ArticleJournal of computational neuroscience2013
Dynamics of spiking neurons: between homogeneity and synchrony.
Article in Journal of computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Efficient models of cortical activity via local dynamic equilibria and coarse-grained interactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Multilevel monte carlo for cortical circuit models.Journal of computational neuroscience · 2022Article
- Malleability of gamma rhythms enhances population-level correlations.Journal of computational neuroscience · 2021Article
- Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds.Frontiers in computational neuroscience · 2021Article
- 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
- How well do reduced models capture the dynamics in models of interacting neurons?Journal of mathematical biology · 2019Article
- Intrinsic and Network Mechanisms Constrain Neural Synchrony in the Moth Antennal Lobe.Frontiers in physiology · 2016Article
- A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.Journal of computational neuroscience · 2015Article
- Emergent spike patterns in neuronal populations.Journal of computational neuroscience · 2015Article
- A coarse-grained framework for spiking neuronal networks: between homogeneity and synchrony.Journal of computational neuroscience · 2014Article
- Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks.Journal of computational neuroscience · 2014Article
- Emergent dynamics in a model of visual cortex.Journal of computational neuroscience · 2013Article
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2 authors.
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Abstract
Randomly connected networks of neurons driven by Poisson inputs are often assumed to produce "homogeneous" dynamics, characterized by largely independent firing and approximable by diffusion processes. At the same time, it is well known that such networks can fire synchronously. Between these two much studied scenarios lies a vastly complex dynamical landscape that is relatively unexplored. In this paper, we discuss a phenomenon which commonly manifests in these intermediate regimes, namely brief spurts of spiking activity which we call multiple firing events (MFE). These events do not depend on structured network architecture nor on structured input; they are an emergent property of the system. We came upon them in an earlier modeling paper, in which we discovered, through a careful benchmarking process, that MFEs are the single most important dynamical mechanism behind many of the V1 phenomena we were able to replicate. In this paper we explain in a simpler setting how MFEs come about, as well as their potential dynamic consequences. Although the mechanism underlying MFEs cannot easily be captured by current population dynamics models, this phenomena should not be ignored during analysis; there is a growing body of evidence that such collaborative activity may be a key towards unlocking the possible functional properties of many neuronal networks.
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