Artificial neural networks, particularly convolutional neural networks (CNNs), have shown remarkable performance in tasks like image classi- fication. However, their robustness under dendritic degradation remains a challenge. Inspired by neurophysiology, we propose a Probabilis- tic Dendritic Activation (PDA) method to improve CNNs’ resilience under dendritic degradation. Our method enhances memory retention in the visual cortex, even when a significant proportion of dendrites are randomly deactivated. We trained a CNN with PDA on the Ima- geNet dataset and observed a 7.5% and 1.6% improvement in Top-1 and Top-5 classification accuracies, respectively, under 50% random dendritic degradation. This study highlights the bidirectional contribu- tion between computational techniques and neurobiology, demonstrating the potential of PDA in enhancing neural network robustness and fostering a deeper understanding of biological neural systems’ resilience.