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PyNEST: A Convenient Interface to the NEST Simulator.

Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO - Front Neuroinform (2009)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Honda Research Institute Europe GmbH, Offenbach Germany.

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 10(4) neurons and 10(7) to 10(9) 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.

No MeSH data available.


Related in: MedlinePlus

Results of the balanced random network simulation. (A) The transcript of the simulation session shows the output during network setup and the summary printed at the end of the simulation. (B) Spike raster (top) and spike time histogram (bottom) of the N_rec recorded excitatory neurons.
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Figure 5: Results of the balanced random network simulation. (A) The transcript of the simulation session shows the output during network setup and the summary printed at the end of the simulation. (B) Spike raster (top) and spike time histogram (bottom) of the N_rec recorded excitatory neurons.

Mentions: The resulting plot is shown in Figure 5 together with a transcript of the simulation session. The simulation was run on a laptop with an Intel Core Duo processor at 1.83 GHz and 1.5 GB of RAM.


PyNEST: A Convenient Interface to the NEST Simulator.

Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig MO - Front Neuroinform (2009)

Results of the balanced random network simulation. (A) The transcript of the simulation session shows the output during network setup and the summary printed at the end of the simulation. (B) Spike raster (top) and spike time histogram (bottom) of the N_rec recorded excitatory neurons.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2636900&req=5

Figure 5: Results of the balanced random network simulation. (A) The transcript of the simulation session shows the output during network setup and the summary printed at the end of the simulation. (B) Spike raster (top) and spike time histogram (bottom) of the N_rec recorded excitatory neurons.
Mentions: The resulting plot is shown in Figure 5 together with a transcript of the simulation session. The simulation was run on a laptop with an Intel Core Duo processor at 1.83 GHz and 1.5 GB of RAM.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Honda Research Institute Europe GmbH, Offenbach Germany.

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 10(4) neurons and 10(7) to 10(9) 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.

No MeSH data available.


Related in: MedlinePlus