Ornstein-Uhlenbeck model neuron revisited

DOI10.1007/BF00201415zbMath0819.92005OpenAlexW2091277970MaRDI QIDQ1890958

Petr Lansky, Jean Pierre Rospars

Publication date: 31 August 1995

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00201415

zbMATH Keywords

simulationOrnstein-Uhlenbeck processmembrane potentialinterspike intervalsnervous systemsaxonal compartmentdendritic compartmentmean and variance of the inputtemporal patterns of action potentialstwo-point stochastic neuron model

Mathematics Subject Classification ID

Neural biology (92C20) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Related Items (11)

Are the input parameters of white noise driven integrate and fire neurons uniquely determined by rate and CV? ⋮ A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input ⋮ Propagation of periodic and chaotic action potential trains along nerve fibers ⋮ Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes ⋮ On dependency properties of the ISIs generated by a two-compartmental neuronal model ⋮ Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings ⋮ The Ornstein-Uhlenbeck neuronal model with signal-dependent noise ⋮ Results on a binding neuron model and their implications for modified hourglass model for neuronal network ⋮ Two-compartment stochastic model of a neuron ⋮ Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies ⋮ Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron

Cites Work

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• A general diffusion model for analyzing the efficacy of synaptic input to threshold neurons
• The relationship between the Gabor elementary function and a stochastic model of the inter-spike interval distribution in the responses of visual cortex neurons
• Bistability and the dynamics of periodically forced sensory neurons
• Introduction to Theoretical Neurobiology
• Inference for stochastic neuronal models

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