Kwan H C, Yeap T H, Jiang B C, Borrett D
Department of Physiology, University of Toronto, Ont., Canada.
Can J Physiol Pharmacol. 1990 Jan;68(1):126-30. doi: 10.1139/y90-019.
It is possible to embed the control and computation of a simple single-joint movement at different speeds by a small non-linear network of neuron-like elements. The network "learns" by appropriate adjustment of the strengths of interconnection, or synaptic weights, between the neuron-like elements. The learning of a few movement trajectories is generalized to the learning of a family of unlearned trajectories. These observations are in support of our hypothesis that relaxation of a network from an initial state to a final equilibrium state is both causal and computational to movement generation and control.
通过一个由类神经元元素构成的小型非线性网络，可以实现对简单单关节运动在不同速度下的控制和计算。该网络通过适当调整类神经元元素之间的连接强度或突触权重来“学习”。对一些运动轨迹的学习可以推广到对一系列未学习轨迹的学习。这些观察结果支持了我们的假设，即网络从初始状态到最终平衡状态的松弛对于运动的产生和控制既是因果性的也是计算性的。
