Maskeen, Jaskirat SinghLashkare, Sandip2025-08-282025-08-282025-06-01http://arxiv.org/abs/2506.19377https://d8.irins.org/handle/IITG2025/19901We develop a unified platform to evaluate Ideal, Linear, and Non-linear \text{Pr}_{0.7}\text{Ca}_{0.3}\text{MnO}_{3} memristor-based synapse models, each getting progressively closer to hardware realism, alongside four STDP learning rules in a two-layer SNN with LIF neurons and adaptive thresholds for five-class MNIST classification. On MNIST with small train set and large test set, our two-layer SNN with ideal, 25-state, and 12-state nonlinear memristor synapses achieves 92.73 %, 91.07 %, and 80 % accuracy, respectively, while converging faster and using fewer parameters than comparable ANN/CNN baselines.en-USNeuromorphic computingSpiking neural networksSpike-timing-dependent-plasticityPattern recognitionMNIST classificationSynapse modelse-Printe-Print123456789/435