Burnod Y, Grandguillaume P, Otto I, Ferraina S, Johnson P B, Caminiti R
Département des Neurosciences de la Vision, Université Paris VI, France.
J Neurosci. 1992 Apr;12(4):1435-53. doi: 10.1523/JNEUROSCI.12-04-01435.1992.
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.
我们提出了一种具有生物学现实意义的神经网络,该网络可计算用于指挥三维空间中手臂伸展运动的坐标变换。此模型与参与这些运动指挥的皮质区域的解剖学和生理学数据一致。对行为猴子的运动皮质(乔治opoulos等人,1986年;施瓦茨等人,1988年;卡米尼蒂等人,1990年a)和运动前皮质(卡米尼蒂等人,1990年b,1991年)中神经元活动的研究表明,单个与手臂相关的神经元的活动在三维空间中围绕一个偏好的运动方向广泛调谐。最近的数据表明,在额叶的两个区域(卡米尼蒂等人,1990年a、b,1991年),这些细胞的偏好方向会随着手臂的初始位置而旋转。此外,偏好方向群体的旋转与手臂在空间中的旋转精确对应。该神经网络模型通过将关于运动轨迹的视觉信息与有关手臂在空间中方向的动觉信息相结合来计算运动指令。网络从自发运动中学习到的适当组合可以通过双线性运算来近似,该运算可解释为将视觉信息投影到随手臂旋转的参考框架上。这种双线性组合意味着,汇聚到运动皮质和运动前皮质中单个神经元的神经回路可以在二维子空间中学习并推广适当的指令,但不能在整个三维空间中学习。然而,这些额叶区域中细胞偏好方向的均匀分布可以解释一群皮质神经元如何计算出正确的解决方案。该模型与现有的神经生理学数据一致,并预测了在视觉到达的视运动转换的不同处理步骤中视觉和躯体信息如何组合。