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Protein structured reservoir computing for spike-based pattern recognition

Tsakalos, Karolos-Alexandros; Ch. Sirakoulis, Georgios; Adamatzky, Andrew; Smith, Jim

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Authors

Karolos-Alexandros Tsakalos

Georgios Ch. Sirakoulis

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Jim Smith James.Smith@uwe.ac.uk
Professor in Interactive Artificial Intelligence



Abstract

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing a reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a ‘hardware’ architecture of the communication networks connecting the processors. We apply on a single readout, layer various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches.

Citation

Tsakalos, K., Ch. Sirakoulis, G., Adamatzky, A., & Smith, J. (2022). Protein structured reservoir computing for spike-based pattern recognition. IEEE Transactions on Parallel and Distributed Systems, 33(2), 322 - 331. https://doi.org/10.1109/TPDS.2021.3068826

Journal Article Type Article
Acceptance Date Mar 9, 2021
Online Publication Date Mar 26, 2021
Publication Date Feb 1, 2022
Deposit Date Apr 21, 2021
Publicly Available Date Mar 29, 2024
Journal IEEE Transactions on Parallel and Distributed Systems
Print ISSN 1045-9219
Electronic ISSN 1558-2183
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 33
Issue 2
Pages 322 - 331
DOI https://doi.org/10.1109/TPDS.2021.3068826
Keywords Index Terms-Molecular networks; Reservoir Computing; Liq- uid State Machine; Izhikevich Model; Remote Supervised Learn- ing; Pattern Recognition
Public URL https://uwe-repository.worktribe.com/output/7278387

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