Hebbian learning and competition in the neural abstraction pyramid

The neural abstraction pyramid is a hierarchical neural architecture for image interpretation that is inspired by the principles of information processing found in the visual cortex. In this paper we present an unsupervised learning algorithm for its connectivity based on Hebbian weight updates and competition. The algorithm yields a sequence of feature detectors that produce increasingly abstract representations of the image content. These representations are distributed and sparse, and facilitate the interpretation of the image. We apply the algorithm to a dataset of handwritten digits, starting from local contrast detectors. The emerging feature detectors correspond to step edges, lines, strokes, curves, and digit shapes. They can be used to reliably classify the digits