The activity of 176 individual cells in the arm area of motor cortex (areas 4 and 6) was studied while monkeys made arm movements of similar direction within different parts of extrapersonal space. The behavioral paradigm used was a 3-dimensional reaction-time task aimed at dissociating the direction of movement, which remained similar across the work space, from the patterns of muscular activity and the angular joint excursions necessary to perform these movements. In agreement with other studies (Georgopoulos et al., 1982; Schwartz et al., 1988), we found that, within a given part of space, the activity of 169 (96.0%) cells studied increased most for a given preferred direction and less for other directions of movement. This change was graded in an orderly fashion. We further analyzed the orientation in space of the cells' preferred directions under the differing conditions of the task. We found that, as movements with similar trajectories were made within different parts of space, the cells' preferred directions changed spatial orientation. This change was of different magnitudes for different cells, but at the level of the population, it followed closely the changes in orientation of the arm necessary to perform the movements required by the task. Movement population vectors (Georgopoulos et al., 1983, 1986, 1988) computed from cell activity proved to be good predictors of movement direction regardless of where in space the movements were performed. These results indicate that motor cortical cells can code direction of movement in a way which is dependent on the position of the arm in space. The data are discussed in relation to the existence of mechanisms which facilitate the transformation between extrinsic and intrinsic coordinates. These transformations are necessary to perform arm movements to visual targets in space.

The activity of 156 individual arm-related neurons was studied in the premotor cortex (area 6) while monkeys made arm movements of similar directions within different parts of 3-dimensional space. This study was aimed at describing the relationship between premotor cortical cell activity and direction of arm movement and assessing the coordinate system underlying this relationship. We found that the activity of 152 (97.4%) cells varied in an orderly fashion with the direction of movement, in at least some region of the work space. Premotor cortical cells fired most for a given preferred direction and less for other directions of movement. These preferred directions covered the directional continuum in a uniform fashion across the work space. It was found that, as movements of similar directions were made within different parts of the work space, the cells' preferred directions changed their orientation. Although these changes had different magnitudes for different cells, at the population level, they followed closely the changes in orientation of the arm necessary to move the hand from one to another part of the work space. This shift of cells' preferred direction with the orientation of the arm in space has been observed with similar characteristics in the motor cortex (see Caminiti et al., 1990). In both premotor and motor cortices, neuronal movement population vectors provide a good description of movement direction. Unlike the individual cell preferred directions upon which they are based, movement population vectors did not change their spatial orientation across the work space, suggesting that they remain good predictors of movement direction regardless of the region of space in which movements are made. The firing frequency of both premotor and motor cortical neurons varied significantly with the position occupied by the hand in space. These static positional effects were observed in 88.5% of premotor and 91.8% of motor cortical cells. In a second task, monkeys made movements from differing origins to a common end point. This task was performed within 3 different parts of space and was aimed at dissociating movement direction from movement end point. It was found that in both premotor and motor cortices virtually all cells were related to the direction and not to the end point of movement. These data suggest that premotor and motor cortices use common mechanisms for coding arm movement direction. They also provide a basis for understanding the coordinate transformation required to move the hand toward visual targets in space.

No abstract

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.