Interacting Hawkes processes with multiplicative inhibition
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Abstract (arXiv)
In the present work, we introduce a general class of mean-field interacting nonlinear Hawkes processes modelling the reciprocal interactions between two neuronal populations, one excitatory and one inhibitory. The model incorporates two features: inhibition, which acts as a multiplicative factor onto the intensity of the excitatory population and additive retroaction from the excitatory neurons onto the inhibitory ones. We give first a detailed analysis of the well-posedness of this interacting system as well as its dynamics in large population. The second aim of the paper is to give a rigorous analysis of the longtime behavior of the mean-field limit process. We provide also numerical evidence that inhibition and retroaction may be responsible for the emergence of limit cycles in such system.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 May 2021.
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Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 1.
7 papers.
- 2023On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Datacited by 14
- 2023Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principlescited by 8
- 2024Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computingcited by 2
- 2024Nonparametric estimation of Hawkes processes with RKHSscited by 2
- 2023A neural network based model for multi-dimensional nonlinear Hawkes processescited by 1
- 2025Deep spatio-temporal point processes: Advances and new directionscited by 1
- 2026Poisson Autoregression on a Large Network with a Stochastic Block Model Structurecited by 0
- 2026Poisson Autoregression on a Large Network with a Stochastic Block Model Structure4 Oct 2026
- 2025Deep spatio-temporal point processes: Advances and new directions8 Apr 2025
- 2024Nonparametric estimation of Hawkes processes with RKHSs1 Nov 2024
- 2024Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing8 Jul 2024
- 2023On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data29 Jun 2023
- 2023A neural network based model for multi-dimensional nonlinear Hawkes processes6 Mar 2023
- 2023Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles22 Feb 2023