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Related Experiment Video

Updated: Jun 15, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Random heterogeneous spiking neural network for adversarial defense.

Jihang Wang1,2,3,4, Dongcheng Zhao1,3,4,5, Chengcheng Du1,6,3,4

  • 1Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Iscience
|June 13, 2025
PubMed
Summary

Random Heterogeneous Spiking Neural Networks (RandHet-SNN) enhance robustness against adversarial attacks by introducing neuron diversity. This approach boosts security for spiking neural networks without sacrificing performance.

Keywords:
Computer scienceEngineeringPhysics

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning Security

Background:

  • Spiking neural networks (SNNs) mimic biological neurons but are vulnerable to adversarial attacks, similar to artificial neural networks (ANNs).
  • Ensuring the robustness of SNNs is crucial for their reliable deployment in real-world applications.

Purpose of the Study:

  • To develop a novel SNN architecture, the Random Heterogeneous Spiking Neural Network (RandHet-SNN), to improve defense against adversarial examples.
  • To leverage biological neural system characteristics like heterogeneity and stochasticity for enhanced network security.

Main Methods:

  • Introduced neuron-level diversity in SNNs by incorporating randomized time decay constants.
  • Enabled unique temporal properties for each neuron during every forward pass.
  • Evaluated RandHet-SNN performance against various adversarial attacks.

Main Results:

  • RandHet-SNN demonstrated significant enhancement in network robustness against adversarial attacks.
  • The proposed architecture maintained minimal impact on the network's clean accuracy.
  • Results highlight the effectiveness of neuron diversity in bolstering SNN security.

Conclusions:

  • RandHet-SNN offers a promising solution for creating robust and energy-efficient SNNs in adversarial conditions.
  • The study validates the potential of biologically inspired heterogeneity for improving AI security.