Recent Developments for Biologically Realistic Neural Mass Models


Alessandro Torcini | CY Cergy Paris Université, France
Simona Olmi | Istituto dei Sistemi Complessi, Rome, Italy


The workshop will focus on recent developments concerning neural mass models, i.e. mean field models able to reproduce biologically realistic dynamics of networks of spiking neurons [1]. In particular, emphasis will be devoted on one side to mean field models for the balanced state, representing a fundamental   aspect of cortical dynamics [2-6], and on the other side to next generation neural mass models, recently developed, and able to reproduce exactly  the dynamics of spiking neural networks [7].

The workshop aims to favour a comprehension of recently developed neural mass approaches and an open discussion on their relevance and  limits of applicability. In particular, a close comparison with   experimental results will be performed. The topics will range from the emergence of long lasting fluctuations and correlations in balanced cortical circuits [8-9], to theta-gamma nested oscillations [10-11],  from short-term memory [12-13] to neural wave propagation [14].
The inclusion in the neural mass models of realistic aspects of neural dynamics,  such as the presence of background noise, sparseness in the connection [15-16], adaptation [17] and synaptic plasticity [12-13] will also be addressed in details during the workshop.

[1] Carlu et al. Journal of Neurophysiology, 123(3), 1042 (2020).
[2]  C.  van  Vreeswijk  and  H.  Sompolinsky, Science 274, 1724 (1996).
[3]  A. Renart, J. de la Rocha, P. Bartho, L. Hollender, N. Parga,A. Reyes, and K. D. Harris, Science 327, 587 (2010).
[4]  A.  Litwin-Kumar  and  B.  Doiron, Nat.  Neurosci. 15, 1498 (2012).
[5]  R. Rosenbaum and B. Doiron, Phys. Rev. X 4, 021039(2014).
[6]  J.  Kadmon  and  H.  Sompolinsky, Phys.  Rev.  X 5, 041030 (2015)
[7] E. Montbro’, D. Pazo’, and A. Roxin,Phys. Rev. X5, 021028 (2015)
[8] F. Mastrogiuseppe and S. Ostojic, PLOS Computational Biology (2017)
[9] Dahmen, D., Gruen, S., Diesmann, M., & Helias, M. Proceedings of the National Academy of Sciences, 116(26), 13051-13060. (2019)
[10] M. Segneri, H.Bi, S. Olmi, A.Torcini, Frontiers in Computational Neuroscience , 14:47 (2020).
[11] A.Ceni, S. Olmi, A. Torcini, D. Angulo Garcia, Chaos , 30, 053121 (2020).
[12] Schmutz, V., Gerstner, W., & Schwalger, T. The Journal of Mathematical Neuroscience, 10(1), 1-32. (2020)
[13] H. Taher, A. Torcini, S. Olmi, PLOS Computational Biology , 16(12): e1008533 (2020)
[14] Ã Byrne, RD O’Dea, M Forrester, J Ross, S Coombes, Journal of neurophysiology 123 (2), 726-742 (2020).
[15] M. di Volo, A. Torcini, Phys. Rev. Lett. 121 , 128301 (2018)
[16] H. Bi, M. Segneri, M. di Volo, A.Torcini, 2, 013042 (2020)
[17] Di Volo, M., Romagnoni, A., Capone, C., & Destexhe, A. Neural computation, 31(4), 653-680. (2019)

Schedule (CEST)


Simona Olmi| Istituto dei Sistemi Complessi, Rome, Italy
Theta-Nested Gamma Oscillations in Next Generation Neural Mass Models


David Dahmen | Jülich Research Centre, Germany
Coordination and dimensionality of balanced network dynamics


Tilo Schwalger | Technical University Berlin, Germany
Stochastic neural mass models for finite-size populations of spiking neurons


20 min break


Gianluigi Mongillo | Sorbonne University, Paris, France
Optimal storage properties of sparsely connected, balanced networks


Matteo di Volo | CY Cergy Paris Université, France
Emergence of collective oscillations in balanced neural networks due to endogeneous fluctuations


Halgurd Taher | INRIA Sophia Antipolis, Antibes, France
Exact neural mass model for synaptic-based working memory


40 min break


Alain Destexhe| Université Paris-Saclay, Paris, France
A general semi-analytic mean-field framework to model neural populations from small networks of spiking neurons to the whole brain


Áine Byrne | University College Dublin, Ireland
Synaptic plasticity in a next generation neural mass model


Maurizio Mattia | Istituto Superiore di Sanita’, Rome, Italy
Out-of-equilibrium stochastic dynamics of finite-size cell assemblies