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Neuroinformatics: Sharing, Organizing and Accessing Data and Models

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Neuroinformatics: Sharing, Organizing and Accessing Data and Models Neuroinformatics: sharing, organizing and accessing data and models Arnd Roth Wolfson Institute for Biomedical Research University College London The optogenetics revolution Fuhrmann et al., 2015 The optogenetics revolution Fuhrmann et al., 2015 The connectomics revolution Helmstaedter et al., 2013 The connectomics revolution Helmstaedter et al., 2013 Connectomics data mining Jonas & Körding, 2015 Connectomics data mining Jonas & Körding, 2015 Deep artificial neural networks Mnih et al., 2015 Neuroinformatics: sharing, organizing and accessing experimental data Allen Institute http://alleninstitute.org Janelia Research Campus https://www.janelia.org/ Open Connectome Project http://www.openconnectomeproject.org/ Cell Image Library http://www.cellimagelibrary.org/ Human Brain Project http://www.humanbrainproject.eu/ INCF http://www.incf.org/ Single neuron and network simulators NEURON http://www.neuron.yale.edu/neuron/ GENESIS https://www.genesis-sim.org/ MOOSE http://moose.ncbs.res.in/ PSICS http://www.psics.org/ NEST http://www.nest-initiative.org/ Meta-simulators: simulator- independent model description PyNN http://neuralensemble.org/PyNN/ neuroConstruct http://www.neuroconstruct.org/ NeuroML http://www.neuroml.org/ NineML http://software.incf.org/software/nineml neuroConstruct http://www.opensourcebrain.org 12 neuroConstruct Software tool (written in Java) developed in Angus Silver’s Laboratory of Synaptic Transmission and Information Processing Facilitates development of 3D network models of biologically realistic cells through graphical interface Allows anatomical positioning of cells and complex connectivity of axons/dendrites Automatically generates scripts for running simulations in NEURON/GENESIS/MOOSE/PSICS/PyNN & more Support for import, export & conversion of NeuroML http://www.opensourcebrain.org 13 neuroConstruct – latest developments neuroConstruct can generate code for Parallel NEURON - Most widespread platform for large scale detailed neuronal simulations - Near linear speedup of simulations up to hundreds of cores Python scripting interface - Python becoming language of choice for neuroinformatics applications - Gives access to all functionality “behind the GUI” Open Source Brain - Platform for sharing & collaboratively developing models in computational neuroscience - Many neuroConstruct projects from multiple brain regions available http://www.opensourcebrain.org 14 Example using Python interface & Parallel NEURON 3D version of Traub et al 2005 Thalamocortical column model Parallel simulation durations scale approx. linearly up to 200 processors & 10,000 cells Wider interoperability framework http://www.opensourcebrain.org 16 Towards multiscale simulation: from molecules to circuits MCell http://www.mcell.org/ CellBlender http://www.mcell.org/ STEPS http://steps.sourceforge.net/ TrakEM2 http://fiji.sc/TrakEM2 TREES toolbox http://www.treestoolbox.org/ Public databases of neural models ModelDB https://senselab.med.yale.edu/ModelDB/ NeuroMorpho.org http://neuromorpho.org/ BigNeuron http://alleninstitute.org/bigneuron OpenSourceBrain http://www.opensourcebrain.org/ Human Brain Project http://www.humanbrainproject.eu/ How to make computational neuroscience a more accepted scientific approach? Reproducibility: easy to rerun and validate simulation result reported in a scientific paper. Accessibility: available to theoretical and experimental neuroscientists in an understandable format Portability: cross-simulator validation and exchange of models and components enabling reuse Transparency: exposure of internal properties and automated validation http://www.opensourcebrain.org 19 Neuroinformatics infrastructure NeuroML A simulator-independent language for describing and exchanging detailed neuronal and network models LEMS Compact and flexible model description language that underlies NeuroML 2 The Open Source Brain Initiative Accessible repository of standardized models and infrastructure for collaborative, open source model development http://www.opensourcebrain.org 20 The Open Source Brain repository 21 Current model development life-cycle 22 Current model development life-cycle http://www.opensourcebrain.org 23 OSB collaborative development scenario http://www.opensourcebrain.org 24 OSB iterative development through critical evaluation Experiment Model Validate http://www.opensourcebrain.org 26 A Whole Community Approach • Must bring experimental and theoretical & computational neuroscience closer. • While the latter seek minimal models, the former want hard earned experimental facts not to be ignored. • As the functional principles of neuronal networks in the brain remain elusive, and the interactions are often highly non-linear, ignoring biological facts without thought to errors can easily result in misleading conclusions, and erroneous theories of brain function. • Adhoc simplification is a matter of taste Level of detail: A rift in neuroscience 1. Simplify the details – minimal model for hypothesis-driven science – Adhoc simplification – Minimal for which question? vs 2. Consider all known – data-driven is data-ready – Hypothesis-free integration of facts – Algorithms fill in gaps from sparse data – Fewer free parameters! – Avoid wasting time hand tuning parameters for a given model “island” “We find that the major obstacle that hinders our understanding the brain is the fragmentation of brain research and the data it produces. Our most urgent need is thus a concerted international effort that can integrate this data in a unified picture of the brain as a single multi- level system...” The HBP-PS Consortium 2012:8 .
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