Gerard J. Rinkus

The paper introduces a new theory of probabilistic representation in the brain, proposing binary units, sparse codes, and noise as a computational resource, and demonstrates its ability to represent full distributions via spike codes.

The paper introduces a novel unsupervised learning algorithm using modular sparse distributed codes that preserves similarity and enables fixed-time learning and retrieval.