Cellular Automata Can Reduce Memory Requirements of Collective-State Computing.

ABSTRACT: Various nonclassical approaches of distributed information processing, such as neural networks, reservoir computing (RC), vector symbolic architectures (VSAs), and others, employ the principle of collective-state computing. In this type of computing, the variables relevant in computation are superimposed into a single high-dimensional state vector, the collective state. The variable encoding uses a fixed set of random patterns, which has to be stored and kept available during the computation. In this article, we show that an elementary cellular automaton with rule 90 (CA90) enables the space-time tradeoff for collective-state computing models that use random dense binary representations, i.e., memory requirements can be traded off with computation running CA90. We investigate the randomiza

SUBMITTER: Kleyko D 

PROVIDER: S-EPMC9215349 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature