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A next generation neural mass model with neuromodulation

  • Damien Depannemaecker
  • , Chloé Duprat
  • , Gabriele Casagrande
  • , Marisa Saggio
  • , Anastasios Polykarpos Athanasiadis
  • , Marianna Angiolelli
  • , Carola Sales Carbonell
  • , Huifang Wang
  • , Spase Petkoski
  • , Pierpaolo Sorrentino
  • , Anthony Randal McIntosh
  • , Hiba Sheheitli
  • , Viktor Jirsa

Research output: Contribution to journalArticlepeer-review

Abstract

The study of brain activity and its function requires the development of computational models alongside experimental investigations to explore different effects of multiple mechanisms at play in the central nervous system. Chemical neuromodulators, such as dopamine, play central roles in regulating the dynamics of neuronal populations. In this work, we propose a modular framework to capture the effects of neuromodulators at the neural mass level. Using this framework, we formulate a specific model for dopamine dynamics affecting D1-type receptors. We detail the dynamical repertoire associated with dopamine concentration evolution and characterize the transitions across qualitatively different oscillatory states as a function of relevant parameters, along with the variations in frequency of the emergent multi-scale oscillations.

Original languageEnglish (US)
Pages (from-to)23-43
Number of pages21
JournalJournal of Computational Neuroscience
Volume54
Issue number1
DOIs
StatePublished - Mar 2026

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.

Keywords

  • Dopamine
  • Mean-field
  • Neural mass model
  • Neuromodulation

PubMed: MeSH publication types

  • Journal Article

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