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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.

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Title Multivariate autoregressive-based neuronal network flow analysis for in-vitro recorded bursts
Author(s) Hettiarachchi, Imali T.ORCID iD for Hettiarachchi, Imali T. orcid.org/0000-0002-4220-0970
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Adlard, Paul A.
Nahavandi, SaeidORCID iD for 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 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
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