NEVESIM: event-driven neural simulation framework with a Python interface.

Dejan Pecevski, David Kappel, Zeno Jonke
Author Information
  1. Dejan Pecevski: Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria.
  2. David Kappel: Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria.
  3. Zeno Jonke: Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria.

Abstract

NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

Keywords

References

  1. Neuroinformatics. 2010 Mar;8(1):43-60 [PMID: 20195795]
  2. Neural Comput. 2014 Jun;26(6):1055-79 [PMID: 24684451]
  3. Neural Comput. 2007 Jan;19(1):47-79 [PMID: 17134317]
  4. Neural Comput. 2003 Apr;15(4):811-30 [PMID: 12689388]
  5. PLoS Comput Biol. 2013 Apr;9(4):e1003037 [PMID: 23633941]
  6. Front Neurosci. 2009 Dec 15;3(3):374-80 [PMID: 20198154]
  7. PLoS Comput Biol. 2013;9(11):e1003311 [PMID: 24244126]
  8. Neural Comput. 2006 Dec;18(12):2959-93 [PMID: 17052155]
  9. Neural Comput. 2000 Oct;12(10):2305-29 [PMID: 11032036]
  10. IEEE Trans Neural Netw Learn Syst. 2014 Feb;25(2):316-31 [PMID: 24807031]
  11. Neural Comput. 2009 Apr;21(4):1068-99 [PMID: 18928367]
  12. Front Neuroinform. 2009 Jan 27;2:11 [PMID: 19194529]
  13. Network. 2003 Nov;14(4):613-27 [PMID: 14653495]
  14. Front Neuroinform. 2009 Jan 29;2:12 [PMID: 19198667]
  15. PLoS Comput Biol. 2011 Nov;7(11):e1002211 [PMID: 22096452]
  16. Neural Netw. 2001 Jul-Sep;14(6-7):921-32 [PMID: 11665782]
  17. Neural Comput. 2007 Oct;19(10):2604-9 [PMID: 17716004]
  18. Front Neuroinform. 2009 May 27;3:11 [PMID: 19543450]
  19. Neural Comput. 2012 Jun;24(6):1426-61 [PMID: 22364504]
  20. Neural Comput. 2006 Sep;18(9):2146-210 [PMID: 16846390]
  21. Front Neuroinform. 2008 Nov 18;2:5 [PMID: 19115011]
  22. Neural Comput. 2006 Aug;18(8):2004-27 [PMID: 16771661]
  23. Neural Comput. 2005 Aug;17(8):1776-801 [PMID: 15969917]
  24. Neural Comput. 2005 Apr;17(4):903-21 [PMID: 15829094]
  25. Front Neuroinform. 2009 Apr 27;3:10 [PMID: 19430597]
  26. Neural Comput. 2007 Dec;19(12):3226-38 [PMID: 17970651]
  27. Front Neuroinform. 2010 Oct 05;4:113 [PMID: 21031031]
  28. Neural Comput. 2012 Dec;24(12):3145-80 [PMID: 22845823]
  29. J Neurophysiol. 2005 Nov;94(5):3637-42 [PMID: 16014787]
  30. Neural Comput. 1997 Aug 15;9(6):1179-209 [PMID: 9248061]
  31. PLoS Comput Biol. 2011 Dec;7(12):e1002294 [PMID: 22219717]
  32. J Comput Neurosci. 2007 Dec;23(3):349-98 [PMID: 17629781]

Word Cloud

Created with Highcharts 10.0.0simulationNEVESIMneuronsframeworkPythonnetworksneuralevent-drivenspikinguserinterfacedifferenttypesalsoheterogeneouscanneuronsynapsenetworkwillconceptsstrategiesefficientlybasicsimulatorsoftwarepackagefastcoreC++scriptingprogramminglanguagesupportssynapseseasilyextendednewenableextensibilitydesigneddecouplelogiccommunicatingeventsspikeslevelimplementationinternaldynamicsindividualpaperpresentfeatureswellaspectsobject-orienteddesignapproachesutilizedimplementfunctionalitiesgiveoverviewcommandsconstructsdiscussbenefitsintegratingOnevaluablecapabilitiessimulateexactlystochasticrecentlydevelopedtheoreticalsamplingfunctionalityimplementedextensiontopAltogetherintendedpurposeprovidebasisextensionssupportvariousmodelsincorporatingpotentiallyuseNEVESIM:

Similar Articles

Cited By (7)