Maskeen, Jaskirat SinghJaskirat SinghMaskeenLashkare, SandipSandipLashkare2026-01-122026-01-122026-01-01[9783032045577]10.1007/978-3-032-04558-4_412-s2.0-105016572047http://repository.iitgn.ac.in/handle/IITG2025/33794We develop a unified platform to evaluate Ideal, Linear, and Non-linear Pr<inf>0.7</inf>Ca<inf>0.3</inf>MnO<inf>3</inf> 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 non-linear memristor synapses achieves 92.73 %, 91.07 %, and 80 % accuracy, respectively, while converging faster and using fewer parameters than comparable ANN/CNN baselines.falseMNIST Classification | Neuromorphic Computing | Pattern Recognition | Spike-Timing-Dependent-Plasticity | Spiking Neural Networks | Synapse ModelsBook Series16113349509-52020260cpConference Paper