Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes

Veerabhadrappa, Rakesh, Bhatti, Asim, Berk, Michael, Tye, Susannah J and Nahavandi, Saeid 2017, Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes, Neurocomputing, vol. 249, pp. 299-313, doi: 10.1016/j.neucom.2016.09.135.

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Title Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes
Author(s) Veerabhadrappa, Rakesh
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Berk, MichaelORCID iD for Berk, Michael orcid.org/0000-0002-5554-6946
Tye, Susannah J
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Neurocomputing
Volume number 249
Start page 299
End page 313
Total pages 15
Publisher Elsevier BV
Place of publication Amsterdam, The Netherlands
Publication date 2017-08-02
ISSN 0925-2312
Keyword(s) Spike sorting
Temporally synchronous spikes
Neural activity analysis
Summary Extracellular recording from living neurons employing microelectrode arrays has attracted paramount attention in recent years as a way to investigate the functionality and disorders of the brain. To decipher useful information from the recorded signals, accurate and efficient neural spike activity detection and sorting becomes an essential prerequisite. Traditional approaches rely on thresholding to detect individual spikes and clustering to identify subset groups; however, these methods fail to identify temporally synchronous spikes due to neuronal synchrony. To address this challenge, we introduce a novel spike sorting algorithm incorporating both quantitative and probabilistic techniques to better approximate the ground truth information of the spike activity. A novel pre-clustering method for identifying key features that can form natural clusters and a dimension reduction technique for identifying the spiking activity are introduced. To address the temporal neuronal synchrony phenomenon leading to detection of multineural overlapped spikes, a procedure for template spike shape estimation and iterative recognition is developed employing the cross correlation methodology tailored to individual neuron's spike rate. A performance comparison between the proposed method and existing techniques in terms of the number of spikes identified and efficiency of sorting the spikes is presented. The outcome shows the effectiveness of the proposed method in identifying temporally synchronous spikes.
Language eng
DOI 10.1016/j.neucom.2016.09.135
Field of Research 099999 Engineering not elsewhere classified
08 Information And Computing Sciences
09 Engineering
17 Psychology And Cognitive Sciences
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2017, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089587

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