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Optimization of Input Patterns and Neuronal Properties to Evoke Motor Neuron Synchronization

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Abstract

The study used a computational approach to identify combinations of synaptic input timing and strength superimposed on a variety of active dendritic conductances that could evoke similar levels of motor unit synchronization in model motor neurons. Two motor neurons with low recruitment thresholds but different passive properties were modeled using GENESIS software. The timing and strength of synaptic inputs and the density of dendritic ion channels were optimized with a genetic algorithm to produce a set of target discharge times. The target times were taken from experimental recordings made in a human subject and had the synchronization characteristics that are commonly observed in hand muscles. The main finding was that the two parameters with the highest association to output synchrony were the ratio of inward-to-outward ionic conductances (r = 0.344; P = 0.003) and the degree of correlation in inhibitory inputs (r = 0.306; P = 0.009). Variation in the amount of correlation in the excitatory input was not positively correlated with variation in output synchrony. Further, the variability in discharge rate of the model neurons was positively correlated with the density of N-type calcium channels in the dendritic compartments (r = 0.727; P < 0.001 and r = 0.533; P < 0.001 for the two cells). This result suggests that the experimentally observed correlation between discharge variability and synchronization is caused by an increase in fast inward ionic conductances in the dendrites. Given the moderate level of correlation between output synchrony and each of the model parameters, especially at moderate levels of synchrony (E < 0.09 and CIS < 1.0), the results suggest caution in ascribing mechanisms to observations of motor unit synchronization.

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Authors
  1. Anna M. Taylor
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  2. Roger M. Enoka
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Taylor, A.M., Enoka, R.M. Optimization of Input Patterns and Neuronal Properties to Evoke Motor Neuron Synchronization. J Comput Neurosci 16, 139–157 (2004). https://doi.org/10.1023/B:JCNS.0000014107.16610.2e

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