Parabolic bursting in an excitable system coupled with a slow oscillation
SIAM Journal on Applied Mathematics
Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem
Communications of the ACM
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
On numerical simulations of integrate-and-fire neural networks
Neural Computation
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Reliability of spike timing is a general property of spiking model neurons
Neural Computation
How Close Are We to Understanding V1?
Neural Computation
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Neural Computation
Optimizing synaptic conductance calculation for network simulations
Neural Computation
Exact Simulation of Integrate-and-Fire Models with Exponential Currents
Neural Computation
Event-driven simulations of nonlinear integrate-and-fire neurons
Neural Computation
Simplicity and efficiency of integrate-and-fire neuron models
Neural Computation
Covert attention with a spiking neural network
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Event and time driven hybrid simulation of spiking neural networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
A Markovian event-based framework for stochastic spiking neural networks
Journal of Computational Neuroscience
Journal of Computational Neuroscience
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Computational neuroscience relies heavily on the simulation of large networks of neuron models. There are essentially two simulation strategies: (1) using an approximation method (e.g., Runge-Kutta) with spike times binned to the time step and (2) calculating spike times exactly in an event-driven fashion. In large networks, the computation time of the best algorithm for either strategy scales linearly with the number of synapses, but each strategy has its own assets and constraints: approximation methods can be applied to any model but are inexact; exact simulation avoids numerical artifacts but is limited to simple models. Previous work has focused on improving the accuracy of approximation methods. In this article, we extend the range of models that can be simulated exactly to a more realistic model: an integrate-and-fire model with exponential synaptic conductances.