Neural network control of simple limb movements

Abstract

It is possible to embed the control and computation of a simple single-joint movement at different speeds by a small nonlinear 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.Key words: control of movement, neural network, learning, nonlinear dynamics.