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The key nonidealities include electric\u2010field noise, conductance noise, and conductance drift. This study investigates the robustness of fully connected, convolutional, residual, and spike\u2010timing\u2010dependent plasticity\u2010based SNNs against hardware nonidealities using the MNIST, Fashion MNIST, and CIFAR10 datasets. In response to these challenges, a novel hybrid residual SNN (HRSNN) is proposed that incorporates a new neuron circuit and a weight\u2010dependent loss function. The HRSNN in a high\u2010intensity noise environment is evaluated using the neuromorphic DVS128 Gesture dataset. The achieved accuracy rate of 92.71% is only 2.15% lower than that of the noise\u2010free environment. 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