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Implementing Workflow Strategies to Handle the Analysis of Complex Electrophysiological Data Sets

Filed under:

Michael Denker (Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich), Andrew Davison (Unité de Neurosciences, Information et Complexité (UNIC), CNRS UPR-3293), Markus Diesmann (Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich), Sonja Grün (Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich)

The complexity of managing electrophysiological experiments has grown considerably with the advent of modern recording setups. This complexity is firstly due to the interest in simultaneously recording the activity recorded from large numbers of channels to study the role of concerted neural activity. This scientific focus requires new analysis methods [1] that exploit the parallel aspect of such data sets [2]. A second source of complexity is the interest in increasingly natural stimulus protocols and behavioral responses. As an example, typical visual stimulation has progressed from simple moving bars to natural movies, Gabor noise, apparent motion stimuli. Taken together, this sophistication implies a level of complexity that encourages researchers to rethink their traditional workflows in electrophysiology.
Here, we showcase experiences in establishing good-practice workflows and building corresponding tool-chains to facilitate the handling of electrophysiological data. We demonstrate how we combine and amend various software tools, both generic (e.g., version control systems, parallelization libraries [3]...) and specifically from the neuroinformatics community (e.g., lab journaling systems such as sumatra [4]), to achieve an efficient working style that is flexible, leads to reproducible results, and is open for collaboration.
In parallel to our own efforts, we present results from two initiatives aimed at sampling the current state of maturation of workflows in the electrophysiology community. First, we analyze an on-line survey pinpointing the major problems encountered in the analysis of high-dimensional data sets. Second, we report hands-on insights sampled from several laboratories gained during a workshop on workflows in electrophysiology.
Acknowledgements: Supported by the European Union (FP7-ICT-2009-6, BrainScaleS).
1. Brown EN, Kass RE, Mitra PP: Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 2004, 7:456-461. doi:10.1038/nn1228
2. Stevenson IH, Kording, KP: How advances in neural recording affect data analysis. Nat Neurosci 2011, 14:139-142. doi:10.1038/nn.2731
3. Denker M, Wiebelt B, Fliegner D, Diesmann M, Morrison A: Practically trivial parallel data processing in a neuroscience laboratory. In: Analysis of parallel spike trains. New York: Springer-Verlag; 2010. doi: 10.1007/978-1-4419-5675-0_20
4. Sumatra: automated electronic lab book []
Preferred presentation format: Poster
Topic: Electrophysiology

Filed under:
Andrew Davison
Andrew Davison says:
May 11, 2012 03:12 PM
Nor rating this as I am a co-author.