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Experimental electronic modeling of focal and progressive brain lesion processes using a network of single-transistor chaotic oscillators

Joakim Vianney Ngamsa Tegnitsap

Paul Didier Kamdem Kuate

Karan K. H. Manjunatha

Manyu Zhao

Federico D’Agata

Zeric Tabekoueng Njitacke

Fanshu Fang

Pedro Antonio Valdes-Sosa

Hiroyuki Ito

Stefano Boccaletti

Mattia Frasca

Ludovico Minati*

  • Department of Electrical Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy

  • *Contact author: lminati@uestc.edu.cn, lminati@ieee.org, ludovico.minati@unitn.it
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Phys. Rev. Research 8, 013098 – Published 29 January, 2026

DOI: https://doi.org/10.1103/16f3-t125

Abstract

Understanding how neurodegenerative diseases disrupt brain dynamics requires models capable of capturing the interplay between network structure and functional activity. This study presents a hardware emulation of progressive disconnection, implemented using a physical network of transistor-based chaotic electronic oscillators inspired by a biological neuronal culture. We evaluate several node disruption scenarios under different coupling strengths. These included single-node disruption and progressive damage sequences from core to periphery and periphery to core. The changes in network and node-level dynamics are assessed through the evaluation of synchronization, entropy, spectral content, and graph-theoretic properties. These analyses show that the network’s response to progressive alteration depends strongly on its topology and the coupling strength. The single-node disruption scenario revealed a high level of network resilience. Changes in average phase synchronization and topology remained modest in this scenario, consistent with functional imaging and lesion studies showing early compensatory mechanisms in healthy and mildly impaired brains. While the periphery-to-core scenario causes gradual and delayed deterioration, the core-to-periphery scenario leads to a sharp breakdown in synchronization, as well as structural and functional graph-based metrics. These profiles of degradation are comparable to those observed in neurodegenerative diseases and, in particular, Alzheimer’s disease, characterized by marked damage to central regions. Remarkably, it is found that modularity reaches a peak and then decreases just before the network collapses, a pattern that may be considered a meaningful marker of network degradation. Spectral analyses reveal a progressive shift toward low frequencies and fragmentation of the oscillatory dynamics, characterized by a decrease in high-frequency power and emergence of local frequency peaks. This study provides insights into how topology-dependent degeneration shapes brain rhythms and functional connectivity. It demonstrates that physical oscillator networks are suitable for emulating the progressive breakdown of neural circuits. The obtained insights could form the basis for hypothetical biomarkers.

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Phys. Rev. Research 8, 013098– Published 29 January, 2026
  • Received 31 October 2025
  • Accepted 28 December 2025
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