Implementing Rules with Aritificial Neurons

Huyck, Christian R. ORCID: https://orcid.org/0000-0003-4015-3549 and Kreivena, Dainius (2018) Implementing Rules with Aritificial Neurons. Artificial Intelligence XXXV: 38th SGAI International Conference on Artificial Intelligence, AI 2018, Cambridge, UK, December 11–13, 2018, Proceedings. In: AI-2018 38th SGAI International Conference on Artificial Intelligence, 11-13 Dec 2018, Cambridge. ISBN 9783030041908. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-030-04191-5_2)

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Abstract

Rule based systems are an important class of computer languages. The brain, and more recently neuromorphic systems, is based on neurons. This paper describes a mechanism that converts a rule based system, specified by a user, to spiking neurons. The system can then be run in simulated neurons, producing the same output. The conversion is done making use of binary cell assemblies, and finite state automata. The binary cell assemblies, eventually implemented in neurons, implement the states. The rules are converted to a dictionary of facts, and simple finite state automata. This is then cached out to neurons. The neurons can be simulated on standard simulators, like NEST, or on neuromorphic hardware. Parallelism is a benefit of neural system, and rule based systems can take advantage of this parallelism. It is hoped that this work will support further exploration of parallel neural and rule based systems, and sup

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