Transcript SBML: Systems Biology Markup Language

Software Systems for
Neuroinformatics
Nigel Goddard
Institute for Adaptive & Neural Computation
Division of Informatics
University of Edinburgh
Overview
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Neuroinformatics: what and why?
Methodological challenges
Software solutions: simulation
Software solutions: collaboration
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Neuroinformatics: What is it?
Computational Models
Computational models and analytical
techniques to help understand informationprocessing in the nervous system
Neural Engineering
Information processing
methods in the nervous
system inspire new IT tools
This talk
Software Systems
IT techniques to help collect, analyze, archive,
share, simulate and visualize knowledge of
information processing in the nervous system
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m
CNS
10 cm
Systems
1 cm
Maps
1 cm
Networks
100 um
Neurons
um
A
Synapses
Molecules
Overview
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•
•
•
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
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•
•
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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
data
T
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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
Connectionist Network
Modeling Component
Purkinje
Diffusion
Modeling Component
http://www.neosim.org
Voltage trace component
granule
Rastorgram component
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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
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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
NeuroML Models
Hodgkin-huxley style
Kinetic scheme model
channel models
of a sodium channel
Monte-carlo synaptic
transmission model
Integrate/fire point
neuron model
Catacomb channel
simulator
Monte-carlo synaptic
transmission model
Neuron compartmental solver
Ball/stick style cell model
Reconstructed 3D cell
Cell generated from
with channels distributed
L-system growth rules
over structure
Multicompartment
network model
Simplified integrate/fire
network model with learning rules
Genesis compartmental solver
Visualisation tools
NEOSIM network
simulator
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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 multiscale 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 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
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