The Mathematical Blueprint of Brain Function: Modelling Neural Networks

Neural networks play a pivotal role in understanding brain functions, bridging the gap between biological processes and computational models. This paper delves into mathematical models that decode neural connectivity and explores their significance in unravelling brain functions such as information processing, memory formation, and decision-making. Key modelling approaches include graph theory, differential equations, and stochastic processes, each shedding light on neural activity and connectivity. Applications span neuroscience and artificial intelligence, contributing to innovations in understanding neurological disorders like epilepsy and Alzheimer’s disease, and advancements in machine learning, natural language processing, and robotics. Furthermore, the paper outlines emerging research directions such as integrating multi-scale models, advancing computational techniques, and investigating inter-region brain dynamics to broaden the field’s horizons.