1. Software Systems for Neuroinformatics Nigel Goddard Institute for Adaptive & Neural Computation Division of Informatics University of Edinburgh

2. IST2001 #2 Overview Neuroinformatics: what and why? Methodological challenges Software solutions: simulation Software solutions: collaboration

3. IST2001 #3 This talk Neuroinformatics: What is it? Computational models and analytical techniques to help understand information- processing in the nervous system Information processing methods in the nervous system inspire new IT tools IT techniques to help collect, analyze, archive, share, simulate and visualize knowledge of information processing in the nervous system Computational Models Neural Engineering Software Systems

4. Molecules Synapses Neurons Networks Systems CNS 1 cm 100 um 10 cm m um A Maps 1 cm

5. IST2001 #5 Overview Neuroinformatics: what and why? Methodological challenges Software solutions: simulation Software solutions: collaboration

6. IST2001 #6 Methodological Challenges Scale and heterogeneity –data, models, computations –multiscale, multiformalism methods needed –Parallel/GRID resources required Collaboration is essential –Data and its understanding is distributed –Computional models are valuable expressions of understanding –we need tools to support exchange, discussion and comparison of models and data

7. IST2001 #7 Overview Neuroinformatics: what and why? Methodological challenges Software solutions: simulation Software solutions: collaboration

8. IST2001 #8 Need for Large-Scale Computing

9. IST2001 #9 Large Scale Network Modeling Compartmental cell modeling understood….. but network modeling support needs study Parallel computing needed for networks of spiking cells… … and amenable to effective parallel simulation using Discrete Event Simulation Tdata

10. IST2001 #10 NEOSIM Simulation Approach Optimize simulation kernel for network activity Plug in single-cell models and other components from other packages Design for parallel computers NEural Open SIMulation

11. IST2001 #11 Multilevel Simulation Purkinje granule Diffusion Modeling Component Connectionist Network Modeling Component Rastorgram component Voltage trace component http://www.neosim.org

12. IST2001 #12 Overview Neuroinformatics: what and why? Methodological challenges Software solutions: simulation Software solutions: collaboration

13. IST2001 #13 Need Improved Methodologies Progress will accelerate when neuroscientists can share model components to build more complex simulations Key technical requirements: –Need a common model exchange format The model description language must be translatable into forms suitable for simulation –Need software tools that support simulation of models, development, visualisation, exchange and storage of computational models and components of models

14. IST2001 #14 Architecture NeuroML is the language all components use to communicate data and models Some components can implement other interfaces

15. IST2001 #15 Neural Open Markup Language We propose NeuroML/Model as a candidate model representation & exchange language –Uses a simple, well-supported, textual substrate (XML) –Adds components that reflect the natural conceptual constructs used by modelers in the domain Data structures – a simulator independent model description –neuroml.model.cell,.synapse,.network… Extensible – tools can add tool-specific annotations Code is hidden behind the NeuroML declarative interface

16. IST2001 #16 Example The templates for a cell tree structure…

17. IST2001 #17 Example II : Structured Networks Specifying networks of networks

18. IST2001 #18 From specifications to working tools A standard data structure is only useful if tools can read & write it We have released a development kit for providing easy access to NeuroML for tool developers Features: –XMLIn / XMLOut : to read & write NeuroML files –A module loader : to download code modules on the fly –A generic model editor

19. IST2001 #19 NeuroML & Simulation Tools Catacomb channel simulator NEOSIM network simulator Kinetic scheme model of a sodium channel NeuroML Models Multicompartment network model Simplified integrate/fire network model with learning rules Genesis compartmental solver Neuron compartmental solver Ball/stick style cell model Integrate/fire point neuron model Monte-carlo synaptic transmission model Monte-carlo synaptic transmission model Cell generated from L-system growth rules Hodgkin-huxley style channel models Visualisation tools Reconstructed 3D cell with channels distributed over structure

20. IST2001 #20 Order in the chaos Step 1 : models declared with the languages of neuroml.model.* –side benefit: parallelisation is easier Step 2 : where simulators need to interoperate during a simulation run, they can implement interfaces in: neuroml.sim.run.* (for execution) and neuroml.sim.state.* (for access to instantiated model state variables)

21. IST2001 #21 Example modules…

22. IST2001 #22 Futures Make more simulators NeuroML-aware Starts to become possible to construct multi- scale models, with different simulators cooperating Napster-like service for modellers : models and software components are valuable – it makes sense to share + reuse as much as possible

23. IST2001 #23 NEOSIM/NeuroML Collaborators Informatics Nigel Goddard, Fred Howell, Paul Rogister - Edinburgh Greg Hood - Pittsburgh, Michael Hines - Yale Oliver Gewaltig - Honda R&D, Robert Cannon - Boston Michael Hucka - Caltech, Hugo Cornelis - Antwerp Paul Verschurre - Zurich, Ronan Reilly – Dublin Simon Thorpe - CNRS Neuroscience Erik De Schutter - Antwerp Terrence Sejnowski - Salk William Levy - Virginia David Willshaw, Andrew Gillies – Edinburgh Angus Silver - UCL Funded by the Human Brain Project, National Institutes of Health and National Science Foundation, USA