Learning sparse messages in networks of neural cliques

TL;DR

This paper introduces an extension to binary neural networks that enables learning sparse messages efficiently, supported by biological and informational justifications, with detailed rules and simulation results.

Contribution

It presents a novel extension for binary neural networks to learn sparse messages, improving memory efficiency and scalability.

Findings

Supports large numbers of sparse messages

Demonstrates high memory efficiency

Provides detailed learning and retrieval rules

Abstract

An extension to a recently introduced binary neural network is proposed in order to allow the learning of sparse messages, in large numbers and with high memory efficiency. This new network is justified both in biological and informational terms. The learning and retrieval rules are detailed and illustrated by various simulation results.