Limits...
[Not Available].

Pecevski D, Natschl├Ąger 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.


(A) Network elements of different type (with different arrangement of input and output ports) interconnected together in a PCSIM network.  Different colors of ports, gray or white, mark their different types, spiking or analog. (B) Neurons and synapses are specific subtypes of the more general concept of an network element. (C) Schematic diagram of the embedding of a network simulated with the Brian simulator into a PCSIM network element.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: (A) Network elements of different type (with different arrangement of input and output ports) interconnected together in a PCSIM network. Different colors of ports, gray or white, mark their different types, spiking or analog. (B) Neurons and synapses are specific subtypes of the more general concept of an network element. (C) Schematic diagram of the embedding of a network simulated with the Brian simulator into a PCSIM network element.

Mentions: The PCSIM communication system is general in a sense that it supports spiking and analog messages as communication between network elements. The network elements are not restricted to one type of message and can have multiple input and output ports, each of them capable of either receiving or sending spiking or analog messages (see Figures 5A,B).


[Not Available].

Pecevski D, Natschl├Ąger T, Schuch K - Front Neuroinform (2009)

(A) Network elements of different type (with different arrangement of input and output ports) interconnected together in a PCSIM network.  Different colors of ports, gray or white, mark their different types, spiking or analog. (B) Neurons and synapses are specific subtypes of the more general concept of an network element. (C) Schematic diagram of the embedding of a network simulated with the Brian simulator into a PCSIM network element.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: (A) Network elements of different type (with different arrangement of input and output ports) interconnected together in a PCSIM network. Different colors of ports, gray or white, mark their different types, spiking or analog. (B) Neurons and synapses are specific subtypes of the more general concept of an network element. (C) Schematic diagram of the embedding of a network simulated with the Brian simulator into a PCSIM network element.
Mentions: The PCSIM communication system is general in a sense that it supports spiking and analog messages as communication between network elements. The network elements are not restricted to one type of message and can have multiple input and output ports, each of them capable of either receiving or sending spiking or analog messages (see Figures 5A,B).

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.