Are spiking neural networks resilient to internal faults? A comparison with other neural network models and an analysis of possible solutions to increase resilience. Spiking neural networks (SNNs) are a new type of computational model whose potential is worth examining due to their similarity to the biological brain, of which AI researchers hope to harness the power and capabilities. These networks achieve comparable performances and accuracy to traditional neural networks in temporal data-related tasks while employing fewer resources. Maintaining the comparison with the biological brain, a question arises spontaneously: Are spiking neural networks able to retain the biological brain capability of resiliency, allowing it to remain functional even when damaged? The question is crucial when implementing artificial neural networks since they play a role in many aspects of our daily lives, including safety-critical areas like self-driving cars, disease detection, and more.