Electrical signals in neurons and artificial neural networks

This report analyzes the process by which the core technology underlying the mechanism of electrical signal transmission in human nerve cells (neurons) was developed and advanced into artificial neural networks. First, this report explains the structure of neurons, the definition of membrane potential, and the process and principles behind the generation of action potentials, and defines how these signals enable information to travel from one neuron to another. In particular, it clarifies that not all stimuli to a neuron result in a signal; rather, the concept of a stimulus exceeding a certain threshold is necessary, and this is summarized through the “all-or-nothing” principle. To analyze how electrical signals—which are merely simple neurological signals—evolved into artificial neural networks, this study defined the concept of weights through perceptron theory, as well as multi-perceptrons and the backpropagation algorithm. It then described the progression to deep learning in artificial intelligence, supported by scientific facts, and demonstrated how stimuli exceeding a neuron’s threshold are applied to modern AI technology through the use of activation functions such as the sigmoid, ReLU, and Softmax to demonstrate how stimuli exceeding a neuron’s threshold are applied in modern AI technology. Finally, the paper examined the areas of application for artificial neural networks and summarized, based on the development of core AI principles, how they are integrated into fields such as healthcare, autonomous vehicles, image recognition, weather forecasting services, and AI chatbots. Through the advancements in deep learning and AI—enhanced by development based on the emulation of human neurons—the importance and value of the convergent development of life sciences and engineering were confirmed.