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.
Original language | English (US) |
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Pages (from-to) | 355-404 |
Number of pages | 50 |
Journal | Journal of Computational Neuroscience |
Volume | 38 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2015 |
Keywords
- Coarse grain
- Ensemble average
- Integrate and fire
- Spiking network
ASJC Scopus subject areas
- Sensory Systems
- Cognitive Neuroscience
- Cellular and Molecular Neuroscience