Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.06.22.546071v1?rss=1
Authors: Alexandersen, C. G., Duprat, C., Ezzati, A., Houzelstein, P., Ledoux, A., Liu, Y., Saghir, S., Destexhe, A., Tesler, F., Depannemaecker, D.
Abstract:
Mean-field models are a class of models used in computational neuroscience to study the behaviour of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behaviour of "mean field" variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semi-analytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.
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