In recent years Multi-Electrode Arrays (MEAs) have emerged as a powerful tool to study brain (dys)functions in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with such MEAs generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA, h uncompressed) and inferring meaningful conclusions from them require rigorous and automated processing. To this goal, the current work proposes a cloud-computing based software workflow, QSpikeTools for preliminary preprocessing and analysis of neuronal activities recorded from MEAs with 60 recording sites. It exploits the facilities provided by some open-source tools to delegate CPU-intensive and independent operations to be performed on individual recorded channels (e.g., signal filtering, multi-unit activity detection, spike sorting, etc.) to a multi-core computer or a computer cluster to be executed in parallel. We report that the required time in performing the desired processing and analysis decreases significantly with increasing number of employed cores. With the commercial availability of new, sophisticated, and inexpensive high-density MEAs, we believe that widely dissemination of QSpikeTools may facilitate its adoption and customization, and possibly inspire the creation of community-supported cloud-computing facilities for MEAs users.

QSpikeTools: An open source toolbox for parallel batch processing of extracellular neuronal signals recorded by substrate microelectrode arrays / Mahmud, M.; Pulizzi, R.; Vasilaki, E.; Giugliano, M.. - (2014), pp. 1-6. ((Intervento presentato al convegno 2014 International Conference on Electrical Engineering and Information & Communication Technology tenutosi a Dhaka, Bangladesh nel 10-12 April 2014 [10.1109/ICEEICT.2014.6919177].

QSpikeTools: An open source toolbox for parallel batch processing of extracellular neuronal signals recorded by substrate microelectrode arrays

Giugliano, M.
2014-01-01

Abstract

In recent years Multi-Electrode Arrays (MEAs) have emerged as a powerful tool to study brain (dys)functions in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with such MEAs generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA, h uncompressed) and inferring meaningful conclusions from them require rigorous and automated processing. To this goal, the current work proposes a cloud-computing based software workflow, QSpikeTools for preliminary preprocessing and analysis of neuronal activities recorded from MEAs with 60 recording sites. It exploits the facilities provided by some open-source tools to delegate CPU-intensive and independent operations to be performed on individual recorded channels (e.g., signal filtering, multi-unit activity detection, spike sorting, etc.) to a multi-core computer or a computer cluster to be executed in parallel. We report that the required time in performing the desired processing and analysis decreases significantly with increasing number of employed cores. With the commercial availability of new, sophisticated, and inexpensive high-density MEAs, we believe that widely dissemination of QSpikeTools may facilitate its adoption and customization, and possibly inspire the creation of community-supported cloud-computing facilities for MEAs users.
2014 International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)
1
6
978-147994819-2
10.1109/ICEEICT.2014.6919177
https://ieeexplore.ieee.org/document/6919177
IEEE
Mahmud, M.; Pulizzi, R.; Vasilaki, E.; Giugliano, M.
File in questo prodotto:
File Dimensione Formato  
2014-02-ICEEICT2014-QSpikeTools.pdf

non disponibili

Descrizione: Articolo principale
Tipologia: Versione Editoriale (PDF)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.29 MB
Formato Adobe PDF
1.29 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/98979
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact