ArticleJournal of computational neuroscience2015
A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- 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
- 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
- Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation.PLoS computational biology · 2017Article
- Intrinsic and Network Mechanisms Constrain Neural Synchrony in the Moth Antennal Lobe.Frontiers in physiology · 2016Article
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Authors and funding
2 authors.
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Abstract
In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.
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