's DA input to frontal and parietal cortices and spinal cord may be responsible for delayed movement initiation.…”

Section: Introductionmentioning

confidence: 99%

Neural Model of Dopaminergic Control of Arm Movements in Parkinson’s Disease Bradykinesia

Cutsuridis

2006

Lecture Notes in Computer Science

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Abstract. Patients suffering from Parkinson's disease display a number of symptoms such a resting tremor, bradykinesia, etc. Bradykinesia is the hallmark and most disabling symptom of Parkinson's disease (PD). Herein, a basal ganglia-cortico-spinal circuit for the control of voluntary arm movements in PD bradykinesia is extended by incorporating DAergic innervation of cells in the cortical and spinal components of the circuit. The resultant model simulates successfully several of the main reported effects of DA depletion on neuronal, electromyographic and movement parameters of PD bradykinesia.

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Emergence of tri-phasic muscle activation from the nonlinear interactions of central and spinal neural network circuits

Cited by 31 publications

References 14 publications

Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist

Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist

Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control

Neural Model of Dopaminergic Control of Arm Movements in Parkinson’s Disease Bradykinesia

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