The simulation of large-scale neural networks
Methods in neuronal modeling
Fast calculation of synaptic conductances
Neural Computation
On numerical simulations of integrate-and-fire neural networks
Neural Computation
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input
Biological Cybernetics
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
Programmable Logic Construction Kits for Hyper-Real-Time Neuronal Modeling
Neural Computation
Spike-Timing-Dependent Plasticity in Balanced Random Networks
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
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Vectorized algorithms for spiking neural network simulation
Neural Computation
Compositionality of arm movements can be realized by propagating synchrony
Journal of Computational Neuroscience
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
Journal of Computational Neuroscience
Firing-rate models capture essential response dynamics of LGN relay cells
Journal of Computational Neuroscience
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Lovelace and Cios (2008) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without repeated summation over the history of input spikes and to integrate the subthreshold dynamics exactly. State-of-the-art solvers for networks of integrate-and-fire model neurons are substantially more efficient than the VSSN simulator and allow routine simulations of networks of some 105 neurons and 109 connections on moderate computer clusters.