Abstract
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.
Original language | English |
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Pages (from-to) | 15-24 |
Number of pages | 10 |
Journal | Nature Neuroscience |
Volume | 22 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2019 |
Bibliographical note
Funding Information:We thank H. Krapp, D. Pospisil, and J. Shlens for helpful feedback on an earlier version of this review. H. Krapp very generously provided the data and schematic shown in Fig. 4a,b. This work was supported by NIH grants F31-EY026288 (to M.H.T.), EY028542 (to F.R.), and a National Science Foundation Grant 1715475 (to O.S.).
Publisher Copyright:
© 2018, Springer Nature America, Inc.
ASJC Scopus subject areas
- Neuroscience (all)