Cellular Automata Can Reduce Memory Requirements of Collective-State Computing

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 6; pp. 2701 - 2713
• Main Authors: Kleyko, Denis, Frady, Edward Paxon, Sommer, Friedrich T.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automata; Automaton; Cellular automata; Cellular automata (CA); Cellular automaton; Cellular automatons; collective-state computing; Computational modeling; Computational modelling; Computer memory; Data processing; Decoding; Distributed representation; distributed representations; hyperdimensional computing; Information processing; Job analysis; Memory architecture; Memory management; Network architecture; Neural networks; Neurons; Pseudorandom; Random processes; Random-number generation; Randomization; Representations; Reservoir Computing; reservoir computing (RC); Reservoir management; Reservoirs; rule 90; State vectors; Task analysis; Vector symbolic architecture; vector symbolic architectures (VSAs)

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 superim...