A neural mass model with direct and indirect excitatory feedback loops: identification of bifurcations and temporal dynamics

Abstract : Neural mass modeling is a part of computational neuroscience that was developed to study the general behavior of a neuronal population. This type of mesoscopic model is able to generate output signals that are comparable to experimental data, such as electroencephalograms. Classically, neural mass models consider two interconnected populations: excitatory pyramidal cells and inhibitory interneurons. However, many authors have included an excitatory feedback on the pyramidal cell population. Two distinct approaches have been developed: a direct feedback on the main pyramidal cell population and an indirect feedback via a secondary pyramidal cell population. In this letter, we propose a new neural mass model that couples these two approaches. We perform a detailed bifurcation analysis and present a glossary of dynamical behaviors and associated time series. Our study reveals that the model is able to generate particular realistic time series that were never pointed out in either simulated or experimental data. Finally, we aim to evaluate the effect of balance between both excitatory feedbacks on the dynamical behavior of the model. For this purpose, we compute the codimension 2 bifurcation diagrams of the system to establish a map of the repartition of dynamical behaviors in a direct versus indirect feedback parameter space. A perspective of this work is, from a given temporal series, to estimate the parameter value range, especially in terms of direct versus indirect excitatory feedback.
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Aurélie Garnier, Alexandre Vidal, Clément Huneau, Habib Benali. A neural mass model with direct and indirect excitatory feedback loops: identification of bifurcations and temporal dynamics. Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2015, 27 (2), pp.329-364. ⟨10.1162/NECO_a_00696⟩. ⟨hal-01112146⟩

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