The NEURON simulation environment
Neural Computation
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Pulsed neural networks
Multiprocessor simulation of neural networks
The handbook of brain theory and neural networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Simulation of Spiking Neural Networks on Different Hardware Platforms
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A Library to Implement Neural Networks on MIMD Machines
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
On the Nonlearnability of a Single Spiking Neuron
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system
Proceedings of the 7th ACM international conference on Computing frontiers
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
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In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.