Next generation neural mass models: bridging the scales from micro to macroscopic dynamics

Founding Body: Human Brain project, co-funded by
European Union Horizon 2020
Total grant: € 96k
Principal Investigator: Simona Olmi
Other participants: Viktor Jirsa (University of Aix-Marseille)
Project duration: 2021-2023

The project aims to be a clever blend between classic unifying multi-scale frameworks and pyramid-style approaches thanks to the following strengths:

  • The exact reduction dimension techniques at the basis of the next generation neural mass model have been developed for coupled phase oscillators [1] and allow for an exact (analytical) moving upwards through the scales: while keeping the influence of smaller scales on larger ones they level out their inherent complexity.
  • Moving downwards through the scales, more detailed modelling parameters can be used, e.g. to test specific hypotheses. Having a 1:1 correspondence between microscopic and mesoscopic level, it will be easier to map the microscopic results to the relative ones in the regional mean field parameters.
  • The next generation neural mass models (and the relative microscopic models) show multi-stability and various behaviors at multiple time scales. Therefore it will be no more necessary to resort to oscillator models or single neuron models depending on the phenomena required to model (either neural dynamical features or multi-stability).
  • In this framework will be developed a next generation neural mass model encompassing short-term plasticity (STP), which has not yet been implemented in TVB. The resulting plasticity models can then be integrated into single neuron or population models and will serve as a starting point for including more realistic and biologically relevant aspects (e.g. pulsatile interactions or transmission delays). In particular the work plan includes the following steps: a) validation and extension of the next generation neural mass model developed in [2] to take into account finite-size fluctuations of the microscopic synaptic variables [3], synaptic delays [4], electrical coupling via gap junctions [5] and chemical synapses [6]; b) parameter optimization; c) application to Showcase 1 (WP1) in SGA3 to reproduce both resting states and task-related states; d) application to Showcase 2 (WP1) in SGA3 to construct personalized brain models of epileptic patients.

    [1] E. Ott and T. M. Antonsen, Chaos, 18, 037113 (2008).
    [2] H. Taher, A. Torcini and S. Olmi, Exact neural mass model for synaptic-based
    working memory, Plos. Comp. Bio. 16(12), e1008533 (2020).
    [3] V. Schmutz, W. Gerstner, T. Schwalger, J. Math. Neurosci. 10(1), 1–32 (2020).
    [4] F. Devalle, E. Montbrió, D. Pazó, Phys. Rev. E 98(4), 042214 (2018)
    [5] E. Montbrió, D. Pazó, Exact mean-field theory explains the dual role of electrical synapses in collective synchronization, Phys. Rev. Lett. 125(24), 248101 (2020).
    [6] S. Coombes, A. Byrne, In: F. Corinto, A. Torcini, editors. Nonlinear Dynamics in Computational Neuroscience. Springer; 1–16 (2019).