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

Software Systems for Neuroinformatics

Nigel Goddard

Institute for Adaptive & Neural Computation

Division of Informatics

University of Edinburgh

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Overview

• Neuroinformatics: what and why?

• Methodological challenges

• Software solutions: simulation

• Software solutions: collaboration

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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

Molecules

Synapses

Neurons

Networks

Systems

CNS

1 cm

100 um

10 cm

m

um

A

Maps1 cm

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Overview

• Neuroinformatics: what and why?

• Methodological challenges

• Software solutions: simulation

• Software solutions: collaboration

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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

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Overview

• Neuroinformatics: what and why?

• Methodological challenges

• Software solutions: simulation

• Software solutions: collaboration

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Need for Large-Scale Computing

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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

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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

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Multilevel Simulation

Purkinje

granuleDiffusion

Modeling Component

Connectionist NetworkModeling Component

Rastorgram component

Voltage trace component

http://www.neosim.org

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Overview

• Neuroinformatics: what and why?

• Methodological challenges

• Software solutions: simulation

• Software solutions: collaboration

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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

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Architecture

NeuroML is the language all components use to communicate data and models

Some components can implement other interfaces

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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

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Example• The templates for a cell tree structure…

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Example II : Structured Networks

• Specifying networks of networks

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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

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NeuroML & Simulation Tools

Catacomb channel simulator

NEOSIM network simulator

Kinetic scheme model of a sodium channel

NeuroML Models

Multicompartment network model

Simplified integrate/firenetwork model with learning rules

Genesis compartmental solver

Neuron compartmental solverBall/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 cellwith channels distributedover structure

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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)

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Example modules…

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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

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NEOSIM/NeuroML CollaboratorsInformatics

Nigel Goddard, Fred Howell, Paul Rogister - EdinburghGreg Hood - Pittsburgh, Michael Hines - YaleOliver Gewaltig - Honda R&D, Robert Cannon - BostonMichael Hucka - Caltech, Hugo Cornelis - AntwerpPaul Verschurre - Zurich, Ronan Reilly – DublinSimon Thorpe - CNRS

NeuroscienceErik De Schutter - AntwerpTerrence Sejnowski - SalkWilliam Levy - VirginiaDavid Willshaw, Andrew Gillies – EdinburghAngus Silver - UCL

Funded by the Human Brain Project, National Institutes of Health and National Science Foundation, USA