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Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project

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Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project. / Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim.

In: Neural Networks, Vol. 21, No. 8, 10.2008, p. 1076-1084.

Research output: Contribution to journalArticle

Harvard

Fletcher, M, Liang, B, Smith, L, Knowles, A, Jackson, T, Jessop, M & Austin, J 2008, 'Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project', Neural Networks, vol. 21, no. 8, pp. 1076-1084. https://doi.org/10.1016/j.neunet.2008.06.009

APA

Fletcher, M., Liang, B., Smith, L., Knowles, A., Jackson, T., Jessop, M., & Austin, J. (2008). Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project. Neural Networks, 21(8), 1076-1084. https://doi.org/10.1016/j.neunet.2008.06.009

Vancouver

Fletcher M, Liang B, Smith L, Knowles A, Jackson T, Jessop M et al. Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project. Neural Networks. 2008 Oct;21(8):1076-1084. https://doi.org/10.1016/j.neunet.2008.06.009

Author

Fletcher, Martyn ; Liang, Bojian ; Smith, Leslie ; Knowles, Alastair ; Jackson, Tom ; Jessop, Mark ; Austin, Jim. / Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project. In: Neural Networks. 2008 ; Vol. 21, No. 8. pp. 1076-1084.

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@article{e8db58878f6c45d38dedddb4471d9c3c,
title = "Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project",
abstract = "In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets. (C) 2008 Elsevier Ltd. All rights reserved.",
keywords = "Grid, Pattern matching, Neural networks, Spike detection, Neuroinformatics",
author = "Martyn Fletcher and Bojian Liang and Leslie Smith and Alastair Knowles and Tom Jackson and Mark Jessop and Jim Austin",
year = "2008",
month = "10",
doi = "10.1016/j.neunet.2008.06.009",
language = "English",
volume = "21",
pages = "1076--1084",
journal = "Neural Networks",
issn = "0893-6080",
publisher = "Elsevier Limited",
number = "8",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Neural network based pattern matching and spike detection tools and services - in the CARMEN neuroinformatics project

AU - Fletcher, Martyn

AU - Liang, Bojian

AU - Smith, Leslie

AU - Knowles, Alastair

AU - Jackson, Tom

AU - Jessop, Mark

AU - Austin, Jim

PY - 2008/10

Y1 - 2008/10

N2 - In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets. (C) 2008 Elsevier Ltd. All rights reserved.

AB - In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets. (C) 2008 Elsevier Ltd. All rights reserved.

KW - Grid

KW - Pattern matching

KW - Neural networks

KW - Spike detection

KW - Neuroinformatics

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U2 - 10.1016/j.neunet.2008.06.009

DO - 10.1016/j.neunet.2008.06.009

M3 - Article

VL - 21

SP - 1076

EP - 1084

JO - Neural Networks

JF - Neural Networks

SN - 0893-6080

IS - 8

ER -