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Complex Spiking Neural Network Evaluated by Injury Resistance Under Stochastic Attacks.

Lei Guo1,2, Chongming Li1,2, Huan Liu1,2

  • 1Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China.

Brain Sciences
|February 26, 2025
PubMed
Summary

This study introduces the complex spiking neural network (Com-SNN), a bio-plausible brain-inspired model that enhances injury resistance. The Com-SNN demonstrates superior resilience compared to other models, highlighting the importance of synaptic plasticity and network topology.

Keywords:
brain-inspired modelscomplex network topologyinjury resistanceinjury-resistance mechanismspiking neural networksynaptic plasticity

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Network Science

Background:

  • Brain-inspired models are vital for AI but struggle with environmental complexities and lack bio-plausibility.
  • Enhancing the injury resistance of artificial neural networks is crucial for robust AI systems.
  • Human brains exhibit self-adaptive abilities under injury, offering a blueprint for resilient AI.

Purpose of the Study:

  • To propose a novel brain-inspired model, the complex spiking neural network (Com-SNN), with enhanced bio-plausibility and injury resistance.
  • To investigate the injury-resistance mechanisms within the Com-SNN, focusing on synaptic plasticity and network topology.
  • To compare the injury resistance of the Com-SNN against alternative spiking neural networks (SNNs) under simulated attacks.

Main Methods:

  • Developed the Com-SNN model with topology inspired by biological brain networks, utilizing Izhikevich neuron models and time-delayed synaptic plasticity.
  • Evaluated Com-SNN injury resistance using two metrics and compared it with other SNNs under simulated neuron removal (stochastic attacks).
  • Analyzed synaptic plasticity dynamics and topological characteristics of the Com-SNN during stochastic attacks to understand resilience mechanisms.

Main Results:

  • The Com-SNN exhibited significantly superior injury resistance compared to SNNs with alternative topologies.
  • Experimental results confirm the potential of the proposed model to improve the overall injury resistance of spiking neural networks.
  • The study successfully demonstrated the enhanced resilience of the Com-SNN under simulated physical attacks.

Conclusions:

  • Synaptic plasticity is identified as a fundamental factor contributing to the injury resistance of brain-inspired models.
  • Network topology plays a critical role in determining the resilience and robustness of spiking neural networks.
  • The findings provide insights into developing more robust and bio-plausible artificial intelligence systems.