Table 8.

Performance comparison with other state-of-the-art SNN models

Reference and Year	Dataset Used	Model/Algorithm Used	Accuracy (in %)
López-Vázquez et al. [36], 2019	UCI Machine Learning Repository for PD	Grammatical Evolution (GE)-based SNN	88.75%
Kerman et al. [37], 2022	Spike data collected from different regions of Brain	Spiking MLP	93%
Siddique et al. [38], 2023	Spike data from the neurons in the subthalamic nucleus region	Spiking LSTM	99.48%
Proposed model [Dataset#1]	UCI Machine Learning Repository for PD [51]	Time-varying Synaptic Efficacy Function based SNN (SEFRON)	100%
Proposed model [Dataset#2]	UCI Machine Learning Repository: Parkinson Dataset with replicated acoustic features [52]	Time-varying Synaptic Efficacy Function based SNN (SEFRON)	91.94%