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).
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