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

• Published in: Frontiers in neuroinformatics Vol. 8; p. 70
• Main Authors: Pecevski, Dejan, Kappel, David, Jonke, Zeno
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 14.08.2014; Frontiers Media S.A
• Subjects: Algorithms; Computer science; Event-driven; Firing pattern; Neural networks; neural simulator; Neurons; Neuroscience; NEVESIM; python; Simulation; Software; spiking neurons; event-driven; NEVESIM; spiking neurons; neural simulator; Python
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2014.00070

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.