ArticleJournal of computational neuroscience2015
Statistical structure of neural spiking under non-Poissonian or other non-white stimulation.
Article in Journal of computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 59 citations in OpenAlex.
- Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.Biological cybernetics · 2026Article
- Deterministic, stochastic, and mean-field PDE models in neuroscience.Frontiers in computational neuroscience · 2026Review
- Theta oscillations optimize a speed-precision trade-off in phase coding neurons.PLoS computational biology · 2024Article
- An integrate-and-fire approach to CaBiophysical journal · 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
- Mean-return-time phase of a stochastic oscillator provides an approximate renewal description for the associated point process.Biological cybernetics · 2022Review
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
- Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach.Biological cybernetics · 2021Article
- Interspike interval correlations in neuron models with adaptation and correlated noise.PLoS computational biology · 2021Article
- Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity.Journal of mathematical neuroscience · 2020Article
- Striatal network modeling in Huntington's Disease.PLoS computational biology · 2020Article
- Dimensional reduction in networks of non-Markovian spiking neurons: Equivalence of synaptic filtering and heterogeneous propagation delays.PLoS computational biology · 2019Article
- The Impact of Frequency Scale on the Response Sensitivity and Reliability of Cortical Neurons to 1/fFrontiers in cellular neuroscience · 2019Article
- 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
- Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.Frontiers in computational neuroscience · 2018Article
- From the statistics of connectivity to the statistics of spike times in neuronal networks.Current opinion in neurobiology · 2017Review
- Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise.Journal of computational neuroscience · 2017Article
- Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation.PLoS computational biology · 2017Article
- Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.PLoS computational biology · 2017Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nerve cells in the brain generate sequences of action potentials with a complex statistics. Theoretical attempts to understand this statistics were largely limited to the case of a temporally uncorrelated input (Poissonian shot noise) from the neurons in the surrounding network. However, the stimulation from thousands of other neurons has various sorts of temporal structure. Firstly, input spike trains are temporally correlated because their firing rates can carry complex signals and because of cell-intrinsic properties like neural refractoriness, bursting, or adaptation. Secondly, at the connections between neurons, the synapses, usage-dependent changes in the synaptic weight (short-term plasticity) further shape the correlation structure of the effective input to the cell. From the theoretical side, it is poorly understood how these correlated stimuli, so-called colored noise, affect the spike train statistics. In particular, no standard method exists to solve the associated first-passage-time problem for the interspike-interval statistics with an arbitrarily colored noise. Assuming that input fluctuations are weaker than the mean neuronal drive, we derive simple formulas for the essential interspike-interval statistics for a canonical model of a tonically firing neuron subjected to arbitrarily correlated input from the network. We verify our theory by numerical simulations for three paradigmatic situations that lead to input correlations: (i) rate-coded naturalistic stimuli in presynaptic spike trains; (ii) presynaptic refractoriness or bursting; (iii) synaptic short-term plasticity. In all cases, we find severe effects on interval statistics. Our results provide a framework for the interpretation of firing statistics measured in vivo in the brain.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.