In the brain, some neurons adapt easily, while others remain resistant to change. This can be likened to our beliefs, where certain deeply ingrained convictions, like the historical belief that the sun revolves around the earth, required substantial evidence and effort to alter. Similarly, in machine learning, we could enhance efficiency by incorporating a "resistance to change" factor for each neuron during training. This factor would determine how readily a neuron or set of neurons can adapt or how firmly they maintain their learned patterns (or weights). By drawing on the concept that some brain regions or neuron patterns encode hard-set beliefs through repeated reinforcement, we can apply this principle to improve the adaptability and stability of artificial neural networks. This could be a pathway for the Alignment Problem. PS: Dropout is a somewhat overlapping, but a completely different concept, introduced for a different need.