Implementing Rules with Aritificial Neurons

Huyck, Christian R. ORCID logoORCID: 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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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

Item Type: Conference or Workshop Item (Paper)
Additional Information: Paper published as:
Huyck C., Kreivenas D. (2018) Implementing Rules with Artificial Neurons. In: Bramer M., Petridis M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science, vol 11311. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 24914
Notes on copyright: The final authenticated version is available online at
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Depositing User: Chris Huyck
Date Deposited: 11 Sep 2018 16:12
Last Modified: 29 Nov 2022 19:26

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