CARMEN: Code Analysis,
Repository and Modelling for
e-Neuroscience
Research Challenge
Understanding the brain
may be the greatest
informatics challenge of
the 21st century
Worldwide >100,000 neuroscientists
(~ 5,000 in UK) are generating vast
amounts of data
Principal experimental data formats:
 molecular (genomic/proteomic)
 neurophysiological (time-series
electrical measures of activity)
 anatomical (spatial)
 behavioural
Neuroinformatics concerns how these
data are handled and integrated, including
the application of computational modelling
Neuroinformatics
In recent years new technological opportunities for
data sharing have emerged with faster networks,
improved database technologies, and affordable
massive data storage capabilities
Neuroinformatics is increasingly exploiting these
opportunities to enable data sharing, re-use of data
and novel analysis based on new combinations of
data that can be performed via database systems
Need for Cooperation
Understanding the brain
may be the greatest
informatics challenge of
the 21st century
OECD identified a need to work cooperatively
in order to achieve major advances and have
established the International Neuroinformatics
Coordinating Facility
Cooperation will permit:
 development of common processes
 best value from data – long term curation
 ‘mega-analysis’ of large data sets
 integration of data sets across
different scales and different approaches
 interdisciplinary research
Potential Barriers to Cooperation
Technical
 Multiple proprietary data formats
 Need for detailed, standardised and evolvable
metadata
 Volume of the data to be analysed
Cultural
 Multiple communities each acting independently
 Concerns about the consequences of sharing data
 Difficulty in appreciating how the science could
be moved forwards by e-Science
CARMEN – Focus on Neural Activity
Understanding the brain
may be the greatest
informatics challenge of
the 21st century
 raw voltage signal data is collected using
single or multi-electrode array recording
 novel optical recording, particularly
the activity dynamics of large networks
 resolving the ‘neural
code’ from the timing
of action potential
activity
neurone 1
neurone 2
neurone 3
Electrophysiological Data
 Much current knowledge about brain function is based on
analysis of firing patterns of individual neurones.
 New computer-based data acquisition systems and techniques
for recording simultaneously from many neurones means data
are amassing rapidly.
 Neural modelling generates massive simulated data sets that
need to be processed, analysed and compared with
experimental data.
 Neuronal recordings can be intra- or extra-cellular recordings
of single spikes, ensembles of neurones, or field potentials.
All of these data are types of time-series data which require a
specialised information handling system.
CARMEN Objectives
 To demonstrate and sustain advances in neuroscience
enabled by e-Science technology
 To create a grid-enabled, real time ‘virtual laboratory’
environment for neurophysiological data
 To develop an extensible, client-defined ‘toolkit’ for
data extraction, analysis and modelling
 To provide a repository for archiving, sharing,
integration and discovery of data
 To achieve wide community and commercial
engagement in developing and using CARMEN
Project Exemplar
Recording from brain tissue removed
from epileptic patients (scarce tissue and
data rates up to 20 GB/h)
On line analysis by distributed collaborators will enable
experiment to be defined during data collection
Repository will enable integration of rare
case types from different laboratories
New knowledge will lead to
advances in treatment
CARMEN Consortium
Newcastle: Colin Ingram
Paul Watson
Stuart Baker
Marcus Kaiser
Phil Lord
Evelyne Sernagor
Tom Smulders
Miles Whittington
Cambridge: Stephen Eglen
York:
Leicester:
Rodrigio Quian Quiroga
Imperial:
Simon Schultz
Stirling:
Jim Austin
Tom Jackson
Warwick:
Jianfeng Feng
Sheffield:
Kevin Gurney
Paul Overton
Manchester: Stefano Panzeri
Leslie Smith
St. Andrews: Anne Smith
Plymouth: Roman Borisyuk
CARMEN Consortium
Commercial Partners
- applications in the pharmaceutical sector
- interfacing of data acquisition software
- application of database infrastructure
- commercialisation of analysis tools
Work Packages
WP1 Spike Detection
& Sorting
WP 3 Data-Driven Parameter
Determination in ConductanceBased Models
WP2 Information Theoretic
Analysis of Derived Signals
Data Storage
& Analysis
WP5 Measurement and Visualisation
of Spike Synchronisation
WP4 Intelligent
Database Querying
WP6 Multilevel Analysis and
Modelling in Networks
CARMEN Structure
Hub and Spoke Project
Hub:
A “CAIRN” repository for the storage and
analysis of neuroscience data
Spokes: A set of neuroscience projects that will produce
data and analysis services for the hub, and use it
to address key neuroscience questions
e-Science Challenges
 Managing vast amounts of data
 > 50TB primary data
 Extracting value from the data
 discovery & interpretation
 analysis – harnessing compute resources
 curation of services as well as data
 Controlling access to the data & services
CARMEN Active Information Repository Node
OMII/ myGrid:
Taverna/ BPEL
OGSA-DAI
& SRB
Web
Portal
DAME:
Signal Data
Explorer
Rich
Clients
.......
Web
Portal
Gold:
Role & Task based
Security
OMII:
Grimoire
Security
.......
Workflow
Enactment
Engine
Compute Cluster
on which Services
are Dynamically
Deployed
Data
myGrid
Metadata
Registry
Service
Repository
& Gold:
Feta,
Provenance
Dynasoar
White Rose Grid
Newcastle Grid
A Typical Scenario we want to Support
• Data Collection from Electrode Array
• Spike Detection
• with User Defined Threshold
• Spike Sorting
• Analysis
• Visualisation
Currently, this is a semi-manual process
We have an initial prototype
for automating this….
Signal Data Explorer
Example Workflow
Example Workflow Enactment
External
Client
Workflow
Engine
BPEL / TAVERNA
Repository
INPUT
Data
Spike Sorting
Service
Security
Available
Services
SRB
FileSystem
Registry
Reporting
Query
RDBMS
Dynamically
Deployed
Services in
Dynasoar
OUTPUT
Metadata
Example Graph Output
Example Movie Output
Some Remaining Challenges
 Extensible, standardised metadata for
neuroscience
 data formats (timing, data channels, etc.)
 experimental design (e.g. stimuli or drug
treatments)
 concurrent data (e.g. behaviour, physiological
measures)
 experimental idiosyncrasies (e.g. artifacts)
 experimental conditions (animals,
temperature, treatments etc.)
Some Remaining Challenges (cont.)
 Locating patterns in time-series data
across multiple levels of abstraction
 Reproducible e-Science
 curating services as well as data
 public repositories of deployable services
 dynamic service deployment
 Real-time expert collaboration
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