Event-Driven Simulation of Spiking Neurons with Stochastic Dynamics

DOI10.1162/08997660360581912zbMath1018.92007OpenAlexW2169707068WikidataQ59899800 ScholiaQ59899800MaRDI QIDQ4408929

Stefano Fusi, Jan Reutimann, Michele Giugliano

Publication date: 16 September 2003

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/08997660360581912

zbMATH Keywords

Fokker-Planck equation

Mathematics Subject Classification ID

Neural biology (92C20)

Related Items (8)

Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing ⋮ Exact Subthreshold Integration with Continuous Spike Times in Discrete-Time Neural Network Simulations ⋮ Unnamed Item ⋮ Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies ⋮ Exact Event-Driven Implementation for Recurrent Networks of Stochastic Perfect Integrate-and-Fire Neurons ⋮ Spiking neural nets with symbolic internal state ⋮ Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics ⋮ Fokker-Planck Solution for a Neuronal Spiking Model

Cites Work

• A population density approach that facilitates large-scale modeling of neural networks: Analysis and an application in orientation tuning
• Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.
• Dynamics of a recurrent network of spiking neurons before and following learning
• A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses
• Quantitative study of attractor neural network retrieving at low spike rates: I. substrate—spikes, rates and neuronal gain

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