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Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm Behaviour

Li, K; Javer, A; Keaveny, EE; Brown, AEX; (2017) Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm Behaviour. In: Proceedings of the NIPS 2017 Workshop on Worm's Neural Information Processing (WNIP). Green open access

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Abstract

An important goal in behaviour analytics is to connect disease state or genome variation with observable differences in behaviour. Despite advances in sensor technology and imaging, informative behaviour quantification remains challenging. The nematode worm C. elegans provides a unique opportunity to test analysis approaches because of its small size, compact nervous system, and the availability of large databases of videos of freely behaving animals with known genetic differences. Despite its relative simplicity, there are still no reports of generative models that can capture essential differences between even well-described mutant strains. Here we show that a multilayer recurrent neural network (RNN) can produce diverse behaviours that are difficult to distinguish from real worms’ behaviour and that some of the artificial neurons in the RNN are interpretable and correlate with observable features such as body curvature, speed, and reversals. Although the RNN is not trained to perform classification, we find that artificial neuron responses provide features that perform well in worm strain classification.

Type: Proceedings paper
Title: Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm Behaviour
Event: NIPS 2017 Workshop on Worm's Neural Information Processing (WNIP)
Location: Los Angeles (CA), USA
Dates: 8th December 2017
Open access status: An open access version is available from UCL Discovery
Publisher version: https://sites.google.com/site/wwnip2017/home
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10089847
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