ArticleJournal of computational neuroscience2014
Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks.
Article in Journal of computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Multilevel monte carlo for cortical circuit models.Journal of computational neuroscience · 2022Article
- Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds.Frontiers in computational neuroscience · 2021Article
- Dimensional reduction of emergent spatiotemporal cortical dynamics via a maximum entropy moment closure.PLoS computational biology · 2020Article
- Stochastic neural field model: multiple firing events and correlations.Journal of mathematical biology · 2019Article
- A coarse-graining framework for spiking neuronal networks: from strongly-coupled conductance-based integrate-and-fire neurons to augmented systems of ODEs.Journal of computational neuroscience · 2019Article
- Intrinsic and Network Mechanisms Constrain Neural Synchrony in the Moth Antennal Lobe.Frontiers in physiology · 2016Article
- A coarse-grained framework for spiking neuronal networks: between homogeneity and synchrony.Journal of computational neuroscience · 2014Article
- Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.Frontiers in neural circuits · 2014Article
- Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Randomly connected populations of spiking neurons display a rich variety of dynamics. However, much of the current modeling and theoretical work has focused on two dynamical extremes: on one hand homogeneous dynamics characterized by weak correlations between neurons, and on the other hand total synchrony characterized by large populations firing in unison. In this paper we address the conceptual issue of how to mathematically characterize the partially synchronous "multiple firing events" (MFEs) which manifest in between these two dynamical extremes. We further develop a geometric method for obtaining the distribution of magnitudes of these MFEs by recasting the cascading firing event process as a first-passage time problem, and deriving an analytical approximation of the first passage time density valid for large neuron populations. Thus, we establish a direct link between the voltage distributions of excitatory and inhibitory neurons and the number of neurons firing in an MFE that can be easily integrated into population-based computational methods, thereby bridging the gap between homogeneous firing regimes and total synchrony.
Indexed as
Identifiers
23851661What OpenQuestion holds
Registered trials
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.