Limits...
[Not Available].

Pecevski D, Natschläger T, Schuch K - Front Neuroinform (2009)

Bottom Line: Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle.The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof.Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

View Article: PubMed Central - PubMed

Affiliation: Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria.

ABSTRACT
The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

No MeSH data available.


Plots from the output analysis example with the pypcsimplus package.  (A) Spike response of the spiking network implemented in the Section “Custom Network Elements”, with input neurons emitting spikes generated from a homogeneous Poisson process with a rate of 5 Hz, for the first 0.4 s of the simulation. (B) Cross-correlogram of the spike response of the network model from (A). (C) Spike response of the spiking network implemented in the Section “Custom Network Elements”, when the input neurons emit spikes generated from an inhomogeneous Poisson process with a rate changing according to a sinusoidal function (see text for details). (D) Cross-correlogram of the spike response of the network model from (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2698777&req=5

Figure 6: Plots from the output analysis example with the pypcsimplus package. (A) Spike response of the spiking network implemented in the Section “Custom Network Elements”, with input neurons emitting spikes generated from a homogeneous Poisson process with a rate of 5 Hz, for the first 0.4 s of the simulation. (B) Cross-correlogram of the spike response of the network model from (A). (C) Spike response of the spiking network implemented in the Section “Custom Network Elements”, when the input neurons emit spikes generated from an inhomogeneous Poisson process with a rate changing according to a sinusoidal function (see text for details). (D) Cross-correlogram of the spike response of the network model from (C).

Mentions: In another script we setup the analysis of the output data and the plotting. After the creation of the Recordings object by loading the recorded data from the saved HDF5 file, we plot the spiking activity of the network for the first 0.4 s of the simulation with the plot_raster function in pypcsimplus (see Figure 6A).


[Not Available].

Pecevski D, Natschläger T, Schuch K - Front Neuroinform (2009)

Plots from the output analysis example with the pypcsimplus package.  (A) Spike response of the spiking network implemented in the Section “Custom Network Elements”, with input neurons emitting spikes generated from a homogeneous Poisson process with a rate of 5 Hz, for the first 0.4 s of the simulation. (B) Cross-correlogram of the spike response of the network model from (A). (C) Spike response of the spiking network implemented in the Section “Custom Network Elements”, when the input neurons emit spikes generated from an inhomogeneous Poisson process with a rate changing according to a sinusoidal function (see text for details). (D) Cross-correlogram of the spike response of the network model from (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2698777&req=5

Figure 6: Plots from the output analysis example with the pypcsimplus package. (A) Spike response of the spiking network implemented in the Section “Custom Network Elements”, with input neurons emitting spikes generated from a homogeneous Poisson process with a rate of 5 Hz, for the first 0.4 s of the simulation. (B) Cross-correlogram of the spike response of the network model from (A). (C) Spike response of the spiking network implemented in the Section “Custom Network Elements”, when the input neurons emit spikes generated from an inhomogeneous Poisson process with a rate changing according to a sinusoidal function (see text for details). (D) Cross-correlogram of the spike response of the network model from (C).
Mentions: In another script we setup the analysis of the output data and the plotting. After the creation of the Recordings object by loading the recorded data from the saved HDF5 file, we plot the spiking activity of the network for the first 0.4 s of the simulation with the plot_raster function in pypcsimplus (see Figure 6A).

Bottom Line: Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle.The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof.Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

View Article: PubMed Central - PubMed

Affiliation: Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria.

ABSTRACT
The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

No MeSH data available.