Before revision: Deep neural networks (DNNs) have revolutionized AI due to their remarkable performance in pattern recognition, comprising of both memorizing complex training sets and demonstrating intelligence by generalizing to previously unseen data (test sets). The high generalization performance in DNNs has been explained by several mathematical tools, including optimization, information theory, and resilience analysis. In humans, it is the ability to abstract concepts from examples that facilitates generalization; this paper thus researches DNN generalization from that perspective. A recent computational neuroscience study revealed a correlation between abstraction and particular neural firing patterns. We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.
After revision: Deep neural networks (DNNs) is understanding their puzzling ability to generalize well. We approach this problem through the unconventional angle of Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.