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