Classification and Geometry of General Perceptual Manifolds

2018/07/05 by SueYeon Chung, Daniel D. Lee, Haim Sompolinsky · 1 voice · 15 citations
Neuroscience · Computer Science · #Visual perception and processing mechanisms #Neural Networks and Applications #Face and Expression Recognition

paper · pdf · doi:10.1103/physrevx.8.031003

Abstract

Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location, and intensity) of the same perceptual object. Object recognition and discrimination require classifying the manifolds in a manner that is insensitive to variability within a manifold. How neuronal systems give rise to invariant object classification and recognition is a fundamental problem in brain theory as well as in machine learning.

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Discussions

• Enjoyed speaking at Frontiers in NeuroAI Symposium @harvard.edu's @kempnerinstitute.bsky.social Key papers from the talk: *Manifold capacity theory: - original: doi.org/10.1103/Phys... - correlated [bsky, 26 points, 1 comments]

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