Interpretability of Neural Network With Physiological Mechanisms
This addresses the interpretability issue in AI for researchers and practitioners, but it appears incremental as it focuses on comparative insights rather than a new method.
The paper tackles the problem of neural networks being treated as black-box approximators by comparing them to biological circuits to discover similarities and differences, aiming to incorporate physiological realism for better interpretability.
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.