dorsal/arxiv
View SchemaMacroscopic dynamics of quadratic integrate-and-fire neurons subject to correlated noise
| Authors | Hui Wang, Chunming Zheng |
|---|---|
| Categories | |
| ArXiv ID | 2601.10032vv1 |
| URL | https://arxiv.org/abs/2601.10032 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
The presence of correlated noise, arising from a mixture of independent fluctuations and a common noisy input shared across the neural population, is a ubiquitous feature of neural circuits, yet its impact on collective network dynamics remains poorly understood. We analyze a network of quadratic integrate-and-fire neurons driven by Gaussian noise with a tunable degree of correlation. Using the cumulant expansion method, we derive a reduced set of effective mean-field equations that accurately describe the evolution of the population's mean firing rate and membrane potential. Our analysis reveals a counterintuitive phenomenon: increasing the noise correlation strength suppresses the mean network activity, an effect we term correlated-noise-inhibited spiking. Furthermore, within a specific parameter regime, the network exhibits metastability, manifesting itself as spontaneous, noise-driven transitions between distinct high- and low-activity states. These results provide a theoretical framework for reducing the dynamics of complex stochastic networks and demonstrate how correlated noise can fundamentally regulate macroscopic neural activity, with implications for understanding state transitions in biological systems.
{
"annotation_id": "33105966-fcec-426e-8800-e2c10f825d92",
"date_created": "2026-02-17T05:53:23.788000Z",
"date_modified": "2026-02-17T05:53:23.788000Z",
"file_hash": "c7f34d202b104708342943d2feaff60312fc086ee5f9615f95e2708aec3f5b89",
"private": false,
"record": {
"abstract": "The presence of correlated noise, arising from a mixture of independent fluctuations and a common noisy input shared across the neural population, is a ubiquitous feature of neural circuits, yet its impact on collective network dynamics remains poorly understood. We analyze a network of quadratic integrate-and-fire neurons driven by Gaussian noise with a tunable degree of correlation. Using the cumulant expansion method, we derive a reduced set of effective mean-field equations that accurately describe the evolution of the population\u0027s mean firing rate and membrane potential. Our analysis reveals a counterintuitive phenomenon: increasing the noise correlation strength suppresses the mean network activity, an effect we term correlated-noise-inhibited spiking. Furthermore, within a specific parameter regime, the network exhibits metastability, manifesting itself as spontaneous, noise-driven transitions between distinct high- and low-activity states. These results provide a theoretical framework for reducing the dynamics of complex stochastic networks and demonstrate how correlated noise can fundamentally regulate macroscopic neural activity, with implications for understanding state transitions in biological systems.",
"arxiv_id": "2601.10032",
"authors": [
"Hui Wang",
"Chunming Zheng"
],
"categories": [
"q-bio.NC",
"cond-mat.stat-mech",
"nlin.AO"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Macroscopic dynamics of quadratic integrate-and-fire neurons subject to correlated noise",
"url": "https://arxiv.org/abs/2601.10032",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "5ed58c98-dfa0-465d-ba2f-f6c8f3f8afd0",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}