A new generation of reduction methods for networks of neurons with complex dynamic phenotypes

Inês C. Guerreiro; Matteo di Volo; Boris Gutkin

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arXiv · 2206.10370

A new generation of reduction methods for networks of neurons with complex dynamic phenotypes

Inês C. Guerreiro·Matteo di Volo·Boris Gutkin

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Abstract

Collective dynamics of spiking networks of neurons has been of central interest to both computation neuroscience and network science. Over the past years a new generation of neural population models based on exact reductions (ER) of spiking networks have been developed. However, most of these efforts have been limited to networks of neurons with simple dynamics (e.g. the quadratic integrate and fire models). Here, we present an extension of ER to conductance-based networks of two-dimensional Izhikevich neuron models. We employ an adiabatic approximation, which allows us to analytically solve the continuity equation describing the evolution of the state of the neural population and thus to reduce model dimensionality. We validate our results by showing that the reduced mean-field description we derived can qualitatively and quantitatively describe the macroscopic behaviour of populations of two-dimensional QIF neurons with different electrophysiological profiles (regular firing, adapting, resonator and type III excitable). Most notably, we apply this technique to develop an ER for networks of neurons with bursting dynamics.

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Inês C. Guerreiro, Matteo di Volo, Boris Gutkin. 2023-10-23. A new generation of reduction methods for networks of neurons with complex dynamic phenotypes. https://arxiv.org/abs/2206.10370

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