Independent-Component-Analysis-Based Spike Sorting Algorithm for High-Density Microelectrode Array Data Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00158838" target="_blank" >RIV/68407700:21230/09:00158838 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Independent-Component-Analysis-Based Spike Sorting Algorithm for High-Density Microelectrode Array Data Processing

Popis výsledku v původním jazyce

Microelectrode arrays (MEAs) become an important tool for neurophysiology research. They are instrumental in revealing neural network formation processes and inter-cell communication schemes, which helps to understand the functioning of the human brain and to treat it's diseases. The electrode pitch of current CMOS-based MEAs can be as low as 18 ?m, which allows for recording the activity of single cells simultaneously on several channels [1]. Each electrode in turn records the activity of several adjacent neurons. The presented algorithm employs Independent Component Analysis (ICA) method to recover the spike signals and to assign them to a particular neuron. To overcome the fundamental ICA requirement of linearly mixed independent sources, which is not satisfied in the case of neuronal recordings, the algorithm runs in a loop, successively extracts traces with spiking activity, overlays those with previously detected ones and assigns signals to individual neurons.

Název v anglickém jazyce

Independent-Component-Analysis-Based Spike Sorting Algorithm for High-Density Microelectrode Array Data Processing

Popis výsledku anglicky

Microelectrode arrays (MEAs) become an important tool for neurophysiology research. They are instrumental in revealing neural network formation processes and inter-cell communication schemes, which helps to understand the functioning of the human brain and to treat it's diseases. The electrode pitch of current CMOS-based MEAs can be as low as 18 ?m, which allows for recording the activity of single cells simultaneously on several channels [1]. Each electrode in turn records the activity of several adjacent neurons. The presented algorithm employs Independent Component Analysis (ICA) method to recover the spike signals and to assign them to a particular neuron. To overcome the fundamental ICA requirement of linearly mixed independent sources, which is not satisfied in the case of neuronal recordings, the algorithm runs in a loop, successively extracts traces with spiking activity, overlays those with previously detected ones and assigns signals to individual neurons.

Druh

D - Stať ve sborníku

CEP obor

JB - Senzory, čidla, měření a regulace

OECD FORD obor

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Projekt

<a href="/cs/project/GD102%2F09%2FH082" target="_blank" >GD102/09/H082: Senzory a inteligentní senzorové systémy</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE SENSORS 2009 - The Eighth IEEE Conference on Sensors

ISBN

978-1-4244-5335-1

ISSN

1930-0395

e-ISSN

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Počet stran výsledku

3

Strana od-do

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Název nakladatele

IEEE Sensors Council

Místo vydání

Christchurch

Místo konání akce

Christchurch

Datum konání akce

25. 10. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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