Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI) are the most severe reliability concerns in advanced CMOS devices. Investigating the extent of device degradation in analog and digital circuits has been an ongoing focus of several research groups. However, emerging fields like spiking neural networks (SNN) constituting neuromorphic circuit blocks, lack insight of device degradation impact over its operative lifetime. This work emphasizes the importance of device degradation in a crucial neuromorphic circuit block, the silicon neuron (SiN). A comprehensive degradation analysis on the stimulated SiN recently reported demonstrated significant fault/mismatches incurred when the SiN was on the verge of spiking. Here, we investigate the presence of fault/mismatches when the neuron spikes for different uniformly- and randomly- distributed input strengths. It was realized through key SiN performance parameters in 'Time0' (pre-degradation) and 'Aged' (post-degradation) simulations. Although the neuron circuit functions effectively for most of the applied uniformly-distributed inputs, its detrimental effects were still very apparent in the randomly-distributed inputs potentially impacting signal/information transmission in an SNN. © 2022 IEEE.