QSpikeTools : an open source toolbox for parallel batch processing of extracellular neuronal signals recorded
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
New york :Ieee
2014 1ST INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATION & COMMUNICATION TECHNOLOGY (ICEEICT 2014)
1st International Conference on Electrical Engineering and Information, and Communication Technology (ICEEICT), APR 10-12, 2014, Dhaka, BANGLADESH
, 6 p.
University of Antwerp
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.