SMT-based modeling and verification of spiking neural networks: a case study

DOI10.1007/978-3-031-24950-1_2zbMath1529.68151OpenAlexW4316662741MaRDI QIDQ6132487

Swarup K. Mohalik, Ansuman Banerjee, Sumana Ghosh, Soham Banerjee

Publication date: 17 August 2023

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-031-24950-1_2

zbMATH Keywords

verificationsatisfiability modulo theoryspiking neural networksadversarial robustness

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Specification and verification (program logics, model checking, etc.) (68Q60) Biologically inspired models of computation (DNA computing, membrane computing, etc.) (68Q07)

Cites Work

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• Modelling and verification of weighted spiking neural systems
• Reluplex: an efficient SMT solver for verifying deep neural networks
• An abstraction-based framework for neural network verification
• Verification of Temporal Properties of Neuronal Archetypes Modeled as Synchronous Reactive Systems

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