• Davison, A.P. (2020) [Rp] Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model. ReScience C 6: #14 doi:10.5281/zenodo.3972130 [BibTeX]
  • Dai, K., Hernando, J., Billeh, Y.N., Gratiy, S.L., Planas, J., Davison, A.P., Dura-Bernal, S., Gleeson, P., Devresse, A., Dichter, B.K., Gevaert, M., King, J.G., Van Geit, W.A.H., Povolotsky, A.V., Muller, E., Courcol, J.-D. and Arkhipov, A. (2020) The SONATA data format for efficient description of large-scale network models. PLOS Computational Biology 16: 1-24 doi:10.1371/journal.pcbi.1007696 [BibTeX] [Full text]
  • Gleeson P., Cantarelli M., Marin B., Quintana A., Earnshaw M., Sadeh S., Piasini E., Birgiolas J., Cannon R.C., Cayco-Gajic N.A., Crook S., Davison A.P., Dura-Bernal S., Ecker A., Hines M.L., Idili G., Lanore F., Larson S.D., Lytton W.W., Majumdar A., McDougal R.A., Sivagnanam S., Solinas S., Stanislovas R., van Albada S.J., van Geit W. and Silver R.A. (2019) Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron : doi:10.1016/j.neuron.2019.05.019 [BibTeX] [Full text]
  • Blundell I., Brette R., Cleland T.A., Close T.G., Coca D., Davison A.P., Diaz-Pier S., Musoles C.F., Gleeson P., Goodman D.F., Hines M., Hopkins M.W., Kumbhar P., Lester D.R., Marin B., Morrison A., Müller E., Nowotny T., Peyser A., Plotnikov D., Richmond P., Rowley A., Rumpe B., Stimberg M., Stokes A.B., Tomkins A., Trensch G., Woodman M. and Eppler J.M. (2018) Code generation in computational neuroscience: a review of tools and techniques. Frontiers in Neuroinformatics 12: doi:10.3389/fninf.2018.00068 [BibTeX] [Full text]
  • Antolík J. and Davison A.P. (2018) Arkheia: data management and communication for open computational neuroscience. Frontiers in Neuroinformatics 12: doi:10.3389/fninf.2018.00006 [BibTeX] [Full text]
  • Gleeson P., Davison A.P., Silver R.A. and Ascoli G.A (2017) A commitment to open source in neuroscience. Neuron 96: 964–965 doi:10.1016/j.neuron.2017.10.013 [BibTeX] [Full text]
  • Rougier N.P., Hinsen K., Alexandre F., Arildsen T., Barba L., Benureau F.C.Y., Brown C.T., de Buyl P., Caglayan O., Davison A.P., Delsuc M.A., Detorakis G., Diem A.K., Drix D., Enel P., Girard B., Guest O., Hall M.G., Henriques R.N., Hinaut X., Jaron K.S., Khamassi M., Klein A., Manninen T., Marchesi P., McGlinn D., Metzner C., Petchey O.L., Plesser H.E., Poisot T., Ram K., Ram Y., Roesch E., Rossant C., Rostami V., Shifman A., Stachelek J., Stimberg M., Stollmeier F., Vaggi F., Viejo G., Vitay J., Vostinar A., Yurchak R. and Zito T. (2017) Sustainable computational science: the ReScience initiative. PeerJ Computer Science 3: e142 [BibTeX] [Full text] [Preprint]

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  • Davison A.P. (2018) Improving reproducibility and reuse in computational and systems neuroscience. F1000Research 7: 1255. doi:10.7490/f1000research.1115931.1
    Neuroinformatics 2018, Montreal, Canada, August.
  • Guarino D. and Antolík J. and Davison A.P. and Frègnac Y (2017) An integrative model explaining many functions of corticothalamic feedback. BMC Neuroscience 18(Suppl 1): P78
    CNS*2017, Antwerp, Belgium, July. [Proceedings]
  • Antolík J. and Monier C. and Frègnac Y and Davison A.P. (2014) A comprehensive large-scale spiking model of cat visual cortex. Society for Neuroscience Abstracts
    Society for Neuroscience Annual Meeting, Washington, D.C., November.
  • 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]

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Porting a model from NEURON to PyNN: a case study

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 ...