Laboratoire de Physique Théorique

de la Matière Condensée

Alessandro Torcini (CY Cergy Paris Université)

Next Generation Neural Mass Models

I will first give a brief overview of the next generation neural mass models, which represent a complete new perspective for the development of exact mean field models of heterogenous spiking neural networks [1]. Then I will report recent results on the application of this formalism to reproduce relevant phenomena in neuroscience ranging from cross-frequency coupling [2] to theta-nested gamma oscillations [3], from slow and fast gamma oscillations [4] to synaptic-based working memory [5]. I will finally show how these neural masses can be extended to capture fluctuations driven phenomena induced by dynamical sources of disorder, naturally present in brain circuits, such as background noise and current fluctuations due to the sparsness in the connections [6-8].

[1] Complete classification of the macroscopic behavior of a heterogeneous network of theta neurons, TB Luke, E Barreto, P So, Neural computation 25 (12), 3207-3234 1482013 (2013); Derivation of a neural field model from a network of theta neurons, CR Laing, Physical Review E 90 (1), 010901 (2014); Montbrió, Ernest, Diego Pazó, Alex Roxin. “Macroscopic description for networks of spiking neurons.” Physical Review X 5.2 (2015): 021028.
[2] A.Ceni, S. Olmi, AT, D. Angulo Garcia, “Cross frequency coupling in next generation inhibitory neural mass models”, Chaos , 30, 053121 (2020)
[3] M. Segneri, H.Bi, S. Olmi, AT, “Theta-nested gamma oscillations in next generation neural mass models”, Frontiers in Computational Neuroscience , 14:47 (2020)
[4] H. Bi, M. Segneri, M. di Volo, AT, “Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons”, Physical Review Research ,2, 013042 (2020)
[5] H. Taher, AT, S. Olmi, “Exact neural mass model for synaptic-based working memory”, PLOS Computational Biology , 16(12): e1008533 (2020)
[6] M. di Volo, AT, “Transition from asynchronous to oscillatory dynamics in balanced spiking networks with instantaneous synapses”, Phys. Rev. Lett. 121 , 128301 (2018)
[7] D. Goldobin, M diVolo, AT, “A reduction methodology for fluctuation driven population dynamics”, Physical Review Letters 127,038301 (2021)
[8] M. di Volo, M. Segneri, D. Goldobin, A. Politi, AT, “Coherent oscillations in balanced neural networks driven by endogenous fluctuations”, Chaos 32, 023120 (2022)