Neuroinformatics
Dir. Andrew Davison

The unique strength of our team is in developing tools for the systematic integration and comparison of experimental findings with reproducible computer models, in order to understand the functioning of generic cortical networks and of the early visual system.

The focus of the Neuroinformatics team within the UNIC lab is on understanding how neuronal properties, synaptic properties and network connectivity interact in giving rise to cortical and sub-cortical network function, using computational approaches.

Our work has both a neuroscience and an informatics component. We use a data-driven approach to studying computational function in sensory systems, in particular the early visual system, making use of databases of experimental data and large-scale modelling and simulation. On the informatics side, we are investigating methodologies for databasing and sharing of neuroscience data, for efficient and reproducible model specification and simulation, and for enhancing the reproducibility of computational research. We are collaborating with several European partners in the BrainScaleS consortium, developing systems for neuromorphic computation.

The majority of the tools developed by the team are released as open-source software, including PyNN, Neo, NeuroTools and Sumatra.

Members of the team are involved in international standardisation efforts in model specification (NeuroML and NineML) and in electrophysiology datasharing. The team plays a central role in the French Node of the INCF.

Methodologies used:

  • computational modelling
  • large-scale simulation
  • database development
  • workflow automation
  • neuromorphic computation

Selected Publications

Andrew Davison, Daniel Brüderle, Jochen Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent Perrinet and Pierre Yger, PyNN: a common interface for neuronal network simulators, Frontiers in NeuroInformatics 2: doi:10.3389/neuro.11.011.2008, (2009) [pdf] [abstract]

Andrew Davison, Automated capture of experiment context for easier reproducibility in computational research, Computing in Science and Engineering 14: 48-56, (2012) [pdf] [abstract]

Andrew Davison and Yves Frégnac, Learning cross-modal spatial transformations through spike timing-dependent plasticity, J Neurosci 26: 5604-15, (2006) [pdf] [abstract]

Daniel Brüderle, MA Petrovici, Bernhard Vogginger, Matthias Ehrlich, Thomas Pfeil, Sebastian Milner, A Grubl, K Wendt, Eric Muller, M-O Schwartz, D Husmann de Oliveira , S Jeltsch, J Fieres, M Schilling , P Muller, O Breitwieser, V Petkov, L Muller, Andrew Davison, P Krishnamurthy , Jens Kremkow, M Lundqvist, Eilif Muller, J Partzsch, S Scholze, H Zühl, C MAYR, Alain Destexhe, Markus Diesmann, TC Potjans , Anders Lansner, R Schüffny , Johannes Schemmel and Karlheinz Meier, A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. , Biological Cybernetics 104: 263-296, (2011) [pdf]

Andrew Davison, Jianfeng Feng and David Brown, Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model, J Neurophysiol 90: 1921-35, (2003) [pdf] [abstract] [ModelDB]