Heterogeneous populations of quadratic integrate-and-fire neurons: on the generality of Lorentzian distributions
Bastian Pietras, Ernest Montbri\'o

TL;DR
This paper investigates the effects of Lorentzian versus other heterogeneity distributions on the collective dynamics of quadratic integrate-and-fire neuron populations, revealing conditions under which Lorentzian heterogeneity leads to unique behaviors.
Contribution
It demonstrates the analytical advantages of Lorentzian heterogeneity and compares its impact on neural population dynamics to other distributions, especially under gap junction coupling.
Findings
Lorentzian heterogeneity simplifies mean-field analysis.
Different heterogeneities generally produce similar dynamics.
Lorentzian heterogeneity causes nonuniversal behavior with gap junctions.
Abstract
Over the last decade, next-generation neural mass models have become increasingly prominent in mathematical neuroscience. These models link microscopic dynamics with low-dimensional systems of so-called firing rate equations that exactly capture the collective dynamics of large populations of heterogeneous quadratic integrate-and-fire (QIF) neurons. A particularly tractable type of heterogeneity is the distribution of the QIF neurons' excitability parameters, or inputs, according to a Lorentzian. While other distributions -- such as those approximating Gaussian or uniform distributions -- admit to exact mean-field reductions, they result in more complex firing rate equations that are challenging to analyze, and it remains unclear whether they produce comparable collective dynamics. Here, we first demonstrate why Lorentzian heterogeneity is analytically favorable and, second, identify…
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Taxonomy
TopicsNeural Networks and Applications · Quantum chaos and dynamical systems · stochastic dynamics and bifurcation
