Publications

• Antolík J., Cagnol R., Rózsa T., Monier C., Frégnac Y. and Davison A.P. (2024) A comprehensive data-driven model of cat primary visual cortex. PLoS Comput. Biol. 20: e1012342 doi:10.1371/journal.pcbi.1012342 [BibTeX]
• Sinha, A., Gleeson, P., Marin, B., Dura-Bernal, S., Panagiotou, S., Crook, S., Cantarelli, M., Cannon, R.C., Davison, A.P., Gurnani, H. and Silver, R.A. (2024) The NeuroML ecosystem for standardized multi-scale modeling in neuroscience. eLife 13: RP95135 doi:10.1101/2023.12.07.570537 [BibTeX]
• Ates, O., Appukuttan, S., Fragnaud, H., Fragnaud, C. and Davison, A.P. (2024) NeoViewer: facilitating reuse of electrophysiology data through browser-based interactive visualization. SoftwareX 26: 101710 doi:10.1016/j.softx.2024.101710 [BibTeX]
• Davison, A.P. and Appukuttan, S. (2022) Computational Neuroscience: A faster way to model neuronal circuitry. eLife 11: e84463 doi:10.7554/eLife.84463 [BibTeX]
• Appukuttan, S. and Davison, A.P. (2022) Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA1 pyramidal cells. Frontiers in Integrative Neuroscience 16: 1041423 doi:10.3389/fnint.2022.1041423 [BibTeX]
• Bologna, L.L., Smiriglia, R., Lupascu, C.A., Appukuttan, S., Davison, A.P., Ivaska G., Courcol, J.-D., Migliore, M. (2022) The EBRAINS Hodgkin-Huxley Neuron Builder: An Online Resource For Building Data-Driven Neuron Models. Frontiers in Neuroinformatics 16: 991609 doi:10.3389/fninf.2022.991609 [BibTeX]
• Appukuttan, S., Bologna, L.L., Schürmann, F., Migliore, M. and Davison, A.P. (2022) EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience. Neuroinformatics : doi:10.1007/s12021-022-09598-z [BibTeX]

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Presentations

• Davison A.P. (2024) Enhancing the findability, accessibility, interoperability, and reusability of neuroscience data using the openMINDS metadata framework.
  INCF Neuroinformatics Assembly 2024, Austin, Texas, September.
• Zehl L. and Pieschnik S. and Najafi P. and Bjaalie J.G. and Leergaard T.B. and Davison A.P. (2024) bids2openminds: extracting linked data from brain imaging data structures.
  INCF Neuroinformatics Assembly 2024, Austin, Texas, September.
• Davison A.P. and Botherel I. and Chan-Kin L. and Morel L. and Schlegel U. and Zehl L. and Najafi P. (2024) Facilitating data reuse with in-depth metadata.
  INCF Neuroinformatics Assembly 2024, Austin, Texas, September.
• Davison A.P. and Bonnier F. (2024) Disentangling the model and the simulation: perspectives for more effective model sharing.
  Building on Models Workshop: Experiences from a Decade with the Potjans-Diesmann Microcircuit Model, Käte Hamburger Kolleg Cultures of Research, Aachen, April.

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Blog

Disentangling the model and the simulation: perspectives for more effective model sharing

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

Projects

NeuralEnsemble

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 ...

PyNN

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 ...

Sumatra

Automated tracking of numerical experiments, for reproducible research. more ...

Neo

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.

NeuroTools

Python tools to simplify the life of a computational neuroscientist, including simulation setup and instrumentation, data storage, analysis and visualisation. more ...

Helmholtz

A framework to make it easier for neuroscientists to build a customised database for their experimental data. more ...