Machine Learning and Neural Network Models for Cognitive Disorders: A Literature Review

Cognitive disorders pose ongoing challenges for accurate diagnosis and treatment due to the brain’s complex, nonlinear mechanisms. This review examines the expanding role of machine learning and neural network models in understanding and managing cognitive dysfunctions. It synthesizes recent advances in the use of feedforward, recurrent, and deep neural architectures for modeling cognitive processes and detecting disorder-specific neural patterns. The paper emphasizes biologically inspired principles, including Hebbian learning and hierarchical organization, as conceptual links between artificial and biological intelligence. It also discusses key challenges such as data scarcity, limited interpretability, and the need for clinically validated models. The findings highlight that biologically grounded and interpretable machine learning frameworks can advance both theoretical neuroscience and clinical applications. The integration of computational modeling with experimental and clinical data is likely to drive the next phase of research into the mechanisms and management of cognitive disorders.