ArticlePLoS computational biology2010
How noisy adaptation of neurons shapes interspike interval histograms and correlations.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings.Biological cybernetics · 2024Article
- Temporal Patterns of Angular Displacement of Endosomes: Insights into Motor Protein Exchange Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Phase-Dependent Response to Electrical Stimulation of Cortical Networks during Recurrent Epileptiform Short Discharge Generation In Vitro.International journal of molecular sciences · 2024Article
- Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Hybrid Bionic Nerve Interface for Application in Bionic Limbs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- 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
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains.Biological cybernetics · 2022Article
- 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
- Resolving molecular contributions of ion channel noise to interspike interval variability through stochastic shielding.Biological cybernetics · 2021Article
- Gap junctions set the speed and nucleation rate of stage I retinal waves.PLoS computational biology · 2019Article
- Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks.PLoS computational biology · 2019Article
- Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains.Entropy (Basel, Switzerland) · 2018Article
- Transmission of temporally correlated spike trains through synapses with short-term depression.PLoS computational biology · 2018Article
- An integrate-and-fire model to generate spike trains with long-range dependence.Journal of computational neuroscience · 2018Article
- Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons.Scientific reports · 2018Article
- Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.PLoS computational biology · 2017Article
- Modulation of neuronal dynamic range using two different adaptation mechanisms.Neural regeneration research · 2017Article
- Stochastic slowly adapting ionic currents may provide a decorrelation mechanism for neural oscillators by causing wander in the intrinsic period.Journal of neurophysiology · 2016Article
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4 authors.
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Abstract
Channel noise is the dominant intrinsic noise source of neurons causing variability in the timing of action potentials and interspike intervals (ISI). Slow adaptation currents are observed in many cells and strongly shape response properties of neurons. These currents are mediated by finite populations of ionic channels and may thus carry a substantial noise component. Here we study the effect of such adaptation noise on the ISI statistics of an integrate-and-fire model neuron by means of analytical techniques and extensive numerical simulations. We contrast this stochastic adaptation with the commonly studied case of a fast fluctuating current noise and a deterministic adaptation current (corresponding to an infinite population of adaptation channels). We derive analytical approximations for the ISI density and ISI serial correlation coefficient for both cases. For fast fluctuations and deterministic adaptation, the ISI density is well approximated by an inverse Gaussian (IG) and the ISI correlations are negative. In marked contrast, for stochastic adaptation, the density is more peaked and has a heavier tail than an IG density and the serial correlations are positive. A numerical study of the mixed case where both fast fluctuations and adaptation channel noise are present reveals a smooth transition between the analytically tractable limiting cases. Our conclusions are furthermore supported by numerical simulations of a biophysically more realistic Hodgkin-Huxley type model. Our results could be used to infer the dominant source of noise in neurons from their ISI statistics.
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