The Artificial Intelligence Lab at University of Toyama laboratory is mainly engaged in research on artificial intelligence (AI), especially deep learning and soft computing technologies, and many of its pioneering research results have attracted worldwide attention. The brain's superior information processing capability is realized through various interactions by dendrites (synaptic connections) of neurons (neurons) in the brain. Understanding the principles of brain operation and designing AI models based on the principles of cognitive science will enable the development of more efficient and intelligent AI systems. deep learning, one of the mainstream AI technologies, is a powerful tool for solving complex real-world problems, because of its highly complex network structure and a large amount of training data are required to solve complex real-world problems. It often relies on the learning paradigm of “big model + big data -> big problem.” Therefore, optimizing the energy efficiency of deep learning is one of the bottlenecks that hinder its rapid development. Inspired by the learning mechanisms of highly intelligent organisms in nature, we have proposed “dendritic learning” (Dendritic Learning), which can be modeled based on low power consumption, small models, and little data. Furthermore, we have evaluated the performance metrics of the proposed model, such as computational power, associative memory, learning efficiency, classification ability, and prediction accuracy, and are applying it to many real-world problems, including image recognition, medical imaging (e.g., ultrasound), automatic analysis of building safety, cancer classification, and time series prediction of the stock market