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. Attached Files Name Description MIMEType Size Downloads Title Statistical modelling of artificial neural network for sorting temporally synchronous spikes Author(s) Veerabhadrappa, Rakesh Bhatti, Asim orcid.org/0000-0001-6876-1437 Lim, Chee Peng orcid.org/0000-0003-4191-9083 Nguyen, Thanh Thi orcid.org/0000-0001-9709-1663 Tye, S.J. Monaghan, Paul Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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 Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 15 Jan 2016, 15:11:53 EST Fri, 15 Jan 2016, 15:12:24 EST Sat, 16 Jan 2016, 04:01:20 EST Mon, 18 Jan 2016, 07:32:53 EST Tue, 08 Mar 2016, 08:22:08 EST Wed, 06 Apr 2016, 08:16:07 EST Tue, 26 Apr 2016, 14:05:12 EST Wed, 27 Apr 2016, 06:09:01 EST Thu, 19 May 2016, 10:55:58 EST Thu, 19 May 2016, 11:00:36 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 257 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Tue, 26 Apr 2016, 14:05:12 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.