» Articles » PMID: 25177291

NEVESIM: Event-driven Neural Simulation Framework with a Python Interface

Overview
Specialty Neurology
Date 2014 Sep 2
PMID 25177291
Citations 7
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.

Kappel D, Tetzlaff C PLoS Comput Biol. 2024; 20(11):e1012531.

PMID: 39495714 PMC: 11534197. DOI: 10.1371/journal.pcbi.1012531.


SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.

Gemo E, Spiga S, Brivio S Front Neurosci. 2024; 17:1270090.

PMID: 38264497 PMC: 10804805. DOI: 10.3389/fnins.2023.1270090.


Event-Based Update of Synapses in Voltage-Based Learning Rules.

Stapmanns J, Hahne J, Helias M, Bolten M, Diesmann M, Dahmen D Front Neuroinform. 2021; 15:609147.

PMID: 34177505 PMC: 8222618. DOI: 10.3389/fninf.2021.609147.


FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency.

Susi G, Garces P, Paracone E, Cristini A, Salerno M, Maestu F Sci Rep. 2021; 11(1):12160.

PMID: 34108523 PMC: 8190312. DOI: 10.1038/s41598-021-91513-8.


Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Naveros F, Garrido J, Carrillo R, Ros E, Luque N Front Neuroinform. 2017; 11:7.

PMID: 28223930 PMC: 5293783. DOI: 10.3389/fninf.2017.00007.


References
1.
Djurfeldt M, Hjorth J, Eppler J, Dudani N, Helias M, Potjans T . Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics. 2010; 8(1):43-60. PMC: 2846392. DOI: 10.1007/s12021-010-9064-z. View

2.
DHaene M, Hermans M, Schrauwen B . Toward unified hybrid simulation techniques for spiking neural networks. Neural Comput. 2014; 26(6):1055-79. DOI: 10.1162/NECO_a_00587. View

3.
Morrison A, Straube S, Plesser H, Diesmann M . Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Comput. 2006; 19(1):47-79. DOI: 10.1162/neco.2007.19.1.47. View

4.
Reutimann J, Giugliano M, Fusi S . Event-driven simulation of spiking neurons with stochastic dynamics. Neural Comput. 2003; 15(4):811-30. DOI: 10.1162/08997660360581912. View

5.
Nessler B, Pfeiffer M, Buesing L, Maass W . Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity. PLoS Comput Biol. 2013; 9(4):e1003037. PMC: 3636028. DOI: 10.1371/journal.pcbi.1003037. View