Karolos-Alexandros Tsakalos
Protein structured reservoir computing for spike-based pattern recognition
Tsakalos, Karolos-Alexandros; Ch. Sirakoulis, Georgios; Adamatzky, Andrew; Smith, Jim
Authors
Georgios Ch. Sirakoulis
Andrew Adamatzky Andrew.Adamatzky@uwe.ac.uk
Professor
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.
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 | Apr 21, 2021 |
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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