Rapid Recognition of Olfactory Scenes with a Portable MOx Sensor System using Hotplate Modulation

© 2022 IEEE. Personal 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. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ISOEN54820.2022.9789654A café, the metro, a supermarket, a book store - many locations of everyday life have a specific smell. Recognising such olfactory scenes could inform personal activity tracking, environmental monitoring, and assist robotic navigation. Yet it is unclear if current Metal-oxide (MOx) sensor technology is sensitive and specific enough to achieve this. Factors like sensor drift, and sensitivity to ambient humidity and temperature further complicate the recognition of olfactory scenes. Hotplate temperature modulation has been suggested as a method to counter these drawbacks. We present an electronic nose based on MEMS-MOx sensors that support rapid hotplate temperature modulation with a 150 ms period. We recorded different natural olfactory scenes in an urban context. A linear SVM was able to recognise four olfactory scenes in single hotplate cycles with near-perfect performance when trained and tested on the same day, and 73% accuracy when tested in the same locations on the next day. Gas sensor responses yielded higher recognition accuracy than humidity, temperature, and pressure, which were also partly-location specific. Our results indicate that hotplate modulation enables recognition of natural odor scenes across extended timespans. These findings encourage the use of MOx-sensors as rapid sensing devices in natural, uncontrolled environments

Sparse Coding with a Somato-Dendritic Rule

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.Cortical neurons are silent most of the time. This sparse activity is energy efficient, and the resulting neural code has favourable properties for associative learning. Most neural models of sparse coding use some form of homeostasis to ensure that each neuron fires infrequently. But homeostatic plasticity acting on a fast timescale may not be biologically plausible, and could lead to catastrophic forgetting in embodied agents that learn continuously. We set out to explore whether inhibitory plasticity could play that role instead, regulating both the population sparseness and the average firing rates. We put the idea to the test in a hybrid network where rate-based dendritic compartments integrate the feedforward input, while spiking somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings con-firm that intrinsic plasticity is not strictly required for regulating sparseness: inhibitory plasticity can have the same effect, although that mechanism comes with its own stability-plasticity dilemma. Going beyond point neuron models, the network illustrates how a learning rule can make use of dendrites and compartmentalised inputs; it also suggests a functional interpretation for clustered somatic inhibition in cortical neurons.Peer reviewe

Dynamic observer: ion channel measurement beyond voltage clamp

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Rapid Inference of Geographical Location with an Event-based Electronic Nose

© 2022 The Author(s). This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1145/3517343.3517381Sensory information determines successful interaction of an agent with its environment. Animals have been shown to be able to encode rapid fluctuations in odour plumes, but this has yet received little attention in electronic gas sensing. State-of-the-art gas sensors actively modify the sensing site using temperature modulation, which decreases the integration time and increases the discriminability. In this work, we propose a novel approach for asynchronous event sampling for temperature-modulated gas sensor data and investigate the effectiveness of different event encoding schemes for solving an inference problem. A multichannel heater-modulated electronic nose was used to record field data at 1kHz. The data consisted of an approx. 90-minute walk in Lisbon covering multiple chemical environments and olfactory sceneries. Single temperature-cycle sensor conductance windows of 140ms were normalised and a model curve was subtracted from each sensor response. Using send-on-delta sampling, on- and off-events were generated and further compressed by considering either their rate, time-to-first-spike, firing-order, or a reconstructed signal. The different representations were assessed by their performance in inferring geographical location on unseen single 140 ms cycles using a linear SVM (75%/25% training/test split). We found that with a small spiking threshold the event-reconstructed signal achieved 82.5±1.0% accuracy, very close to the raw data (84.2±1.2%), and gracefully degraded when reducing the event count by increasing the spike threshold. The rate, latency and rank-order codes could not match that of the reconstructed signal, suggesting that temporal dynamics of the intra-cycle signal contain essential information. We conclude that heater-modulated gas sensors lend themselves to event-based processing, allowing for rapid inference in the sub-second regime. Our work could pave the way from distinguishing broad olfactory scenes to recognising individual odorants in turbulent plumes, with the potential to break new ground in traditional and neuromorphic gas sensing

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Sustainable computational science: the ReScience initiative

Computer science o ers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel con dent their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. e actual scholarship is the full so ware environment, code, and data that produced the result. is implies new work ows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically di erent from other traditional scienti c journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and so ware tests

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022