Neuromorphic computing endeavors to imitate the way biological brains process information and solve problems. Uses for neuromorphic computing span disciplines and include applications in image processing, audio processing, optimization, and more. This work explores the effect of proton-induced single-event upsets (SEUs) on a neuromorphic computing architecture engaged in image recognition. Two main results are found. One, the overall classification accuracy is unchanged although a high number of hidden, tolerable errors occurred. Additionally, SEUs are found to alter the relative occurrence of false positives and false negatives, which occurred despite the overall classification accuracy remaining unaffected.