SMT-Based Modeling and Verification of Spiking Neural Networks: A Case Study

Document Type

Conference Article

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

In this paper, we present a case study on modeling and verification of Spiking Neural Networks (SNN) using Satisfiability Modulo Theory (SMT) solvers. SNN are special neural networks that have great similarity in their architecture and operation with the human brain. These networks have shown similar performance when compared to traditional networks with comparatively lesser energy requirement. We discuss different properties of SNNs and their functioning. We then use Z3, a popular SMT solver to encode the network and its properties. Specifically, we use the theory of Linear Real Arithmetic (LRA). Finally, we present a framework for verification and adversarial robustness analysis and demonstrate it on the Iris and MNIST benchmarks.

First Page

25

Last Page

43

DOI

10.1007/978-3-031-24950-1_2

Publication Date

1-1-2023

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