Ignoring correlated activity causes a failure of retinal population codes.

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From starlight to sunlight, adaptation alters retinal output, changing both the signal and noise among populations of retinal ganglion cells (RGCs). Here we determine how these light level-dependent changes impact decoding of retinal output, testing the importance of accounting for RGC noise correlations to optimally read out retinal activity. We find that at moonlight conditions, correlated noise is greater and assuming independent noise severely diminishes decoding performance. In fact, assuming independence among a local population of RGCs produces worse decoding than using a single RGC, demonstrating a failure of population codes when correlated noise is substantial and ignored. We generalize these results with a simple model to determine what conditions dictate this failure of population processing. This work elucidates the circumstances in which accounting for noise correlations is necessary to take advantage of population-level codes and shows that sensory adaptation can strongly impact decoding requirements on downstream brain areas.





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Ruda, Kiersten, Joel Zylberberg and Greg D Field (2020). Ignoring correlated activity causes a failure of retinal population codes. Nature communications, 11(1). p. 4605. 10.1038/s41467-020-18436-2 Retrieved from https://hdl.handle.net/10161/22492.

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Greg D. Field

Adjunct Associate Professor of Neurobiology

My laboratory studies how the retina processes visual scenes and transmits this information to the brain.  We use multi-electrode arrays to record the activity of hundreds of retina neurons simultaneously in conjunction with transgenic mouse lines and chemogenetics to manipulate neural circuit function. We are interested in three major areas. First, we work to understand how neurons in the retina are functionally connected. Second we are studying how light-adaptation and circadian rhythms alter visual processing in the retina. Finally, we are working to understand the mechanisms of retinal degenerative conditions and we are investigating potential treatments in animal models.

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