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Inferring the location of neurons within an artificial network from their activity

Dyer, Alexander J; Griffin, Lewis D; (2023) Inferring the location of neurons within an artificial network from their activity. Neural Networks , 157 pp. 160-175. 10.1016/j.neunet.2022.10.012. Green open access

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Abstract

Inferring the connectivity of biological neural networks from neural activation data is an open problem. We propose that the analogous problem in artificial neural networks is more amenable to study and may illuminate the biological case. Here, we study the specific problem of assigning artificial neurons to locations in a network of known architecture, specifically the LeNet image classifier. We evaluate a supervised learning approach based on features derived from the eigenvectors of the activation correlation matrix. Experiments highlighted that for an image dataset to be effective for accurate localisation, it should fully activate the network and contain minimal confounding correlations. No single image dataset was found that resulted in perfect assignment, however perfect assignment was achieved using a concatenation of features from multiple image datasets.

Type: Article
Title: Inferring the location of neurons within an artificial network from their activity
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neunet.2022.10.012
Publisher version: https://doi.org/10.1016/j.neunet.2022.10.012
Language: English
Additional information: Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Artificial neural networks, Network inference, Supervised learning, Correlation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10160795
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