Computational Methods for Mind, Brain and Behavior

Catalog description

Introduces computational methods for studying mind, brain, and behavior, drawing on the emerging field of NeuroAI. Students develop a mathematical and computational toolkit that combines classical approaches with hands-on experience in modern methods such as deep learning. Core topics include representational spaces (multidimensional scaling, PCA, nonlinear dimensionality reduction), learning rules and statistical models, and deep neural networks — from multilayer perceptrons and convolutional networks to recurrent architectures, transformers, and generative models. Applications span visual perception, learning and memory, cognition, language, and behavior, with an emphasis on modeling experimental data from neuroscience and cognitive psychology and connecting these models to contemporary developments in AI. By the end of the course, students will have both a conceptual framework and practical skills for exploring how AI models can explain, and be inspired by, natural intelligence.