Limits...
Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model.

Schuurmans J, van der Helm FC, Schouten AC - J Comput Neurosci (2010)

Bottom Line: The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior.A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics.However, position feedback and force feedback gains show strong interactions with other neural and sensory properties.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. j.schuurmans@tudelft.nl

ABSTRACT
During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments.

Show MeSH
Neuromusculoskeletal model. A muscle pair actuated a one degree of freedom joint while being controlled by a spinal network with populations of motoneurons (MN), group Ia interneurons (IA), Renshaw cells (RC), inhibitory interneurons (IN), excitatory interneurons (EX) and group Ib interneurons (IB). Feedback is provided by Ia, Ib and II afferents
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3108017&req=5

Fig1: Neuromusculoskeletal model. A muscle pair actuated a one degree of freedom joint while being controlled by a spinal network with populations of motoneurons (MN), group Ia interneurons (IA), Renshaw cells (RC), inhibitory interneurons (IN), excitatory interneurons (EX) and group Ib interneurons (IB). Feedback is provided by Ia, Ib and II afferents

Mentions: The spinal neural network, which integrated the afferent input to generate the efferent control signals to the muscles, was based on Bashor (1998) and presented before in Stienen et al. (2007). The model consisted of six pairs of spinal neuron populations, i.e. motoneurons, Renshaw cells, group Ia and Ib interneurons and inhibitory and excitatory interneurons (see Fig. 1). Each population consisted of either 169 or 196 individual spiking neurons (MacGregor and Oliver 1974). These neurons have four state variables, i.e. membrane potential, variable threshold, potassium conductance and synaptic conductance. Whenever the membrane potential reached threshold, the neuron fired a discrete spike which was transmitted to the connected synapses. The synaptic connections between the neurons were created according to the connection scheme in Fig 1. Tonic, descending excitation (TDE) provided background activity to the motoneurons (resulting in co-contraction) and to some of the other neural populations. Each neuron in a receiving population was connected to 34–232 neurons, afferent fibers or descending fibers. Generally, the afferent input fans out over the populations. The connections with the lower number of synapses are closer to the afferent input than the connections with high number of synapses (the interneuronal connections). A full overview of synapse count can be found in Stienen et al. (2007). The individual projections were randomized. Five pre-set types of synaptic connections were used: single, double and triple strength excitatory synapses, excitatory synapses a with long time constant (to the Renshaw cells), and inhibitory synapses. No network training or any form of neural plasticity was implemented. Since the many motoneurons in a population all activated a single, lumped muscle (no individual muscle fibers and a single neuromuscular junction), the input signal to the muscle activation dynamics was obtained by taking a 20 ms moving average of the summed spike output of the motoneuron populations. Efferent time delay was 10 ms.Fig. 1


Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model.

Schuurmans J, van der Helm FC, Schouten AC - J Comput Neurosci (2010)

Neuromusculoskeletal model. A muscle pair actuated a one degree of freedom joint while being controlled by a spinal network with populations of motoneurons (MN), group Ia interneurons (IA), Renshaw cells (RC), inhibitory interneurons (IN), excitatory interneurons (EX) and group Ib interneurons (IB). Feedback is provided by Ia, Ib and II afferents
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108017&req=5

Fig1: Neuromusculoskeletal model. A muscle pair actuated a one degree of freedom joint while being controlled by a spinal network with populations of motoneurons (MN), group Ia interneurons (IA), Renshaw cells (RC), inhibitory interneurons (IN), excitatory interneurons (EX) and group Ib interneurons (IB). Feedback is provided by Ia, Ib and II afferents
Mentions: The spinal neural network, which integrated the afferent input to generate the efferent control signals to the muscles, was based on Bashor (1998) and presented before in Stienen et al. (2007). The model consisted of six pairs of spinal neuron populations, i.e. motoneurons, Renshaw cells, group Ia and Ib interneurons and inhibitory and excitatory interneurons (see Fig. 1). Each population consisted of either 169 or 196 individual spiking neurons (MacGregor and Oliver 1974). These neurons have four state variables, i.e. membrane potential, variable threshold, potassium conductance and synaptic conductance. Whenever the membrane potential reached threshold, the neuron fired a discrete spike which was transmitted to the connected synapses. The synaptic connections between the neurons were created according to the connection scheme in Fig 1. Tonic, descending excitation (TDE) provided background activity to the motoneurons (resulting in co-contraction) and to some of the other neural populations. Each neuron in a receiving population was connected to 34–232 neurons, afferent fibers or descending fibers. Generally, the afferent input fans out over the populations. The connections with the lower number of synapses are closer to the afferent input than the connections with high number of synapses (the interneuronal connections). A full overview of synapse count can be found in Stienen et al. (2007). The individual projections were randomized. Five pre-set types of synaptic connections were used: single, double and triple strength excitatory synapses, excitatory synapses a with long time constant (to the Renshaw cells), and inhibitory synapses. No network training or any form of neural plasticity was implemented. Since the many motoneurons in a population all activated a single, lumped muscle (no individual muscle fibers and a single neuromuscular junction), the input signal to the muscle activation dynamics was obtained by taking a 20 ms moving average of the summed spike output of the motoneuron populations. Efferent time delay was 10 ms.Fig. 1

Bottom Line: The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior.A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics.However, position feedback and force feedback gains show strong interactions with other neural and sensory properties.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. j.schuurmans@tudelft.nl

ABSTRACT
During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments.

Show MeSH