Stimulus- and goal-oriented frameworks for understanding natural vision - Turner, Maxwell H.; Sanchez Giraldo, Luis Gonzalo; Schwartz, Odelia; Rieke, Fred (Nature neuroscience, 2019)
Our knowledge of sensory processing has advanced dramatically in the last few decades, but this understanding remains far from complete, especially for stimuli with the large dynamic range and strong temporal and spatial correlations characteristic of natural visual inputs. Here we describe some of the issues that make understanding the encoding of natural images a challenge. We highlight two broad strategies for approaching this problem: a stimulus-oriented framework and a goal-oriented one. Different contexts can call for one framework or the other. Looking forward, recent advances, particularly those based in machine learning, show promise in borrowing key strengths of both frameworks and by doing so illuminating a path to a more comprehensive understanding of the encoding of natural stimuli. A satisfactory understanding of how natural stimuli are encoded by neural circuits has remained elusive. Advances in machine learning provide new approaches to this problem by merging constraints imposed by stimulus statistics and behavioral goals.