Online Learning in an Oculomotor Integrator
نویسندگان
چکیده
The oculomotor neural integrator is a brainstem nucleus that stores the short term memory of eye position during gaze holding. This neural integrator is believed to perform temporal integration of transient eye velocity input using positive feedback. Such models of integration are known to suffer from a fine-tuning problem [9] since changes in the amount of feedback by less than 10% can de-tune the neural integrator. This problem may be overcome by online re-tuning using an error signal. We propose and analyze a simple learning rule for online tuning of weights in the integrator circuit using a scalar visual error signal. Our learning rule requires only local spatial and temporal information, so has minimal computational overhead and can be implemented easily in a biological setting. We show that the learning rule is globally convergent and believe that it represents a more general class of learning rules for motor control problems.
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