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Statistical modelling of artificial neural network for sorting temporally synchronous spikes

Veerabhadrappa, Rakesh, Bhatti, Asim, Lim, Chee Peng, Nguyen, Thanh Thi, Tye, S.J., Monaghan, Paul and Nahavandi, Saeid 2015, Statistical modelling of artificial neural network for sorting temporally synchronous spikes, in 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III, Springer, New York, N.Y., pp. 261-272, doi: 10.1007/978-3-319-26555-1_30.

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Title Statistical modelling of artificial neural network for sorting temporally synchronous spikes
Author(s) Veerabhadrappa, Rakesh
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Nguyen, Thanh ThiORCID iD for Nguyen, Thanh Thi orcid.org/0000-0001-9709-1663
Tye, S.J.
Monaghan, Paul
Nahavandi, Saeid
Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings Part III
Publication date 2015
Series Neural Information Processing v.9491
Start page 261
End page 272
Total pages 12
Publisher Springer
Place of publication New York, N.Y.
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
RECORDINGS
SEPARATION
CELLS
Summary Artificial neural network (ANN) models are able to predict future events based on current data. The usefulness of an ANN lies in the capacity of the model to learn and adjust the weights following previous errors during training. In this study, we carefully analyse the existing methods in neuronal spike sorting algorithms. The current methods use clustering as a basis to establish the ground truths, which requires tedious procedures pertaining to feature selection and evaluation of the selected features. Even so, the accuracy of clusters is still questionable. Here, we develop an ANN model to specially address the present drawbacks and major challenges in neuronal spike sorting. New enhancements are introduced into the conventional backpropagation ANN for determining the network weights, input nodes, target node, and error calculation. Coiflet modelling of noise is employed to enhance the spike shape features and overshadow noise. The ANN is used in conjunction with a special spiking event detection technique to prioritize the targets. The proposed enhancements are able to bolster the training concept, and on the whole, contributing to sorting neuronal spikes with close approximations.
ISBN 9783319265544
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-26555-1_30
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
110999 Neurosciences not elsewhere classified
08 Information And Computing Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080751

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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