👉 Hormone computing is an innovative computational paradigm that mimics biological processes, particularly those involving hormones, to solve complex problems in artificial intelligence and machine learning. It leverages the principles of neurobiology, such as synaptic plasticity and neural network dynamics, to create adaptive systems capable of learning from dynamic environments. In this framework, hormones act as signals that modulate the strength and efficacy of neural connections, analogous to how neurotransmitters function in biological systems. By simulating these hormonal interactions, hormone computing enables the development of more efficient and robust AI models that can adapt to changing conditions, optimize decision-making processes, and improve learning capabilities in real-time scenarios. This approach holds significant potential for applications in areas like personalized medicine, adaptive robotics, and intelligent control systems.
