Coarse-graining retinal responses to reveal predictive information

The vertebrate retina performs prediction on incoming visual signals, which can compensate for lags in neural processing [1]. This computation is collective, meaning it relies upon interactions between many neurons. However, it is not well understood how correlations between neurons enable prediction in large subpopulations (greater than ten) or when the visual stimulus is complex. In this work, we address these challenges together by searching for maximally-predictive collective variables in large subsets of 93 salamander retinal ganglion cells under stimulation with natural movies. To find these collective variables, we apply a tractable, approximate implementation of the information bottleneck method to neural data [2], and infer a lower-dimensional representation that is maximally informative about the future neural activity. We observe scaling relationships between this mutual information estimate, neural subset size, and information decay timescale. Further, we examine the structure of collective modes learned by this method and compare them to those obtained by other forms of coarse-graining.

[1] S. E. Palmer, O. Marre, M. J. Berry, and W. Bialek, “Predictive information in a sensory population,” Proceedings of the National Academy of Sciences, vol. 112, no. 22, pp. 6908–6913, Jun. 2015

[2] D. E. Gökmen, Z. Ringel, S. D. Huber, and M. Koch-Janusz, “Symmetries and phase diagrams with real-space mutual information neural estimation,” Phys. Rev. E, vol. 104, no. 6, p. 064106, Dec. 2021