Spike-train spectra and network response functions for nonlinear integrate-and-fire neurons

DOI10.1007/s00422-008-0244-yzbMath1161.92014OpenAlexW2018176917WikidataQ46244281 ScholiaQ46244281MaRDI QIDQ999380

Magnus J. E. Richardson

Publication date: 3 February 2009

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

Full work available at URL: https://doi.org/10.1007/s00422-008-0244-y

zbMATH Keywords

Fokker-Planck equationFirst-passage-time densityRecurrent networksSpike-train spectrumThreshold-integration method

Mathematics Subject Classification ID

Neural biology (92C20) Biophysics (92C05) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70) Computational methods for problems pertaining to biology (92-08)

Related Items (13)

Decision-time statistics of nonlinear diffusion models: characterizing long sequences of subsequent trials ⋮ Variable synaptic strengths controls the firing rate distribution in feedforward neural networks ⋮ How does transient signaling input affect the spike timing of postsynaptic neuron near the threshold regime: an analytical study ⋮ One-Dimensional Population Density Approaches to Recurrently Coupled Networks of Neurons with Noise ⋮ Oscillations and variability in neuronal systems: interplay of autonomous transient dynamics and fast deterministic fluctuations ⋮ Bimodality of the interspike interval distributions for subordinated diffusion models of integrate-and-fire neurons ⋮ Statistical structure of neural spiking under non-Poissonian or other non-white stimulation ⋮ Motif statistics and spike correlations in neuronal networks ⋮ Investigating the correlation-firing rate relationship in heterogeneous recurrent networks ⋮ Extracting nonlinear integrate-and-fire models from experimental data using dynamic \(I-V\) curves ⋮ Special issue on quantitative neuron modeling ⋮ Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps ⋮ Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach

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