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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 example simulation. (A) The transcript of the simulation session shows the intermediate results of r_target as bisect() searches for the optimal rate. (B) The membrane potential of the target neuron as a function of time. Repeated adjustment of the spike rate of the inhibitory population by bisect() results in a convergence of the mean membrane potential to −112 mV, corresponding to an output spike rate of 5.0 Hz.
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Figure 1: Results of the example simulation. (A) The transcript of the simulation session shows the intermediate results of r_target as bisect() searches for the optimal rate. (B) The membrane potential of the target neuron as a function of time. Repeated adjustment of the spike rate of the inhibitory population by bisect() results in a convergence of the mean membrane potential to −112 mV, corresponding to an output spike rate of 5.0 Hz.

Mentions: A transcript of the simulation session and the resulting plot are shown in Figure 1.


PyNEST: A Convenient Interface to the NEST Simulator.

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

Results of the example simulation. (A) The transcript of the simulation session shows the intermediate results of r_target as bisect() searches for the optimal rate. (B) The membrane potential of the target neuron as a function of time. Repeated adjustment of the spike rate of the inhibitory population by bisect() results in a convergence of the mean membrane potential to −112 mV, corresponding to an output spike rate of 5.0 Hz.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Results of the example simulation. (A) The transcript of the simulation session shows the intermediate results of r_target as bisect() searches for the optimal rate. (B) The membrane potential of the target neuron as a function of time. Repeated adjustment of the spike rate of the inhibitory population by bisect() results in a convergence of the mean membrane potential to −112 mV, corresponding to an output spike rate of 5.0 Hz.
Mentions: A transcript of the simulation session and the resulting plot are shown in Figure 1.

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