Neural structure of a sensory decoder for motor control
| Autor: | Seth W. Egger, Stephen G. Lisberger |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Multidisciplinary
Computer science business.industry General Physics and Astronomy Motor control Sensory system General Chemistry General Biochemistry Genetics and Molecular Biology Smooth pursuit Noise Transmission (telecommunications) Biological neural network Artificial intelligence business Decoding methods |
| Popis: | We seek to understand the neural mechanisms that perform sensory decoding for motor behavior, advancing the field by designing decoders based on neural circuits. A simple experiment produced a surprising result that shapes our approach. Changing the size of a target for smooth pursuit eye movements changes the relationship between the variance and mean of the evoked behavior in a way that contradicts the regime of “signal-dependent noise” and defies traditional decoding approaches. A theoretical analysis leads us to conclude that sensory decoding circuits for pursuit include multiple parallel pathways and multiple sources of variation. Behavioral and neural responses with biomimetic statistics emerge from a biologically-motivated circuit model with noise in the pathway that is dedicated to flexibly adjusting the strength of visual-motor transmission. Flexible adjustment of transmission strength applies much more broadly to issues in sensory-motor control such as Bayesian integration and control strategies to optimize motor behavior. |
| Databáze: | OpenAIRE |
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