Upload
alfonso-collins
View
53
Download
0
Tags:
Embed Size (px)
DESCRIPTION
The CARMEN Neuroinformatics Server Paul Watson 1 , Tom Jackson 2 , Georgios Pitsilis 1 , Frank Gibson 1 , Jim Austin 2 , Martyn Fletcher 2 , Bojian Liang 2 , Phillip Lord 1 1 School of Computing Science, Newcastle University 2 Department of Computer Science, University of York. - PowerPoint PPT Presentation
Citation preview
The CARMEN Neuroinformatics Server
Paul Watson1, Tom Jackson2, Georgios Pitsilis1, Frank Gibson1, Jim Austin2, Martyn Fletcher2, Bojian Liang2, Phillip Lord1
1School of Computing Science, Newcastle University2Department of Computer Science, University of York
Research Challenge
Understanding the brain is the greatest informatics challenge
Enormous implications for science:
• medicine
• biology
• computer science
100,000 neuroscientists are
generating vast amounts of data
• molecular (genomic/proteomic)
• neurophysiological (time-series electrical measures of activity)
• anatomical (spatial)
• behavioural
Collecting the Evidence
Current Problems in Neuroinformatics
• Data is:• expensive to collect but rarely shared• proprietary and locally described
• The result:• a shortage of analysis techniques that can be
applied across neuronal systems• Limited interaction between research centres
with complementary expertise
CARMEN
CARMEN uses e-science to tackle the problem
CARMEN supports the archiving, sharing, discovery, integration and analysis of neuroscience data
EPSRC e-Science Pilot Project (2006-10)
Builds on previous e-science projects DAME, Gold, myGrid, BROADEN, CISBAN...
CARMEN focuses on Neural Activity
cracking the neural code
neurone 1
neurone 2
neurone 3
raw voltage signal data is collected using single or multi-electrode array recording
Hub: A “CAIRN” repository for the storage and analysis of neuroscience dataSpokes: Neuroscience projects that produce data and analysis services for the hub, and use it to address key neuroscience questions
CARMEN : A Hub & Spoke Structure
Data Storage& Analysis
WP1 Spike Detection& Sorting
WP2 Information TheoreticAnalysis of Derived Signals
WP 3 Data-Driven Parameter
Determination in Conductance-Based
Models
WP5 Measurement and Visualisationof Spike Synchronisation
WP6 Multilevel Analysis andModelling in Networks
WP4 Intelligent Database Querying
Data
Metadata
Compute Cluster on which Services are Dynamically
Deployed
WebPortal
..............
WebPortal
Rich Clients
Sec
urity
Workflow Enactment
Engine
RegistryServiceRepos-
itory
CARMEN Active Information Repository Node
OMII:Grimoire
DAME:Signal Data Explorer
OMII/ myGrid:Taverna
OGSA-DAI, SRB, DAME
Gold:Role & Task based Security
myGrid & CISBAN
Dynasoar
• Data Collection from a Multi-Electrode Array• Data Visualisation and Exploration• Spike Detection• Spike Sorting• Analysis• Visualisation of Analysis Results
Currently, this is asemi-manual process
CARMEN has automated this….
A Typical CARMEN Scenario
Data Exploration with the Signal Data Explorer
Defining the Process: Workflow
SRB FileSystem
RDBMS
External
Client Spike Sorting
Service
Reporting
Dynamically Deployed Services in Dynasoar
TAVERNA
Registry
INPUT Data
OUTPUT Metadata
Available Services
RepositoryS
ecur
ityWorkflow Engine
Query
Example Workflow Enactment
13
C WSP
req
res
1
Host Provider
node 1s2, s5
…
node 2
node ns2
Web Service Provider
3
2: service fetch &deploy
SR
Service Repository
Dynamic service deployment
R
The deployed service remains in place andcan be re-used - unlike job scheduling
A request to s4cannot be satisfiedby an existingdeployment of theservice
14
Routing to an Existing Service Deployment
C WSP
req
res
Host Provider
node 1s2, s5
…
node 2
node ns2
Web Service Provider
Consumer
A request for s2 is routed to an existing
deployment of the service
Example Graph Output
Example Movie Output
Support for sharing vast amounts of data:
How was this data produced?
Which workflow produced this data?
Is there any data of this type…..?
Are there services that process this data?
e-Science Challenges: Discovery & Interpretation
Extensible, standardised metadata for neuroscience
data formats (e.g. timing, data channels)
experimental design (e.g. stimuli or drug treatments)
concurrent data (e.g. behaviour, physiological measures)
experimental idiosyncrasies (e.g. artifacts)
experimental conditions (e.g. animals, temperature)
e-Science Challenge: Metadata Design
How to locate patterns in time-series data across multiple levels of abstraction
Challenge: Discovery
“Only I am allowed to see this data”
“My collaborators can look at this data”
“Anyone can see this data”
“The funders want the data to be openly available after 1 year”
The Gold Project’s Security infrastructure will be used for this
Challenge: Controlling Sharing
Reproducible e-Sciencecurating services as well as datarepositories of deployable servicesdynamic service deployment
Challenge: Reproducible e-Science
CARMEN
CARMEN is delivering an e-Science infrastructure that can be applied across a range of diverse and challenging
applications (not only neuroscience)
CARMEN enables cooperation and interdisciplinary working in ways currently not possible
CARMEN will deliver new results in neuroscience, computer science and medicine
Demos on North East Regional e-Science Centre, White Rose and EPSRC stalls
CARMEN Consortium
Newcastle: Colin Ingram Paul Watson Stuart Baker Marcus Kaiser Phil Lord Evelyne Sernagor Tom Smulders Miles Whittington
York: Jim Austin Tom Jackson
Stirling: Leslie Smith Plymouth: Roman Borisyuk
Cambridge: Stephen Eglen
Warwick: Jianfeng Feng
Sheffield: Kevin Gurney Paul Overton
Manchester: Stefano Panzeri
Leicester: Rodrigio Quian Quiroga
Imperial: Simon Schultz
St. Andrews: Anne Smith
CARMEN Consortium
Commercial Partners
- applications in the pharmaceutical sector
- interfacing of data acquisition software
- application of database infrastructure
- commercialisation of analysis tools