Usability and functionality of NeuroML description language evaluated using three distinct spiking neuron models
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1
Tampere University of Technology, Signal Processing, Finland
Computational models are increasingly used in the exploration and interpretation of complex phenomena of the brain and peripheral nervous system. The growing number of molecular, cellular and multiscale neural network models creates a need for straightforward transfer methods of models between simulators. This is accomplished with standard languages for simulator-independent model description, such as the Neural Open Markup Language (NeuroML) [1, 2] and NineML [3]. Description languages are expected to ease model sharing and facilitate the replication of results across different simulators. It is therefore of great interest to test the interoperability between model description languages and neural simulators, employing use cases that are commonly in the computational neuroscience community.
In this study, version 2.0 of the XML based NeuroML was studied and evaluated using a Windows operating system. Additionally, other description languages in neuroinformatics were examined and qualitatively compared to NeuroML. NeuroML implementations of the Hindmarsh-Rose [5], Izhikevich [6], and FitzHugh-Nagumo [7] spiking neuron models were analyzed and their conversion to the format used by the popular neural simulator NEURON [4] was performed. The entire conversion process is illustrated in Figure 1. Finally, a comparison of the models to corresponding reference implementations in the Matlab numerical computing environment was done.
Although some features were found to be malfunctioning when converting the models into the NEURON simulator format, the results demonstrate the power and ease of use of the latest version of NeuroML. Both the regular and chaotically behaving Hindmarsh-Rose models were transferred perfectly, with simulation results matching the reference implementation, while other models encountered some complications in the conversion process that could not be easily resolved. The usability of the tools was found relatively straightforward for a computer-oriented user; however, a biologically-trained person may have difficulties in using the tools.
This work is one of the few studies to quantitatively evaluate the performance, usability and functionality of NeuroML in the context of spiking neuron models. The present study showed the potential of the NeuroML description language in transferring models of neural systems between simulators, while also recognizing the need for an ecosystem of standard languages in the field of neuroscience. Eventually, standard languages will allow for faster and wider development of modeling and simulation software, leading to graphical tools that are accessible from all fields of neuroscience.
References
[1] N. Goddard et al. Philosophical Transactions of the Royal Society, 356:1209–1228, August 2001.
[2] P. Gleeson et al. PLoS Computational Biology, 6, June 2010.
[3] I. Raikov et al. BMC Neuroscience, 12, July 2011.
[4] M. L. Hines and N. T. Carnevale. Neural Computation, 9:1179–1209, August 1997.
[5] J. L. Hindmarsh and R. M. Rose. Proceedings of the Royal Society of London, series B, 221:87–102, March 1984.
[6] E.M. Izhikevich. IEEE Transactions on Neural Networks, 14:1569–1572, 2003.
[7] R. FitzHugh. Biophysical Journal, 1:445–466, July 1961.
Keywords:
computational neuroscience,
neuroinformatics,
data sharing,
databases,
model de,
XML-based language,
Standards
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
General neuroinformatics
Citation:
Lehtimäki
M and
Linne
M
(2014). Usability and functionality of NeuroML description language evaluated using three distinct spiking neuron models.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00005
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Received:
04 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Mr. Mikko Lehtimäki, Tampere University of Technology, Signal Processing, Tampere, 33101, Finland, lehtimaki.mikko@gmail.com
Dr. Marja-Leena Linne, Tampere University of Technology, Signal Processing, Tampere, 33101, Finland, marja-leena.linne@tuni.fi