A cerebellar neural network model for adaptative control of saccades implemented with MATLAB.
This paper describes the implementation of a neural network for the adaptative control of the saccadic system. The model shows the cerebellum plays an important role in the adaptive control of the saccadic gain. Using only eye position input through the granule cells, the cerebellum projects this signal to the other cerebellar structures and then to motor neurons responsible for the saccade. The generation of an adjustment signal occurs in the inferior olive as a result of the error sensory signal created by the open loop saccade system from propioceptive position inputs from the last eye movement generated by the network until the movement towards the target is completed. In addition, a memory component has been defined in the error system to achieve the adaptation. This neural network involves only the horizontal saccade component modeled with Matrix Laboratory language (MATLAB), in conjunction with the Simulink tool.
University of Connecticut, Storrs, Connecticut 06269-2157, USA.
Francisco A
FA
Rodriguez Campos
John
J
Enderle
D000200:Action Potentials / Q000502:physiology; D000222:Adaptation, Physiological / Q000502:physiology*; D000818:Animals; D002531:Cerebellum / Q000502:physiology*; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D009801:Oculomotor Muscles / Q000502:physiology; D009847:Olivary Nucleus / Q000502:physiology; D012438:Saccades / Q000502:physiology*