Interpreting the retinal neural code for natural scenes: From computations to neurons
Niru Maheswaranathan, Lane McIntosh, Hidenori Tanaka, Satchel Grant, David B. Kastner, Joshua B. Melander, Aran Nayebi, Luke E. Brezovec, Julia Wang, Surya Ganguli, Stephen A. Baccus
Abstract
Understanding the circuit mechanisms of the visual code for natural scenes is a central goal of sensory neuroscience. We show that a three-layer network model predicts retinal natural scene responses with an accuracy nearing experimental limits. The model's internal structure is interpretable, as interneurons recorded separately and not modeled directly are highly correlated with model interneurons. Models fitted only to natural scenes reproduce a diverse set of phenomena related to motion encoding, adaptation, and predictive coding, establishing their ethological relevance to natural visual computation. A new approach decomposes the computations of model ganglion cells into the contributions of model interneurons, allowing automatic generation of new hypotheses for how interneurons with different spatiotemporal responses are combined to generate retinal computations, including predictive phenomena currently lacking an explanation. Our results demonstrate a unified and general approach to study the circuit mechanisms of ethological retinal computations under natural visual scenes.