Ignoring correlated activity causes a failure of retinal population codes

• Published in: Nature communications Vol. 11; no. 1; p. 4605
• Main Authors: Ruda, Kiersten, Zylberberg, Joel, Field, Greg D.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.09.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116/2394; 631/378/2613/1786; 9/10; Adaptation; Brain; Correlation; Decoding; Failure; Humanities and Social Sciences; Light intensity; Light levels; Local population; Luminous intensity; multidisciplinary; Noise; Population; Populations; Retina; Retinal ganglion cells; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-18436-2

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. To see during day and night, the retina adapts to a trillion-fold change in light intensity. The authors show that an accurate read-out of retinal signals over this intensity range requires that brain circuits account for changing noise correlations across populations of retinal neurons.