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Towards an Accurate Identification of Pyloric Neuron Activity with VSDi

dos Santos, Filipa; Andras, Peter; Lam, K.P.

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Authors

Filipa dos Santos

Peter Andras



Abstract

Voltage-sensitive dye imaging (VSDi) which enables simultaneous optical recording of many neurons in the pyloric circuit of the stomatogastric ganglion is an important technique to supplement electrophysiological recordings. However, utilising the technique to identify pyloric neurons directly is a computationally exacting task that requires the development of sophisticated signal processing procedures to analyse the tri-phasic pyloric patterns generated by these neurons. This paper presents our work towards commissioning such procedures. The results achieved to date are most encouraging.

Citation

dos Santos, F., Andras, P., & Lam, K. (2017). Towards an Accurate Identification of Pyloric Neuron Activity with VSDi. In Artificial Neural Networks and Machine Learning – ICANN 2017. https://doi.org/10.1007/978-3-319-68600-4_15

Conference Name 26th International Conference on Artificial Neural Networks
Conference Location Alghero, Sardinia, Italy
Start Date Sep 11, 2017
End Date Sep 15, 2017
Acceptance Date May 18, 2017
Online Publication Date Oct 24, 2017
Publication Date Oct 25, 2017
Publicly Available Date May 26, 2023
Publisher Springer
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743; 1611-3349
Edition 1
Book Title Artificial Neural Networks and Machine Learning – ICANN 2017
ISBN 978-3-319-68599-1
DOI https://doi.org/10.1007/978-3-319-68600-4_15
Keywords Duty Cycle; Tri-phasic Pyloric Neural Network; Voltage-Sensitive Dye Imaging; Singular Spectrum Analysis; Dynamic phase detection
Publisher URL https://link.springer.com/chapter/10.1007/978-3-319-68600-4_15
Related Public URLs https://doi.org/10.1007/978-3-319-68600-4_15
https://link.springer.com/book/10.1007/978-3-319-68600-4

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