Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
Polychronization: Computation with Spikes
Neural Computation
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input
Biological Cybernetics
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Event-driven simulations of nonlinear integrate-and-fire neurons
Neural Computation
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
On a stochastic leaky integrate-and-fire neuronal model
Neural Computation
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Finding the event structure of neuronal spike trains
Neural Computation
A Markovian event-based framework for stochastic spiking neural networks
Journal of Computational Neuroscience
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
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In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.