Abstract
The intersection of computational neuroscience and environmental modeling has birthed novel algorithms that emulate brain-inspired emotional learning. One such contribution is the BELBFM (Brain Emotional Learning Based on Basic and Functional Memories) model, recently proposed for dual-height wind speed forecasting. While designed for meteorological applications, the structure and function of BELBFM echo principles long studied in neurobiology. In this commentary, we explore the neurological underpinnings of BELBFM, its potential feedback to cognitive modeling, and the broader implications of emotionally inspired computation in both natural and artificial systems.