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Abstract:
Visually-guided arm reaching movements are produced by
distributed neural networks within parietal and frontal regions of
the cerebral cortex. Experimental data indicate that (1) single
neurons in these regions are broadly tuned to parameters of
movement; (2) appropriate commands are elaborated by populations of
neurons; (3) the coordinated action of neurons can be visualized
using a neuronal population vector (NPV). However, the NPV provides
only a rough estimate of movement parameters (direction, velocity)
and may even fail to reflect the parameters of movement when arm
posture is changed. We designed a model of the cortical motor
command to investigate the relation between the desired direction
of the movement, the actual direction of movement and the direction
of the NPV in motor cortex. The model is a two-layer
self-organizing neural network which combines broadly-tuned
(muscular) proprioceptive and (cartesian) visual information to
calculate (angular) motor commands for the initial part of the
movement of a two-link arm. The network was trained by motor
babbling in 5 positions. Simulations showed that (1) the network
produced appropriate movement direction over a large part of the
workspace; (2) small deviations of the actual trajectory from the
desired trajectory existed at the extremities of the workspace; (3)
these deviations were accompanied by large deviations of the NPV
from both trajectories. These results suggest the NPV does not give
a faithful image of cortical processing during arm reaching
movements.
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