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Article Dans Une Revue Physical Review Letters Année : 2021

A reduction methodology for fluctuation driven population dynamics

Résumé

Lorentzian distributions have been largely employed in statistical mechanics to obtain exact results for heterogeneous systems. Analytic continuation of these results is impossible even for slightly deformed Lorentzian distributions due to the divergence of all the moments (cumulants). We have solved this problem by introducing a "pseudocumulants" expansion. This allows us to develop a reduction methodology for heterogeneous spiking neural networks subject to extrinsic and endogenous fluctuations, thus obtaining a unified mean-field formulation encompassing quenched and dynamical sources of disorder.
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Dates et versions

hal-03137115 , version 1 (10-02-2021)

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Citer

Denis S Goldobin, Matteo Di Volo, Alessandro Torcini. A reduction methodology for fluctuation driven population dynamics. Physical Review Letters, 2021, 127, pp.038301. ⟨10.1103/PhysRevLett.127.038301⟩. ⟨hal-03137115⟩
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