Multivariate autoregressive-based neuronal network flow analysis for in-vitro recorded bursts Hettiarachchi, Imali T., Bhatti, Asim, Adlard, Paul A. and Nahavandi, Saeid 2015, Multivariate autoregressive-based neuronal network flow analysis for in-vitro recorded bursts, in ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings, Springer, Berlin, Germany, pp. 324-331, doi: 10.1007/978-3-319-26561-2_39. Attached Files Name Description MIMEType Size Downloads Title Multivariate autoregressive-based neuronal network flow analysis for in-vitro recorded bursts Author(s) Hettiarachchi, Imali T. orcid.org/0000-0002-4220-0970 Bhatti, Asim orcid.org/0000-0001-6876-1437 Adlard, Paul A. 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 ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings Editor(s) Arik, Sabri Huang, Tingwen Lin, Weng Kin Liu, Qingshan Publication date 2015 Series Lecture Notes in Computer Science; 9492 Start page 324 End page 331 Total pages 8 Publisher Springer Place of publication Berlin, Germany Summary Neuroscientific studies of in vitro neuron cell cultures has attracted paramount attention to investigate the behaviour of neuronal networks in response to different environmental conditions and external stimuli such as drugs, optical and electrical stimulations. Microelec trodearray (MEA) technology has been widely adopted as a tool for this investigation. In this work, we present a new approach to estimate interconnectivity of neural spikes using multivariate autoregressive (MVAR) analysis and Partial Directed Coherence (PDC). The proposed approach has the potential to discover hidden intra-burst causal connectivity patterns and to help understand the spatiotemporal communication patterns within bursts, pre and post stimulations. ISBN 9783319265605 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-26561-2_39 Field of Research 090609 Signal Processing 08 Information And Computing Sciences Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30080686 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   Connect to Elements publication management system 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 Tue, 12 Jan 2016, 13:11:05 EST Wed, 13 Jan 2016, 04:00:53 EST Mon, 18 Jan 2016, 07:32:13 EST Thu, 31 Mar 2016, 13:46:49 EST Fri, 01 Apr 2016, 05:07:21 EST Wed, 27 Apr 2016, 11:10:44 EST Wed, 27 Apr 2016, 11:14:08 EST Mon, 18 Jul 2016, 15:55:13 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 148 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 31 Mar 2016, 13:46:49 EST

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