• Johanna Senk, Alper Yegenoglu, Olivier Amblet, Yury Brukau, Andrew Davison, David Roland Lester, Anna Lührs, Pietro Quaglio, Vahid Rostami, Andrew Rowley, Bernd Schuller, Alan Barry Stokes, Sacha Jennifer van Albada, Daniel Zielasko, Markus Diesmann, Benjamin Weyers, Michael Denker and Sonja Grün (2017) A Collaborative Simulation-Analysis Workflow for Computational Neuroscience Using HPC. In: High-Performance Scientific Computing. JHPCS 2016. Lecture Notes in Computer Science, edited by , Springer. doi:10.1007/978-3-319-53862-4_21 [BibTeX]
  • Pouzat C., Davison A. and Hinsen K. (2015) La recherche reproductible : une communication scientifique explicite. Statistique et société 3: 35--38 [BibTeX]
  • Muller, E., Bednar, J.A., Diesmann, M., Gewaltig, M.-O., Hines, M. and Davison, A.P. (2015) Python in Neuroscience. Frontiers in Neuroinformatics 9: doi:10.3389/fninf.2015.00011 [BibTeX]
  • Davison A.P., Mattioni M., Samarkanov D. and Teleńczuk B. (2014) Sumatra: A Toolkit for Reproducible Research. In: Implementing Reproducible Research, edited by , Chapman & Hall/CRC. [BibTeX] [Full text]
  • Vella M., Cannon R.C., Crook S., Davison A.P., Ganapathy G., Robinson H.P.C., Silver R.A. and Gleeson P. (2014) libNeuroML and PyLEMS: using Python to combine procedural and declarative modelling approaches in computational neuroscience. Frontiers in Neuroinformatics 8:38: doi:10.3389/fninf.2014.00038 [BibTeX] [Full text]
  • Djurfeldt M., Davison A.P. and Eppler J.M. (2014) Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Frontiers in Neuroinformatics 8:43: doi:10.3389/fninf.2014.00043 [BibTeX] [Full text]
  • Garcia S., Guarino D., Jaillet F., Jennings T.R., Pröpper R., Rautenberg P.L., Rodgers C., Sobolev A., Wachtler T., Yger P. and Davison A.P. (2014) Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8:10: doi:10.3389/fninf.2014.00010 [BibTeX] [Full text]

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  • Antolik J. and Davison A.P. (2013) Mozaik: a framework for model construction, simulation, data analysis and visualization for large-scale spiking neural circuit models. Front. Neuroinform. Conference Abstract doi:10.3389/conf.fninf.2013.09.00018
    Neuroinformatics 2013, Stockholm, Sweden, August. [Full text] [Proceedings]
  • Davison A.P., Djurfeldt M., Eppler J.M., Gleeson P., Hull M. and Muller E.B. (2013) An integration layer for neural simulation: PyNN in the software forest. Front. Neuroinform. Conference Abstract doi:10.3389/conf.fninf.2013.09.00020
    Neuroinformatics 2013, Stockholm, Sweden, August. [Full text] [Proceedings]
  • Davison A.P. (2013) Sumatra: a system for reproducible research.
    Workshop on Software Infrastructure for Reproducibility in Science, NYU Poly, New York, USA, May. [Slides]
  • Davison A.P. (2013) PyNN: a simulator-independent platform for large-scale data-driven neuronal simulations.
    Large-scale neuronal simulations - science, languages and platforms, Cosyne workshops, Snowbird, Utah, USA, March. [Slides]

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Managing complex workflows in neural simulation/data analysis

Workflows for reproducible research in comp. neurosci.

Modelling simple neurons with PyMOOSE

From Hoc to Python: a case study

Accessing hoc from Python

Modelling single cells in NEURON with the Python interpreter

Installation of NEURON with Python

Modelling STDP in the NEURON simulator


An initiative to foster collaborative software development and good software development practices in neuroscience, with an emphasis on use of the Python programming language. Includes hosting for open-source neuroscience software, the NeuralEnsemble Google Group, and the CodeJam meetings. more ...
a Python package for simulator-independent specification of spiking neuronal network models. In other words, you can write the code for a model once, using the PyNN API, and then run it without modification on any simulator that PyNN supports. more ...
Automated tracking of numerical experiments, for reproducible research. more ...
The goal of Neo is to improve interoperability between Python tools for working with electrophysiology data, by providing a common, shared object model and support for reading a wide range of neurophysiology file formats. more ...
NeuroML and NineML
NeuroML and NineML are XML-based languages for describing neuronal network models. I am currently involved in developing associated Python libraries: see libNeuroML and the NineML Python API.
Python tools to simplify the life of a computational neuroscientist, including simulation setup and instrumentation, data storage, analysis and visualisation. more ...
A framework to make it easier for neuroscientists to build a customised database for their experimental data. more ...