Statistical properties of stochastic nonlinear dynamical models of single spiking neurons and neural networks.
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Summary
Differential equations are obtained for the means, variances, and covariances of the dynamical variables in a network of n connected spiking neurons in the presence of noise.
- Type
- article
- Published
- 1996-11-01
- Cited by
- 88
- References
- 25
- OpenAlex
- https://openalex.org/W2009986636
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7558459
Keywords
Ordinary differential equation, Stochastic differential equation, Nonlinear system, Dynamical systems theory, White noise
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- Synchronization properties of spindle oscillations in a thalamic reticular nucleus model.
- Stimulus-Dependent Synchronization of Neuronal Assemblies
- Random Perturbations of the Reduced Fitzhugh-Nagumo Equation
- Modern Group Theoretical Methods in Physics
- Elementary applications of probability theory - with an introduction to stochastic differential equations (2. ed.)
- Elementary Applications of Probability Theory
- Elementary Applications of Probability Theory, Second Edition
Cited by
- Doubly stochastic processes: an approach for understanding central nervous system activity
- Analysis of inverse stochastic resonance and long-term firing in Hodgkin-Huxley neurons
- Dynamic models of brain imaging data and their Bayesian inversion
- Controlling excitable media with noise
- Neural stochastic dynamics of perceptual decision making
- Optimizing information flow in small genetic networks. IV. Spatial coupling.
- Analytical and Simulation Results for Stochastic Fitzhugh-Nagumo Neurons and Neural Networks
- Extended method of moments for deterministic analysis of stochastic multistable neurodynamical systems.
- Observing bifurcation and resonance in a mean-field coupled periodically driven noisy overdamped oscillators by the method of moments
- Mean field approximation for noisy delay coupled excitable neurons
- Noisy spiking neurons and networks: useful approximations for firing probabilities and global behavior.
- Dynamics of moments of FitzHugh-Nagumo neuronal models and stochastic bifurcations.
- Noise induced complexity: from subthreshold oscillations to spiking in coupled excitable systems.
- Stability, bifurcations, and dynamics of global variables of a system of bursting neurons.
- Population dynamics under the Laplace assumption
- Population dynamics: Variance and the sigmoid activation function
- Stationary and dynamical properties of finite N-unit Langevin models subjected to multiplicative noises
- Augmented moment method for stochastic ensembles with delayed couplings. II. FitzHugh-Nagumo model.
- The spike timing precision of FitzHugh–Nagumo neuron network coupled by gap junctions
- Modeling Neural Activity
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