Neuroinformatics: sharing, organizing and

accessing data and models

Arnd Roth

Wolfson Institute for Biomedical Research

University College London

The optogenetics revolution

Fuhrmann et al., 2015

The optogenetics revolution

Fuhrmann et al., 2015

The connectomics revolution

Helmstaedter et al., 2013

The connectomics revolution

Helmstaedter et al., 2013

Connectomics data mining

Jonas & Körding, 2015

Connectomics data mining

Jonas & Körding, 2015

Deep artificial neural networks

Mnih et al., 2015

Neuroinformatics: sharing, organizing

and accessing experimental data

Allen Institute http://alleninstitute.org

Janelia Research Campus https://www.janelia.org/

Open Connectome Project http://www.openconnectomeproject.org/

Cell Image Library http://www.cellimagelibrary.org/

Human Brain Project http://www.humanbrainproject.eu/

INCF http://www.incf.org/

Single neuron and network simulators

NEURON http://www.neuron.yale.edu/neuron/

GENESIS https://www.genesis-sim.org/

MOOSE http://moose.ncbs.res.in/

PSICS http://www.psics.org/

NEST http://www.nest-initiative.org/

Meta-simulators: simulator-

independent model description

PyNN http://neuralensemble.org/PyNN/

neuroConstruct http://www.neuroconstruct.org/

NeuroML http://www.neuroml.org/

NineML http://software.incf.org/software/nineml

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neuroConstruct

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neuroConstruct

Software tool (written in Java) developed in Angus Silver’s Laboratory of Synaptic Transmission and Information Processing

Facilitates development of 3D network models of biologically realistic cells through graphical interface

Allows anatomical positioning of cells and complex connectivity of axons/dendrites

Automatically generates scripts for running simulations in NEURON/GENESIS/MOOSE/PSICS/PyNN & more

Support for import, export & conversion of NeuroML

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neuroConstruct – latest developments

neuroConstruct can generate code for Parallel NEURON

- Most widespread platform for large scale detailed neuronal simulations

- Near linear speedup of simulations up to hundreds of cores

Python scripting interface

- Python becoming language of choice for neuroinformatics applications

- Gives access to all functionality “behind the GUI”

Open Source Brain

- Platform for sharing & collaboratively developing models in computational neuroscience

- Many neuroConstruct projects from multiple brain regions available

3D version of Traub et al 2005

Thalamocortical column model

Parallel simulation durations

scale approx. linearly up to 200 processors & 10,000

cells

Example using Python interface & Parallel NEURON

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Wider interoperability framework

Towards multiscale simulation:

from molecules to circuits

MCell http://www.mcell.org/

CellBlender http://www.mcell.org/

STEPS http://steps.sourceforge.net/

TrakEM2 http://fiji.sc/TrakEM2

TREES toolbox http://www.treestoolbox.org/

Public databases of neural models

ModelDB https://senselab.med.yale.edu/ModelDB/

NeuroMorpho.org http://neuromorpho.org/

BigNeuron http://alleninstitute.org/bigneuron

OpenSourceBrain http://www.opensourcebrain.org/

Human Brain Project http://www.humanbrainproject.eu/

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How to make computational neuroscience a more accepted scientific approach?

Reproducibility: easy to rerun and validate simulation result reported in a scientific paper.

Accessibility: available to theoretical and experimental neuroscientists in an understandable format

Portability: cross-simulator validation and exchange of models and components enabling reuse

Transparency: exposure of internal properties and automated validation

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Neuroinformatics infrastructure

NeuroMLA simulator-independent language for describing and exchanging

detailed neuronal and network models

LEMS Compact and flexible model description language that underlies

NeuroML 2

The Open Source Brain InitiativeAccessible repository of standardized models and infrastructure

for collaborative, open source model development

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The Open Source Brain repository

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Current model development life-cycle

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Current model development life-cycle

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OSB collaborative development scenario

OSB iterative development through critical evaluation

Validate

Experiment

Model

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http://www.opensourcebrain.org

A Whole Community Approach

• Must bring experimental and theoretical & computational neuroscience closer.

• While the latter seek minimal models, the former want hard earned experimental facts not to be ignored.

• As the functional principles of neuronal networks in the brain remain elusive, and the interactions are often highly non-linear, ignoring biological facts without thought to errors can easily result in misleading conclusions, and erroneous theories of brain function.

• Adhoc simplification is a matter of taste

Level of detail: A rift in neuroscience

1. Simplify the details– minimal model for hypothesis-driven science– Adhoc simplification– Minimal for which question?

vs2. Consider all known

– data-driven is data-ready– Hypothesis-free integration of facts– Algorithms fill in gaps from sparse data– Fewer free parameters!– Avoid wasting time hand tuning parameters for a

given model “island”

“We find that the major obstacle that hinders our understanding the brain is the fragmentation of brain research and the data it produces.

Our most urgent need is thus a concerted international effort that can integrate this data in a unified picture of the brain as a single multi-level system...”

The HBP-PS Consortium 2012:8