The Neuralensemble project brings together several efforts to make simulations of neurons easier. Currently, I spend most of my time working with the tools they created. Since a few days I have an account on their TRAC server, so I thought this is a good time to blog about it.
The project which got me into neuralensemble is PyNN , which provides a unified front end to several widely used neural simulators, e.g. NEURON, NEST and PCSIM. PyNN greatly facilitates setting up neural networks, because it provides an abstraction from simulator-specific languages. As the name suggests, it's written in Python, and lets you specify the network in Python, too.
For experiment design and analysis of simulated data there is Neurotools. It's written for PyNN (in Python), but can also be used independently from it. One of the most promising design goals is its ability to control large experiment sets, e.g. to systematically investigate the impact of certain parameters on network function. I'm sure every scientist who does computational modeling has written code to deal with this tasks at least once. Having a common API for this is a great thing.
Neuralensemble is a dynamic community which is still very open to all sorts of contributions. So if you're into modeling of neural networks, you should definitely give it a shot!
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