Neo: an object model for handling electrophysiology data in multiple formats

被引:98
作者
Garcia, Samuel [1 ]
Guarino, Domenico [2 ]
Jaillet, Florent [3 ]
Jennings, Todd [4 ]
Proepper, Robert [5 ]
Rautenberg, Philipp L. [6 ]
Rodgers, Chris C. [7 ]
Sobolev, Andrey [6 ]
Wachtler, Thomas [6 ]
Yger, Pierre [2 ]
Davison, Andrew P. [2 ]
机构
[1] Univ Lyon 1, CNRS UMR5292, INSERM U1028, Ctr Rech Neurosci Lyon, Lyon, France
[2] CNRS, UPR 3293, Neuroinformat Grp, Unite Neurosci Informat & Complexite, F-91198 Gif Sur Yvette, France
[3] Aix Marseille Univ, CNRS, Inst Neurosci Timone, UMR 7289, Marseille, France
[4] Univ Munich, Dept Biol 2, Div Neurobiol, Planegg Martinsried, Germany
[5] TU Berlin, Neural Informat Proc Grp, Berlin, Germany
[6] Univ Munich, Dept Biol 2, G Node, Planegg Martinsried, Germany
[7] Univ Calif Berkeley, Helen Wills Neurosci Inst, Berkeley, CA 94720 USA
关键词
electrophysiology; interoperability; !text type='Python']Python[!/text; software; file formats; SIGNALML; PLATFORM; EXCHANGE; !text type='PYTHON']PYTHON[!/text;
D O I
10.3389/fninf.2014.00010
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.
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页数:10
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