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

R. Veerabhadrappa, A. Bhatti, Michael Berk, Susannah J Tye, S. Nahavandi

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
JournalNeurocomputing
DOIs
StateAccepted/In press - Aug 20 2015

Fingerprint

Sorting
Neurons
Microelectrodes
Cluster Analysis
Brain
Brain Diseases

Keywords

  • Neural activity analysis
  • Spike sorting
  • Temporally synchronous spikes

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes. / Veerabhadrappa, R.; Bhatti, A.; Berk, Michael; Tye, Susannah J; Nahavandi, S.

In: Neurocomputing, 20.08.2015.

Research output: Contribution to journalArticle

Veerabhadrappa, R. ; Bhatti, A. ; Berk, Michael ; Tye, Susannah J ; Nahavandi, S. / Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes. In: Neurocomputing. 2015.
@article{46d3e56e00a4490e8e20c8af734d1cd6,
title = "Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes",
abstract = "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.",
keywords = "Neural activity analysis, Spike sorting, Temporally synchronous spikes",
author = "R. Veerabhadrappa and A. Bhatti and Michael Berk and Tye, {Susannah J} and S. Nahavandi",
year = "2015",
month = "8",
day = "20",
doi = "10.1016/j.neucom.2016.09.135",
language = "English (US)",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

TY - JOUR

T1 - Hierarchical estimation of neural activity through explicit identification of temporally synchronous spikes

AU - Veerabhadrappa, R.

AU - Bhatti, A.

AU - Berk, Michael

AU - Tye, Susannah J

AU - Nahavandi, S.

PY - 2015/8/20

Y1 - 2015/8/20

N2 - 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.

AB - 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.

KW - Neural activity analysis

KW - Spike sorting

KW - Temporally synchronous spikes

UR - http://www.scopus.com/inward/record.url?scp=85017222502&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017222502&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2016.09.135

DO - 10.1016/j.neucom.2016.09.135

M3 - Article

AN - SCOPUS:85017222502

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -