Stimulus- and goal-oriented frameworks for understanding natural vision

• Published in: Nature neuroscience Vol. 22; no. 1; pp. 15 - 24
• Main Authors: Turner, Maxwell H., Sanchez Giraldo, Luis Gonzalo, Schwartz, Odelia, Rieke, Fred
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2019; Nature Publishing Group
• Subjects: 631/378/2613; 631/378/3917; Animal Genetics and Genomics; Animals; Behavioral Sciences; Biological Techniques; Biomedical and Life Sciences; Biomedicine; Brain research; Coding; Humans; Hypotheses; Information processing; Learning algorithms; Machine learning; Models, Neurological; Neural circuitry; Neural networks; Neurobiology; Neurophysiology; Neurosciences; Perceptual-motor processes; Photic Stimulation; Review Article; Sensory integration; Sensory perception; Stimuli; Stimuli (Psychology); Vision, Ocular - physiology; Visual pathway; Visual Pathways - physiology
• ISSN: 1097-6256; 1546-1726; 1546-1726
• DOI: 10.1038/s41593-018-0284-0

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.