Introductory Concepts of Neuroscience and Computational Intelligence

This chapter explores the interface between neuroscience and computational intelligence in the investigation of brain function and the development of advanced neurotechnologies. It proposes a comprehensive conceptual framework that integrates biological principles with methodologies derived from artificial intelligence, structured around three core aspects: (1) the neurofunctional bases of brain organization, with an emphasis on distributed processing and network dynamics; (2) electroencephalography (EEG) as a fundamental and accessible method for capturing neural activity with high temporal resolution; and (3) computational intelligence techniques, particularly artificial neural networks and deep learning architectures, employed in the analysis of complex neurophysiological data. The chapter underscores how computational models both influence and are shaped by neuroscientific findings, fostering a reciprocal and cumulative process of knowledge production. Moreover, it presents a detailed examination of the technical characteristics and applications of EEG, followed by a critical analysis of machine learning approaches used for pattern recognition in neural data. The discussion demonstrates how this interdisciplinary convergence eases the emergence of innovative methodologies in brain research, while simultaneously introducing challenges related to model interpretability and alignment with biological principles. Emerging trends and prospective directions are also addressed, including multimodal data integration, the role of explainable artificial intelligence in neuroscience, and the pursuit of more biologically plausible neural models. Our goal is to equip readers with both foundational knowledge and contemporary insights into this dynamic and evolving intersection between brain science and artificial intelligence, 2 in which the mutual advancement of both domains continues to redefine the boundaries of inquiry.