Andrew Davison
Hi, welcome to my personal homepage. I am a senior research scientist in the UNIC lab (Unité de Neuroscience, Information et Complexité) of the CNRS, where I lead the Neuroinformatics group. You might also be interested in my lab homepage or my Twitter feed.
My main research interests are in large-scale, data-constrained, biologically-detailed modelling of neuronal networks. My work at the moment has several strands:
- development of tools to facilitate collaborative modelling, model sharing, and the use of novel hardware (GPU, neuromorphic) for neuronal simulations, notably PyNN, NineML and NeuroML.
- promoting reproducible research in computational neuroscience and neuroinformatics, both through trying to spread best practices and through tool development (see the Sumatra project).
- promoting neurophysiology data sharing, by participation in international standardisation efforts, by development of tools to harmonize the handling of electrophysiology data in Python (see the Neo project), and by development of a framework to simplify databasing of neurophysiology data in small labs (see the Helmholtz project).
- development of models of the early visual system, from retina to primary visual cortex, in close collaboration with experimentalists.
For more information, see the links under “Projects” below. The following icons link to stuff about or by me elsewhere on the web:
Publications
Crook S.M., Bednar J.A., Berger S., Cannon R., Davison A.P., Djurfeldt M., Eppler J., Kriener B., Furber S., Graham B., Plesser H.E., Schwabe L., Smith L., Steuber V. and van Albada S. (2012) Creating, documenting and sharing network models.. Network: Computation in Neural Systems 23: 131-149. [Abstract] [preprint] [Informa]
Davison A.P. (2012) Collaborative modelling: the future of computational neuroscience?. Network: Computation in Neural Systems 23: 157-166. [Abstract] [preprint] [Informa]
Davison A.P. (2012) Automated capture of experiment context for easier reproducibility in computational research. Computing in Science and Engineering 14: 48-56. [Abstract] [CiSE] [preprint]
Brüderle D., Petrovici M.A., Vogginger B., Ehrlich M., Pfeil T., Millner S., Grübl A., Wendt K., Müller E., Schwartz M.O., Husmann de Oliveira D., Jeltsch S., Fieres J., Schilling M., Müller P., Breitwieser O., Petkov V., Muller L., Davison A.P., Krishnamurthy P., Kremkow J., Lundqvist M., Muller E., Partzsch J., Scholze S., Zühl L., Mayr C., Destexhe A., Diesmann M., Potjans T.C., Lansner A., Schüffny R., Schemmel J., Meier K. (2011) A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems. Biological Cybernetics 104: 263-296. [Abstract] [arXiv] [SpringerLink]
Gleeson P., Crook S., Cannon R.C., Hines M.L., Billings, G.O., Farinella M., Morse T.M., Davison A.P., Ray S., Bhalla U.S., Barnes S.R., Dimitrova Y.D. and Silver, R.A. (2010) NeuroML: A language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology 6: e1000815. [Abstract] [PLoS]
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 ...
© 2013 Andrew P. Davison 
Most of the icons are by IconDock.com. The company logos in the icons are copyright of their respective owners.