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Implementing Rules with Artificial Neurons

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Artificial Intelligence XXXV (SGAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11311))

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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 support further work in cognitive modelling and cognitive architecture.

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Acknowledgments

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 720270 (the Human Brain Project).

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Authors and Affiliations

  1. Middlesex University, London, NW4 4BT, UK

    Christian Huyck & Dainius Kreivenas

Authors
  1. Christian Huyck
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  2. Dainius Kreivenas
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Correspondence to Christian Huyck .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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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. https://doi.org/10.1007/978-3-030-04191-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-04191-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04190-8

  • Online ISBN: 978-3-030-04191-5

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