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Computational Neuroscience Journal Club

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If you have a question about this talk, please contact Guillaume Hennequin .

Yan Wu will cover: Hierarchical model of natural images and the origin of scale invariance Saeed Saremia and Terrence J. Sejnowski

http://www.pnas.org/content/110/8/3071.short

ABSTRACT : The study of natural images and how our brain processes them has been an area of intense research in neuroscience, psychology, and computer science. We introduced a unique approach to studying natural images by decomposing images into a hierarchy of layers at different logarithmic intensity scales and mapping them to a quasi-2D magnet. The layers were in different phases: “cold” and ordered at large-intensity scales, “hot” and disordered at small-intensity scales, and going through a second-order phase transition at intermediate scales. There was a single “critical” layer in the hierarchy that exhibited long-range correlation similar to that found in the 2D Ising model of ferromagnetism at the critical temperature. We also determined the interactions between layers mapped from natural images and found mutual inhibition that generated locally “frustrated” antiferromagnetic states. Almost all information in natural images was concentrated in a few layers near the phase transition, which has biological implications and also points to the hierarchical origin of scale invariance in natural images.

This talk is part of the Computational Neuroscience series.

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