A Particle Swarm Optimization-Based Interpretable Spiking Neural Classifier with Time-Varying Weights

Mohammed Thousif (), Shirin Dora and Suresh Sundaram
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Mohammed Thousif: Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India
Shirin Dora: Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
Suresh Sundaram: Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India

Mathematics, 2024, vol. 12, issue 18, 1-16

Abstract: This paper presents an interpretable, spiking neural classifier (IpT-SNC) with time-varying weights. IpT-SNC uses a two-layered spiking neural network (SNN) architecture in which weights of synapses are modeled using amplitude-modulated, time-varying Gaussian functions. Self-regulated particle swarm optimization (SRPSO) is used to update the amplitude, width, and centers of the Gaussian functions and thresholds of neurons in the output layer. IpT-SNC has been developed to improve the interpretability of spiking neural networks. The time-varying weights in IpT-SNC allow us to describe the rationale behind predictions in terms of specific input spikes. The performance of IpT-SNC is evaluated on ten benchmark datasets in the UCI machine learning repository and compared with the performance of other learning algorithms. According to the performance results, IpT-SNC enhances classification performance on testing datasets from a minimum of 0.5% to a maximum of 7.7%. The significance level of IpT-SNC with other learning algorithms is evaluated using statistical tests like the Friedman test and the paired t -test. Furthermore, on the challenging real-world BCI (Brain Computer Interface) competition IV dataset, IpT-SNC outperforms current classifiers by about 8% in terms of classification accuracy. The results indicate that IpT-SNC has better generalization performance than other algorithms.

Keywords: self-regulated particle swarm optimization; spiking neural network; classifier; interpretable (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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