Unsupervised learning architecture based on neural Darwinism and Hopfield networks recognizes symbols with high accuracy

Abstract

This paper introduces a novel unsupervised learning paradigm inspired by Gerald Edelman's theory of neuronal group selection ("Neural Darwinism"). The presented automaton learns to recognize arbitrary symbols (e.g., letters of an alphabet) when they are presented repeatedly, as they are when children learn to read. On a second hierarchical level, the model creates abstract categories representing the learnt symbols. The fundamental computational unit are simple McCulloch-Pitts neurons arranged into fully-connected groups (Hopfield networks with randomly initialized weights), which are "selected", in an evolutionary sense, through symbol presentation. The learning process is fully tractable and easily interpretable for humans, in contrast to most neural network architectures. Computational properties of Hopfield networks enabling pattern recognition are discussed. In simulations, the model achieves high accuracy in learning the letters of the Latin alphabet, presented as binary patterns on a grid. This paper is a proof of concept with no claims to state-of-the-art performance in letter recognition, but hopefully inspires new thinking in bio-inspired machine learning.