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Neuroinformatics

Our work has two intertwined threads, one focused on specific questions in neuroscience, the other on developing novel informatics tools and approaches for neuroscience data sharing, modelling and simulation, and brain-inspired computing more generally. The neuroinformatics tools are essential for our own neuroscience research. By making the extra effort to generalize and share the tools so they can be used more widely, we accelerate the work of others, receive feedback that increases the quality and robustness of our own work, and benefit from the contributions of others.

Working at the interface of physics, biology and computer science, the Neuroinformatics team focuses on understanding how neuronal properties, synaptic mechanisms and network connectivity interact in giving rise to cortical and sub-cortical network function. To study this we use a data-driven, open science approach based on large-scale modelling and simulation, data-sharing, and development of open source software tools.

 

Key projects :

- PyNN simulator-independent specification of spiking neuronal network models
- Neo a standard data model for electrophysiology, with support for a wide range of neurophysiology file formats
- Mozaik - An integrated workflow framework for large scale neural simulations
- BRAINS data repository - find, share and reuse open neuroscience data
- Human Brain Project Neuromorphic Computing Platform - online access to the BrainScaleS and SpiNNaker neuromorphic computing systems
- Model Validation Framework - tools for systematically validating models against experimental results
- Sumatra Automated tracking of numerical experiments, for reproducible research

 

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)

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

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

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)

Andrew Davison, Thierry Brizzi, Domenico Guarino, Olivier Manette, Cyril Monier, Gérard Sadoc and Yves Frégnac, Helmholtz : a customizable framework for neurophysiology data management, Frontiers in Neuroinformatics : , (2013)


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