small correction to bibtex file

Author: Jaquier Aurélien Tristan

misc/github_wiki/bibtex/mentions_BPO.bib

@@ -34,7 +34,7 @@ @article{appukuttanSoftwareFrameworkValidating
 
 @article{awileModernizingNEURONSimulator,
   title = {Modernizing the {{NEURON Simulator}} for {{Sustainability}}, {{Portability}}, and {{Performance}}},
-  author = {Awile, Omar and Kumbhar, Pramod and Cornu, Nicolas and {Dura-Bernal}, Salvador and King, James Gonzalo and Lupton, Olli and Magkanaris, Ioannis and McDougal, Robert A and Newton, Adam J H and Pereira, Fernando and Sa, Alexandru and Carnevale, Nicholas T and Lytton, William W and Hines, Michael L and Sch{\"u}rmann, Felix},
+  author = {Awile, Omar and Kumbhar, Pramod and Cornu, Nicolas and {Dura-Bernal}, Salvador and King, James Gonzalo and Lupton, Olli and Magkanaris, Ioannis and McDougal, Robert A. and Newton, Adam J.H. and Pereira, Fernando and S{\u a}vulescu, Alexandru and Carnevale, Nicholas T. and Lytton, William W. and Hines, Michael L. and Sch{\"u}rmann, Felix},
   abstract = {The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.},
   langid = {english}
 }