Preprints

  1. Heterogeneous populations of quadratic integrate-and-fire neurons: on the generality of Lorentzian distributions
    B. Pietras, E. Montbrió
    Preprint, 2024. [PDF, arXiv]

    2. Journal articles

    2026

  2. Synchronization modes in bipartite oscillator networks
    P. Pomés, B. Pietras, E. Montbrió
    Phys. Rev. Research 8, 033124, 2026. [DOI, PDF, Supp.Mat., arXiv]

    3. 2025

  3. Low-dimensional model for adaptive networks of spiking neurons
    B. Pietras, P. Clusella, E. Montbrió
    Editor's Suggestion in: Phys. Rev. E 111, 014422, 2025. [DOI, PDF, arXiv]

    4. 2024

  4. Pulse shape and voltage-dependent synchronization in spiking neuron networks
    B. Pietras
    Neural Computation, 2024. [DOI, PDF, Appendix (Proofs), arXiv]

    5. 2023

  5. Exact finite-dimensional description for networks of globally coupled spiking neurons
    B. Pietras, R. Cestnik, A. Pikovsky
    Phys. Rev. E., 2023. [DOI, PDF, arXiv]

    6. 2022

  6. Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity
    B. Pietras, V. Schmutz, T. Schwalger
    PLoS Comput. Biol., 2022. [DOI, PDF, arXiv]

  7. Kuramoto model for populations of quadratic integrate-and-fire neurons with chemical and electrical coupling
    P. Clusella, B. Pietras, E. Montbrió
    Chaos, 2022. [DOI, PDF, arXiv]

    8. 2020

  8. Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons
    B. Pietras, N. Gallice, T. Schwalger
    Editor's Suggestion in: Phys. Rev. E., 2020. [DOI, PDF, arXiv]

    9. 2019

  9. An exact firing rate model reveals the differential effects of chemical versus electrical synapses in spiking networks
    B. Pietras, F. Devalle, A. Roxin, A. Daffertshofer, E. Montbrió
    Phys. Rev. E., 2019. [DOI, PDF, arXiv]

  10. Network dynamics of coupled oscillators and phase reduction techniques
    B. Pietras, A. Daffertshofer
    Physics Reports, 2019. [DOI, PDF]

    11. 2018

  11. First-order phase transitions in the Kuramoto model with compact bimodal frequency distributions
    B. Pietras, N. Deschle, A. Daffertshofer
    Phys. Rev. E, 2018. [DOI, PDF, arXiv]

  12. Scale-freeness or partial synchronization in neural mass phase oscillator networks: Pick one of two?
    A. Daffertshofer, R. Ton, B. Pietras, M. L. Kringelbach, G. Deco
    NeuroImage, 2018. [DOI, PDF]

    13. 2016

  13. Equivalence of coupled networks and networks with multimodal frequency distributions: Conditions for the bimodal and trimodal case
    B. Pietras, N. Deschle, A. Daffertshofer
    Phys. Rev. E, 2016. [DOI, PDF, arXiv]

  14. Ott-Antonsen attractiveness for parameter-dependent oscillatory systems
    B. Pietras, A. Daffertshofer
    Chaos, 2016. [DOI, PDF, arXiv]

    15. in books

Book chapters

  1. Reduced Phase Models of Oscillatory Neural Networks
    B. Pietras, A. Daffertshofer
    in: Stefanovska A. and McClintock P.V.E. (eds) Physics of Biological Oscillators. Understanding Complex Systems, Springer, Cham, 2021. [DOI, PDF]

  2. Phase synchronization in neural systems
    A. Daffertshofer, B. Pietras
    in: Meyers R. (eds) Encyclopedia of Complexity and Systems Science, Springer, Berlin, Heidelberg, 2020. [DOI, PDF]


    3. phd

PhD thesis

Modeling phase synchronization of interacting neuronal populations: From phase reductions to collective behavior of oscillatory neural networks
B. Pietras, 2018
Promotoren: A. Daffertshofer (VU Amsterdam), A. Stefanovska (Lancaster University)
Copromotor: P. V. E. McClintock (Lancaster University)
[LINK]