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. pp. 701-705 doi: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
License
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)doi:10.1109/SAI.2017.8252173
Web address (URL) of conference proceedingshttps://doi.org/10.1109/SAI.2017.8252173
Copyright holder© 2017 IEEE. All rights reserved.
Page range701-705
Permalink -

https://repository.uel.ac.uk/item/87079

  • 11
    total views
  • 7
    total downloads
  • 11
    views this month
  • 6
    downloads this month

Related outputs

Explaining Sentiment Classification
Rajwadi, M., Glackin, C., Wall, J., Chollet, G. and Cannings, N. 2019. Explaining Sentiment Classification. Interspeech 2019. Graz, AT 15 - 19 Sep 2019 International Speech Communication Association. doi:10.21437/Interspeech.2019-2743
Towards a More Representative Definition of Cyber Security
Schatz, Daniel, Bashroush, R. and Wall, J. 2017. Towards a More Representative Definition of Cyber Security. Journal of Digital Forensics, Security and Law. 12 (2), pp. 53-74.
Privacy preserving encrypted phonetic search of speech data
Wall, J., Glackin, C., Chollet, G., Dugan, N., Cannings, N., Tahir, S., Ghosh Ray, I. and Rajarajan, M. 2017. Privacy preserving encrypted phonetic search of speech data. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Louisiana, USA 05 - 09 Mar 2017 IEEE. pp. 6414-6418 doi:10.1109/ICASSP.2017.7953391
Recurrent lateral inhibitory spiking networks for speech enhancement
Wall, J., Glackin, Cornelius, Cannings, Nigel, Chollet, Gerard and Dugan, Nazim 2016. Recurrent lateral inhibitory spiking networks for speech enhancement. in: Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN) IEEE. pp. 1023-1028
Spiking neuron models of the medial and lateral superior olive for sound localisation
Wall, J., McDaid, L.J., Maguire, L.P. and McGinnity, T.M. 2008. Spiking neuron models of the medial and lateral superior olive for sound localisation. IEEE International Joint Conference on Neural Networks (IJCNN) (IEEE World Congress on Computational Intelligence). Hong Kong 01 - 08 Jun 2008 Hong Kong IEEE. pp. 2641-2647 doi:10.1109/IJCNN.2008.4634168
A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics
Wall, J., McGinnity, Thomas M. and Maguire, Liam P. 2011. A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics. Neural Networks (IJCNN), The 2011 International Joint Conference on. San Jose, CA 31 Jul - 05 Aug 2011 IEEE. pp. 1981-1987
Deep Laterally Recurrent Spiking Neural Networks for Speech Enhancement
Wall, J. 2016. Deep Laterally Recurrent Spiking Neural Networks for Speech Enhancement. UEL Computing & Engineering Showcase. London, UK 16 Jun 2016 UEL.
A spiking neural network implementation of sound localisation
Wall, J., McDaid, L.J., Maguire, L.P. and McGinnity, T.M. 2007. A spiking neural network implementation of sound localisation. IET Irish Signals and Systems. Derry, UK 13 - 14 Sep 2007 Derry, UK pp. 1-5
Using the interaural time difference and cross-correlation to localise short-term complex noises
Wall, J., McGinnity, Martin and Maguire, Liam 2011. Using the interaural time difference and cross-correlation to localise short-term complex noises. Artificial Intelligence and Cognitive Science (AICS). Derry, UK 31 Aug - 02 Sep 2011 University of Ulster, Intelligent Systems Research Centre.
A Framework for Realistic 3D Tele-Immersion
Fechteler, P., Hilsmann, A., Eisert, P., Broeck, S.V., Stevens, C., Wall, J., Sanna, M., Mauro, D.A., Kuijk, F., Mekuria, R., Cesar, P., Monaghan, D., O'Connor, N.E., Daras, P., Alexiadis, D. and Zahariadis, T. 2013. A Framework for Realistic 3D Tele-Immersion. 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications. Berlin, Germany 2013 New York, NY, USA ACM. pp. 1-8 doi:10.1145/2466715.2466718
Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding
Wall, J. and Glackin, Cornelius 2013. Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding. Frontiers Media SA.
A Roadmap for Privacy Preserving Speech Processing
Wall, J., Glackin, C., Chollet, G., Dugan, N., Cannings, N., Tahir, S., Ghosh Ray, I, Rajarajan, M., Falkner, R. and Badii, A. 2016. A Roadmap for Privacy Preserving Speech Processing. Preserving Privacy in an Age of Increased Surveillance – A Biometrics Perspective. London, UK 17 - 17 Oct 2016
Post-Cochlear Auditory Modelling for Sound Localisation using Bio-Inspired Techniques
Wall, J. 2010. Post-Cochlear Auditory Modelling for Sound Localisation using Bio-Inspired Techniques. PhD Thesis University of Ulster Faculty of Computing and Engineering
Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds
Wall, J., Izquierdo, E. and Zhang, Q. 2013. Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds. in: Proceedings 2013 18th International Conference on Digital Signal Processing (DSP) IEEE. pp. 1-6
Advancements and Challenges towards a Collaborative Framework for 3D Tele-Immersive Social Networking
Mauro, D.A., O'Connor, N.E., Monaghan, D., Gowing, M., Fechteler, P., Eisert, P., Wall, J., Izquierdo, E., Alexiadis, D.S., Daras, P., Mekuria, R. and Cesar, P. 2013. Advancements and Challenges towards a Collaborative Framework for 3D Tele-Immersive Social Networking. 4th IEEE International Workshop on Hot Topics in 3D (Hot3D). San Jose, CA, USA 15 Jul 2013 IEEE. pp. 1-2
A Framework for Human-like Behavior in an Immersive Virtual World
Kuijk, Fons, Van Broeck, Sigurd, Dareau, Claude, Ravenet, Brian, Ochs, Magalie, Apostolakis, Konstantinos, Daras, Petros, Monaghan, David, O'Connor, Noel E, Wall, J. and Izquierdo, Ebroul 2013. A Framework for Human-like Behavior in an Immersive Virtual World. in: Proceedings of 2013 18th International Conference on Digital Signal Processing (DSP) IEEE. pp. 1-7
REVERIE: Natural Human Interaction in Virtual Immersive Environments
Wall, J., Izquierdo, Ebroul, Argyriou, Lemonia, Monaghan, David S., O'Connor, Noel E., Poulakos, Steven, Smolic, Aljoscha and Mekuria, Rufael 2014. REVERIE: Natural Human Interaction in Virtual Immersive Environments. in: 2014 IEEE International Conference on Image Processing (ICIP) IEEE. pp. 2165-2167
A Methodological Approach to User Evaluation and Assessment of a Virtual Environment Hangout
Pasin, Marco, Frisiello, Antonella, Wall, J., Poulakos, Steven and Smolic, Aljoscha 2015. A Methodological Approach to User Evaluation and Assessment of a Virtual Environment Hangout. in: Sanna, Andrea, Lamberti, Fabrizio, Rokne, Jon and Gatteschi, Valentina (ed.) Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment EAI. pp. 1-5
Playing immersive games on the REVERIE platform
Doumanis, Ioannis, Wall, J. and Monaghan, David S. 2015. Playing immersive games on the REVERIE platform. in: Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM) IEEE. pp. 1572-1577
Spiking neural network model of sound localisation using the interaural intensity difference
Wall, J., McDaid, Liam J., Maguire, Liam P. and McGinnity, Thomas M. 2012. Spiking neural network model of sound localisation using the interaural intensity difference. IEEE Transactions on Neural Networks. 23 (4), pp. 574-586.
Perception-based Modelling of System Behaviour
Wall, J. 2006. Perception-based Modelling of System Behaviour. Proc. of the IEEE Systems, Man and Cybernetics Society.
A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation
Glackin, B., Wall, J., McGinnity, T.M., Maguire, L.P. and McDaid, L.J. 2010. A Spiking Neural Network Model of the Medial Superior Olive using Spike Timing Dependent Plasticity for Sound Localisation. Frontiers in Computational Neuroscience. 4 (18), pp. 1-16.
Spiking neural network connectivity and its potential for temporal sensory processing and variable binding
Wall, J. and Glackin, Cornelius 2013. Spiking neural network connectivity and its potential for temporal sensory processing and variable binding. Frontiers in Computational Neuroscience. 7 (182), pp. 1-2.