  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.
