Parallel calculation of multi-electrode array correlation networks

When calculating correlation networks from multi-electrode array (MEA) data, one works with extensive computations. Unfortunately, as the MEAs grow bigger, the time needed for the computation grows even more: calculating pair-wise correlations for current 60 channel systems can take hours on normal...

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Bibliographic Details
Published in:Journal of neuroscience methods 2009-11, Vol.184 (2), p.357-364
Main Authors: Ribeiro, Pedro, Simonotto, Jennifer, Kaiser, Marcus, Silva, Fernando
Format: Article
Language:English
Subjects:
Algorithms
Animals
Computer Communication Networks
Computer Simulation
Condor
Correlation networks
Electrodes, Implanted
Electrophysiology - methods
Humans
Image Processing, Computer-Assisted
Microelectrodes
Neuroimaging
Neurophysiology - methods
Optical imaging
Parallel computing
Signal Processing, Computer-Assisted
Software
Time series analysis
Voltage-Sensitive Dye Imaging
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Summary:When calculating correlation networks from multi-electrode array (MEA) data, one works with extensive computations. Unfortunately, as the MEAs grow bigger, the time needed for the computation grows even more: calculating pair-wise correlations for current 60 channel systems can take hours on normal commodity computers whereas for future 1000 channel systems it would take almost 280 times as long, given that the number of pairs increases with the square of the number of channels. Even taking into account the increase of speed in processors, soon it can be unfeasible to compute correlations in a single computer. Parallel computing is a way to sustain reasonable calculation times in the future. We provide a general tool for rapid computation of correlation networks which was tested for: (a) a single computer cluster with 16 cores, (b) the Newcastle Condor System utilizing idle processors of university computers and (c) the inter-cluster, with 192 cores. Our reusable tool provides a simple interface for neuroscientists, automating data partition and job submission, and also allowing coding in any programming language. It is also sufficiently flexible to be used in other high-performance computing environments.
ISSN:0165-0270
1872-678X
DOI:10.1016/j.jneumeth.2009.08.001