G-Node Workshop on Neuronal GPU Computing

Announcement by Michael Schmuker, Christian Kellner and Thomas Wachtler of a very interesting workshop:

Graphics processing units (GPUs) offer a low-cost approach to parallel high-performance computing. Neuronal simulations can be parallelized efficiently and are particularly well suited for implementation on GPUs. There is also great potential for GPU-based high-throughput analysis of neuronal data. The field is progressing at rapid pace, and has reached a point where it may strongly benefit from some kind of convergence between the different approaches.

To facilitate communication and foster collaboration in the field, the German INCF Node (G-Node) organizes a one-day symposium on neuronal GPU computing with an adjoint two-day developer workshop.

Symposium

Invited Speakers (preliminary):

• Romain Brette (École Normale Supérieure, Paris)
• Andreas Fidjeland (Imperial College, London)
• Dan Goodman (École Normale Supérieure, Paris)
• Thomas Nowotny (University of Sussex, Brighton-Falmer)
• Pierre Yger (Imperial College, London)

Applications are encouraged for talks at the symposium. Topics may cover one or more of the following:

• GPU-based neuronal simulation: development, applications, user reports
• GPU-based data analysis: software and use-cases
• Reports on GPU-powered neuronal research
• Comparison of GPU-based neuronal applications with other high-throughput technologies (e.g. clusters, neuromorphic hardware)

Participation in the symposium is free, but registration is required.

Developer workshop

We encourage applications for participation in the developer workshop. The workshop's aim is to bring together developers of GPU-based applications for neuroscience and to enable exchange of ideas, knowledge, and code. Enthusiastic users of GPU-based tools with programming skills are also warmly invited. The number of participants in the workshop is limited to 20.

Invited symposium speakers will also be present at the developer workshop.

Application and registration:

To apply for a presentation slot at the symposium, send us an abstract (approx. 500 words) of your presentation. A note with your name and affiliation is sufficient if you only want to register for the symposium. To apply for the developer workshop, please send a us a short letter of motivation stating your background, why you want to participate, and what you could contribute to the workshop.

Contact:

Direct your applications, registrations and any questions to gpu-computing@g-node.org .

Deadline for application: 28 Feb 2012

Workshop website: https://portal.g-node.org/gpu-workshop-2012/ Current information about speakers will be posted there.

Dates:

April 11, 2012 (Symposium)
April 12-13, 2012 (Developer Workshop)

Venue:

LMU Biocenter
Großhaderner Str. 2
82152 Planegg-Martinsried

Hope to see you in Munich in April!

The organizers

Michael Schmuker, Freie Universität Berlin & BCCN Berlin
Christian Kellner and Thomas Wachtler, G-Node, LMU München

Neo, a base library for handling electrophysiology data in Python

We are proud to announce the 0.2.0 release of Neo, a Python library for working with electrophysiology data, whether from biological experiments or from simulations.

A considerable problem in neurophysiology is the huge number of different, largely proprietary, data formats, which hinders data sharing and can make it difficult for researchers to use the best analysis methods, if the analysis software they wish to use does not support the data formats provided by their data acquisition system.

For cross-platform analysis, the field of neurophysiology is dominated by Matlab, but there is also a small but growing use of Python, perhaps boosted by the success Python has had in the neighbouring field of computational neuroscience. Certainly Python has a major cost advantage compared to Matlab, especially for larger-scale analyses on cluster computers, and as a popular, general purpose programming language makes it easy to tie data analysis into larger workflows including web- and database-access, modelling and simulation, and visualisation.

Neo arose from the realisation by a number of different groups developing Python software for the analysis and databasing of neurophysiology data that we were each independently reimplementing much of the same functionality. Although there is much scope for merging our analysis and visualization code, we decided that the best place to start was to develop standard data structures for electrophysiology, building on NumPy arrays, and to develop input-output modules to allow us to read from a large number of different electrophysiology data formats (and to write to a somewhat smaller subset, including HDF5).

This decision, to exclude data analysis and visualisation from the scope of Neo and to focus only on data representation and IO, makes Neo fairly lightweight as a dependency for other projects, requiring only NumPy and the Quantities package. The software packages OpenElectrophy, NeuroTools, Helmholtz, and PyNN, together with the database tools developed by the German Neuroinformatics Node, are all now in the process of moving to Neo as the basis of their data structures and for their IO layers. We would like to encourage the developers of other Python packages that work with electrophysiology data to consider adopting Neo, which will give you access to a large number of data formats and increase the interoperability of the tools in this domain. If you have Python support for a data format that is not currently available in Neo, we'd also like to talk to you!

Documentation:

http://packages.python.org/neo/

Licence:

Modified BSD

Download:

from PyPI or from the INCF Software Center