Simulations of the alpha motoneuron pool electromyogram reflex at different preactivation levels in man

Slot, Peter Jacob; Sinkj˦r, Thomas

doi:10.1007/s004220050038

Cited by 6 publications

(11 citation statements)

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“…However, the presence of gain scaling may actually impede this goal because, under the assumption of a relatively linear relationship between a muscle's activity and its force production (Hof 1984;Lawrence and Deluca 1983;Milner-Brown and Stein 1975), the motor output should reflect the magnitude of the perturbation regardless of the initial conditions. Since the gain-scaling phenomenon is caused by the size-recruitment principle of the motoneuron pool (Capaday and Stein 1987;Kernell and Hultborn 1990;Slot and Sinkjaer 1994), its presence in the short-latency epoch appears to reflect an inadequacy of the short-latency response to account for the size-recruitment principle.…”

Section: Effect Of Environmental Constraintsmentioning

confidence: 97%

Optimal feedback control and the long-latency stretch response

2012

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There has traditionally been a separation between voluntary control processes and the fast feedback responses which follow mechanical perturbations (i.e., stretch "reflexes"). However, a recent theory of motor control, based on optimal control, suggests that voluntary motor behavior involves the sophisticated manipulation of sensory feedback. We have recently proposed that one implication of this theory is that the long-latency stretch "reflex", like voluntary control, should support a rich assortment of behaviors because these two processes are intimately linked through shared neural circuitry including primary motor cortex. In this review, we first describe the basic principles of optimal feedback control related to voluntary motor behavior. We then explore the functional properties of upper-limb stretch responses, with a focus on how the sophistication of the long-latency stretch response rivals voluntary control. And last, we describe the neural circuitry that underlies the long-latency stretch response and detail the evidence that primary motor cortex participates in sophisticated feedback responses to mechanical perturbations.

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Section: Effect Of Environmental Constraintsmentioning

confidence: 97%

Optimal feedback control and the long-latency stretch response

2012

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“…Hodgkin-Huxley-type ionic channels for AP generation were thus avoided. The computing time and the availability of parameter values led us to use a single-compartment MN model with a homogenous membrane obeying Ohm's law, instead of the four-compartment model by Slot and Sinkjaer (1994). It is extremely hard to decide, in their model, which of the 60 parameters depend on the MN type and which values should be attributed to them.…”

Section: Model Complexitymentioning

confidence: 99%

“…However, the synaptic weight for a given MNPMC input-force relation can be computed in none of them. The model of Slot and Sinkjaer (1994) and Wilmink et al (1996), which resembles most the present one, has the following deficiencies with regard to our objectives:…”

Section: Introductionmentioning

confidence: 97%

Computer simulation of the motoneuron pool-muscle complex. I. Input system and motoneuron pool

Nussbaumer

Rüegg

Studer³

et al. 2002

Biological Cybernetics

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The aim of the present study was to simulate the input system and the motoneuron (MN) pool of the MN pool-muscle complex (MNPMC). Input fibers, which can originate from command centers in the central nervous system or from sensory organs, activate the MN pool. They generate sequences of action potentials, the frequency of which is proportional to a time-dependent activation factor (which is an input to the model). Different connection patterns between the input fibers and motor units (MUs) are allowed. For simplicity and since no precise experimental data are available, 70 input fibers and 4 boutons per fiber and MN are simulated (this corresponds approximately to the monosynaptic group-Ia input of the cat medial gastrocnemius muscle). Each bouton generates the same conductance change in the postsynaptic membrane. The MNs are modeled with a single compartment and a homogeneous membrane. According to experimental data, the membrane leakage conductance and capacitance are MU dependent. Since the precise relation is unknown: (a) the computed relation between MU contraction force and the MN leakage conductance was taken from a steady-state MNPMC model, and (b) the capacitance was assumed to be proportional to the leakage conductance. The MN membrane includes time- and voltage-dependent ionic channels (fast and slow K(+) and low- and high-threshold Ca(2+) channels). The density and time constant of the slow K(+) channels and the density of the Ca(2+) channels were fitted to approximate afterhyperpolarization characteristics and frequency-injected current relations of type-identified cat MNs. If the membrane reaches a voltage threshold the MNs generate action potentials, which were simulated by voltage pulses. The activation of the MN pool of the human first dorsal interosseus muscle was simulated with injected and synaptic currents in order to illustrate the size principle, synaptic noise, and other features of muscle activation. It is concluded that the present model reproduces the main properties of the input-output relations of different MN types within a muscle. Together with the simulation of the muscle force and the surface EMG, which will be published in subsequent papers, it will be a powerful tool for reproducing experiments on the motor system and investigating functional mechanisms of motor control.

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“…The various scaled (duration and size) MUAPs are then added together, assuming the muscle tissue to be electrophysiologically linear and time invariant (for justification of this assumption, see Slot and Sinkjer, 1994), resulting in a simulated soleus EMG signal based on the action potentials in the ol-motoneuron pool. This combination of motoneuron pool and muscle model has proven to be appropriate for modeling the H-reflexes at different tonic contraction levels in humans (Slot and Sinkjaer, 1994), and this model formed the basis for the extended model as it is presented in this article.…”

Section: The Modelmentioning

confidence: 99%

“…Recruitment Gain. The variation of the sizes of the a-motoneurons throughout the pool obeys Henneman's size principle (Henneman, 1957), where in the case of the model the last motoneuron (neuron #200) has twice the size of the smallest neuron (neuron #l) (SIot and Sinkjaer, 1994). Using the recruitment gain mechanism we wanted to test whether or not it can explain the experimental findings by changing Tuble 2.…”

Section: Simulationsmentioning

confidence: 99%

Modeling of the H-reflex facilitation during ramp and hold contractions

1996

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Healthy subjects were asked to make a voluntary ramp and hold contraction. The duration of the ramp stage was 500 ms, and the torque increment in this period was set to 15 Nm. The contraction was made from a relaxed and from a 5 Nm background torque situation. Hoffmann (H-) reflexes were elicited during the voluntary contraction, mostly with 100 ms intervals. These experiments showed an increase (facilitation) in the H-reflex before the torque or the EMG started to increase. This facilitation of the H-reflex remained during all the stages of the voluntary movement and declined to normal levels again only at the very end of the hold phase, which lasted for one second. This specific pattern of facilitation during a voluntary contraction was modeled using a modeling language, that is specifically designed to calculate neuronal systems with a high degree of reality (Ekeberg et al., 1991). Our model consisted of a motoneuron pool with 200 neurons connected to an EMG-model of the human soleus muscle and an extra group of higher-level neurons for controlling the amount of decrease of presynaptic inhibition. The model was used to simulate the observed modulation of the H-reflex with both a presynaptic and a postsynaptic mechanism. Simulations showed that a continuous change in the descending control signals is needed to make the model based on postsynaptic mechanism fit with the experimental data, whereas no extra control from the CNS over the excitatory drive to the motoneuron pool is needed when the decrease of presynaptic inhibition mechanism is applied.

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Simulations of the alpha motoneuron pool electromyogram reflex at different preactivation levels in man

Cited by 6 publications

References 0 publications

Optimal feedback control and the long-latency stretch response

Optimal feedback control and the long-latency stretch response

Computer simulation of the motoneuron pool-muscle complex. I. Input system and motoneuron pool

Modeling of the H-reflex facilitation during ramp and hold contractions

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