Open AccessStimulus- and goal-oriented frameworks for understanding natural vision
Author(s) -
Maxwell H. Turner,
Luis G. Sánchez Giraldo,
Odelia Schwartz,
Fred Rieke
Publication year - 2018
Publication title -
nature neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 13.403
H-Index - 422
eISSN - 1546-1726
pISSN - 1097-6256
DOI - 10.1038/s41593-018-0284-0
Subject(s) - neuroscience , stimulus (psychology) , systems neuroscience , psychology , cognitive psychology , neuroscientist , cognitive science , central nervous system , myelin , oligodendrocyte
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.