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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.
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$NetBSD: distinfo,v 1.1 2013/09/20 10:12:02 jihbed Exp $
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SHA1 (PyNN-0.7.5.tar.gz) = 34bc1cbe7cd3b77a384a18e6ebc65467229dae9c
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RMD160 (PyNN-0.7.5.tar.gz) = 9fabed6cb8f4846e110eaf6b735c3b1087146e38
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Size (PyNN-0.7.5.tar.gz) = 263487 bytes
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