ArticlePLoS computational biology2013
Characteristic effects of stochastic oscillatory forcing on neural firing: analytical theory and comparison to paddlefish electroreceptor data.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
- Fluctuation-response relations for integrate-and-fire models with an absolute refractory period.Biological cybernetics · 2024Article
- The steady state and response to a periodic stimulation of the firing rate for a theta neuron with correlated noise.Journal of computational neuroscience · 2023Article
- Mean-return-time phase of a stochastic oscillator provides an approximate renewal description for the associated point process.Biological cybernetics · 2022Review
- Interspike interval correlations in neuron models with adaptation and correlated noise.PLoS computational biology · 2021Article
- Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.Frontiers in computational neuroscience · 2018Article
- Statistical structure of neural spiking under non-Poissonian or other non-white stimulation.Journal of computational neuroscience · 2015Article
- Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity.Frontiers in computational neuroscience · 2014Article
- Patterns of interval correlations in neural oscillators with adaptation.Frontiers in computational neuroscience · 2013Article
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5 authors.
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Abstract
Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing), along with Gaussian narrow-band noise (a simple example of stochastic oscillations), and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs) and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.
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