  Deep learning continues to play as a powerful state-of-art technique that has
achieved extraordinary accuracy levels in various domains of regression and
classification tasks, including images, video, signal, and natural language
data. The original goal of proposing the neural network model is to improve the
understanding of complex human brains using a mathematical expression approach.
However, recent deep learning techniques continue to lose the interpretations
of its functional process by being treated mostly as a black-box approximator.
To address this issue, such an AI model needs to be biological and
physiological realistic to incorporate a better understanding of human-machine
evolutionary intelligence. In this study, we compare neural networks and
biological circuits to discover the similarities and differences from various
perspective views. We further discuss the insights into how neural networks
learn from data by investigating human biological behaviors and understandable
justifications.
