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pypm install pynn

How to install PyNN

  1. Download and install ActivePython
  2. Open Command Prompt
  3. Type pypm install pynn
 Python 2.7Python 3.2Python 3.3
Windows (32-bit)
0.7.3
0.7.5Never BuiltWhy not?
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0.7.2 Available View build log
0.7.1 Available View build log
0.7.0 Available View build log
0.6.0 Available View build log
Windows (64-bit)
0.7.3
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0.7.3 Available View build log
0.7.2 Available View build log
0.7.1 Available View build log
0.7.0 Available View build log
0.6.0 Available View build log
Mac OS X (10.5+)
0.7.3
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0.7.2 Available View build log
0.7.1 Available View build log
0.7.0 Available View build log
0.6.0 Available View build log
Linux (32-bit)
0.7.3
0.7.5Never BuiltWhy not?
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0.7.2 Available View build log
0.7.1 Available View build log
0.7.0 Available View build log
0.6.0 Available View build log
Linux (64-bit)
0.7.5 Available View build log
0.7.3 Available View build log
0.7.2 Available View build log
0.7.1 Available View build log
0.7.0 Available View build log
0.6.0 Available View build log
 
Author
License
CeCILL http://www.cecill.info
Lastest release
version 0.7.5 on Jan 9th, 2014

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, PCSIM and Brian).

The API has two parts, a low-level, procedural API (functions create(), connect(), set(), record(), record_v()), and a high-level, object-oriented API (classes Population and Projection, which have methods like set(), record(), setWeights(), etc.).

The low-level API is good for small networks, and perhaps gives more flexibility. The high-level API is good for hiding the details and the book-keeping, allowing you to concentrate on the overall structure of your model.

The other thing that is required to write a model once and run it on multiple simulators is standard cell and synapse models. PyNN translates standard cell-model names and parameter names into simulator-specific names, e.g. standard model IF_curr_alpha is iaf_neuron in NEST and StandardIF in NEURON, while SpikeSourcePoisson is a poisson_generator in NEST and a NetStim in NEURON.

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.

PyNN is a work in progress, but is already being used for several large-scale simulation projects.

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Last updated Jan 9th, 2014

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