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Journal of Neuroscience, Vol 12, 1435-1453, Copyright © 1992 by Society for Neuroscience
Visuomotor transformations underlying arm movements toward visual targets: a neural network model of cerebral cortical operations
Y Burnod, P Grandguillaume, I Otto, S Ferraina, PB Johnson and R Caminiti
Departement des Neurosciences de la Vision, Universite Paris VI, France.
We propose a biologically realistic neural network that computes coordinate
transformations for the command of arm reaching movements in 3-D space.
This model is consistent with anatomical and physiological data on the
cortical areas involved in the command of these movements. Studies of the
neuronal activity in the motor (Georgopoulos et al., 1986; Schwartz et al.,
1988; Caminiti et al., 1990a) and premotor (Caminiti et al., 1990b, 1991)
cortices of behaving monkeys have shown that the activity of individual
arm-related neurons is broadly tuned around a preferred direction of
movements in 3-D space. Recent data demonstrate that in both frontal areas
(Caminiti et al., 1990a,b, 1991) these cell preferred directions rotate
with the initial position of the arm. Furthermore, the rotation of the
population of preferred directions precisely corresponds to the rotation of
the arm in space. The neural network model computes the motor command by
combining the visual information about movement trajectory with the
kinesthetic information concerning the orientation of the arm in space. The
appropriate combination, learned by the network from spontaneous movement,
can be approximated by a bilinear operation that can be interpreted as a
projection of the visual information on a reference frame that rotates with
the arm. This bilinear combination implies that neural circuits converging
on a single neuron in the motor and premotor cortices can learn and
generalize the appropriate command in a 2-D subspace but not in the whole
3-D space. However, the uniform distribution of cell preferred directions
in these frontal areas can explain the computation of the correct solution
by a population of cortical neurons. The model is consistent with the
existing neurophysiological data and predicts how visual and somatic
information can be combined in the different processing steps of the
visuomotor transformation subserving visual reaching.
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