Abstract
The neural simulation tool NEST (http://www.nest-initiative.org) is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 104 neurons and 107 to 109 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org) is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org). In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST's efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST's native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used.
Highlights
The first user interface for NEST (Gewaltig and Diesmann, 2007; Plesser et al, 2007) was the simulation language SLI, a stack-based language derived from PostScript (Adobe Systems Inc., 1999)
In the Section “Using PyNEST”, we introduce the basic modeling concepts of NEST
In the Section “The Interface Between Python and NEST”, we describe in detail how we bind NEST to the Python interpreter
Summary
The first user interface for NEST (Gewaltig and Diesmann, 2007; Plesser et al, 2007) was the simulation language SLI, a stack-based language derived from PostScript (Adobe Systems Inc., 1999). We describe how PyNEST connects NEST and Python and how it is implemented. Its arguments are the name of the neuron or device model and optionally the number of nodes to create.
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