Independent Component Analysis Based Single Cell Activity Discrimination Algorithm for Multielectrode Array Sensor Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00166940" target="_blank" >RIV/68407700:21230/10:00166940 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Independent Component Analysis Based Single Cell Activity Discrimination Algorithm for Multielectrode Array Sensor Data
Original language description
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 presented algorithm employs a modified Independent Component Analysis (ICA) method to recover the spike signals from recorded data 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.
Czech name
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Czech description
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Project
<a href="/en/project/GD102%2F09%2FH082" target="_blank" >GD102/09/H082: Sensors and intelligent sensor systems</a><br>
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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