PyNN 0.9.0 released

I'm happy to announce the release of PyNN 0.9.0!

This version of PyNN adopts the new, simplified Neo object model, first released as Neo 0.5.0, for the data structures returned by Population.get_data(). For more information on the new Neo API, see the Neo release notes

The main difference for a PyNN user is that the AnalogSignalArray class has been renamed to AnalogSignal, and similarly the Segment.analogsignalarrays attribute is now called Segment.analogsignals

What is PyNN?

PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models.

In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian as well as the SpiNNaker and BrainScaleS neuromorphic hardware systems).

Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models.

The code is released under the CeCILL licence (GPL-compatible).

Neo 0.5.0-alpha1 released

We are pleased to announce the first alpha release of Neo 0.5.0.

Neo is a Python library which provides data structures for working with electrophysiology data, whether from biological experiments or from simulations, together with a large library of input-output modules to allow reading from a large number of different electrophysiology file formats (and to write to a somewhat smaller subset, including HDF5 and Matlab).

For Neo 0.5, we have taken the opportunity to simplify the Neo object model. Although this will require an initial time investment for anyone who has written code with an earlier version of Neo, the benefits will be greater simplicity, both in your own code and within the Neo code base, which should allow us to move more quickly in fixing bugs, improving performance and adding new features. For details of what has changed and what has been added, see the Release notes.

If you are already using Neo for your data analysis, we encourage you to give the alpha release a try. The more feedback we get about the alpha release, the quicker we can find and fix bugs. If you do find a bug, please create a ticket. If you have questions, please post them on the mailing list or in the comments below.

Documentation:

http://neo.readthedocs.io/en/neo-0.5.0alpha1/

Licence:

Modified BSD

Source code:

https://github.com/NeuralEnsemble/python-neo

Updated Docker images for biological neuronal network simulations with Python

The NeuralEnsemble Docker images for biological neuronal network simulations with Python have been updated to contain NEST 2.10, NEURON 7.4, Brian 2.0rc1 and PyNN 0.8.1.

In addition, the default images (which are based on NeuroDebian Jessie) now use Python 3.4. Images with Python 2.7 and Brian 1.4 are also available (using the "py2" tag). There is also an image with older versions (NEST 2.2 and PyNN 0.7.5).

The images are intended as a quick way to get simulation projects up-and-running on Linux, OS X and Windows. They can be used for teaching or as the basis for reproducible research projects that can easily be shared with others.

The images are available on Docker Hub.

To quickly get started, once you have Docker installed, run

docker pull neuralensemble/simulation
docker run -i -t neuralensemble/simulation /bin/bash

For Python 2.7:

docker pull neuralensemble/simulation:py2

For older versions:

docker pull neuralensemble/pynn07

For ssh/X11 support, use the "simulationx" image instead of "simulation". Full instructions are available here.

If anyone would like to help out, or suggest other tools that should be installed, please contact me, or open a ticket on Github.

PyNN 0.8.1 released

Having forgotten to blog about the release of PyNN 0.8.0, here is an announcement of PyNN 0.8.1!

For all the API changes between PyNN 0.7 and 0.8 see the release notes for 0.8.0. The main change with PyNN 0.8.1 is support for NEST 2.10.

PyNN 0.8.1 can be installed with pip from PyPI.

What is PyNN?

PyNN (pronounced 'pine' ) is a simulator-independent language for building neuronal network models.

In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian as well as the SpiNNaker and BrainScaleS neuromorphic hardware systems).

Even if you don't wish to run simulations on multiple simulators, you may benefit from writing your simulation code using PyNN's powerful, high-level interface. In this case, you can use any neuron or synapse model supported by your simulator, and are not restricted to the standard models.

The code is released under the CeCILL licence (GPL-compatible).

Released - BluePyOpt 0.2 : Leveraging OSS and the cloud to optimise models to neuroscience data

BluePyOpt

The BlueBrain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.

The code is available here:
https://github.com/BlueBrain/BluePyOpt
A preprint to the paper is available here:
http://arxiv.org/abs/1603.00500