Neural structure of a sensory decoder for motor control

Seth W. Egger () and Stephen G. Lisberger
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Seth W. Egger: Duke University School of Medicine
Stephen G. Lisberger: Duke University School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract The transformation of sensory input to motor output is often conceived as a decoder operating on neural representations. We seek a mechanistic understanding of sensory decoding by mimicking neural circuitry in the decoder’s design. The results of a simple experiment shape 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 propose a circuit for pursuit that includes 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. Our results demonstrate the power of re-imagining decoding as processing through the parallel pathways of neural systems.

Date: 2022
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DOI: 10.1038/s41467-022-29457-4

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