Solving the Linearly Inseparable XOR Problem with Spiking Neural Networks

Conference paper

Wall, J. and Reljan-Delaney, M. 2017. Solving the Linearly Inseparable XOR Problem with Spiking Neural Networks . SAI Computing Conference 2017. London, UK 18 - 20 Jul 2017 IEEE. https://doi.org/10.1109/SAI.2017.8252173
AuthorsWall, J. and Reljan-Delaney, M.
TypeConference paper
Abstract

Spiking Neural Networks (SNN) are third generation neural networks and are considered to be the most biologically plausible so far. As a relative newcomer to the field of artificial learning, SNNs are still exploring their own capabilities, as well as dealing with the singular challenges that arise from attempting to be computationally applicable and biologically accurate. This paper explores the possibility of a different approach to solving linearly inseparable problems by using networks of spiking neurons. To this end two experiments were conducted. The first experiment was an attempt in creating a spiking neural network that would mimic the functionality of logic gates. The second experiment relied on the addition of receptive fields in order to filter the input. This paper demonstrates that a network of spiking neurons utilizing receptive fields or routing can successfully solve the XOR linearly inseparable problem.

Year2017
ConferenceSAI Computing Conference 2017
PublisherIEEE
Accepted author manuscript
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Publication dates
Online11 Jan 2018
Publication process dates
Deposited04 Oct 2019
Book titleProceedings of Computing Conference 2017
ISBN978-1-5090-5443-5
978-1-5090-5444-2
Digital Object Identifier (DOI)https://doi.org/10.1109/SAI.2017.8252173
Web address (URL)https://doi.org/10.1109/SAI.2017.8252173
Copyright holder© 2017 IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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