How do our brains process visual information? This comprehensive review delves into the longstanding assumption that sensory neurons adapt to the statistical properties of the signals they are exposed to, through both evolutionary and developmental processes. It examines the theoretical link between environmental statistics and neural responses, using the concept of coding efficiency proposed by Attneave (1954) and Barlow (1961). Recent advances in statistical modeling and computational tools have enabled researchers to study more complex statistical models for visual images. These models have been rigorously validated against extensive datasets. Furthermore, researchers have begun experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons. Ultimately, this review summarizes the progress in understanding how the visual system adapts to the statistical properties of natural images. It highlights the convergence of theoretical models, empirical validation, and experimental testing in advancing our knowledge of neural representation and coding efficiency. The insights gained from this research have far-reaching implications for understanding sensory processing, developing artificial vision systems, and unraveling the complexities of the brain.
This review, published in the Annual Review of Neuroscience, fits perfectly within the journal's focus on the latest advances in understanding the nervous system. The paper's exploration of how neural representation adapts to natural image statistics contributes significantly to the field of sensory neuroscience.