title: 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 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.
authors.affiliation: Department of Physiology, University of Toronto, Ont., Canada.
authors.forename: H C
authors.initials: HC
authors.lastname: Kwan
authors.forename: T H
authors.initials: TH
authors.lastname: Yeap
authors.forename: B C
authors.initials: BC
authors.lastname: Jiang
authors.forename: D
authors.initials: D
authors.lastname: Borrett
mesh_terms: D004576:Electromyography; D005121:Extremities / Q000502:physiology*; D007858:Learning / Q000502:physiology; D008954:Models, Biological; D009068:Movement; D009434:Neural Pathways / Q000502:physiology*; D013569:Synapses / Q000502:physiology