Module #1 Introduction to Computational Methods in Cognitive Neuroscience Overview of the field, importance of computational methods, and course objectives
Module #2 Neural Networks and Neural Computation Basic concepts of neural networks, neural coding, and neural computation
Module #3 Mathematical Background:Linear Algebra and Calculus Review of essential mathematical concepts for computational modeling in cognitive neuroscience
Module #4 Computational Models of Decision-Making Introduction to decision-making models, including Bayesian inference and reinforcement learning
Module #5 Neural Representation of Sensory Information Computational models of sensory processing, including vision and audition
Module #6 Machine Learning for Cognitive Neuroscience Introduction to machine learning, including supervised and unsupervised learning, and their applications
Module #7 Neural Networks for Natural Language Processing Application of neural networks to language processing and comprehension
Module #8 Computational Models of Attention Theoretical and computational models of attention, including neural mechanisms and behavioral outcomes
Module #9 Memory and Learning:Computational Models Computational models of memory formation, consolidation, and retrieval, including neural mechanisms
Module #10 Neural Oscillations and Rhythms Computational models of neural oscillations, including their roles in cognitive processes
Module #11 Functional Neuroimaging:EEG and fMRI Introduction to functional neuroimaging methods, including EEG and fMRI, and their applications
Module #12 Computational Analysis of Neuroimaging Data Introduction to computational tools and methods for analyzing neuroimaging data
Module #13 Computational Models of Cognitive Development Theoretical and computational models of cognitive development, including neural mechanisms and behavioral outcomes
Module #14 Computational Models of Neuropsychiatric Disorders Computational models of neuropsychiatric disorders, including schizophrenia, depression, and anxiety
Module #15 Brain-Computer Interfaces:Principles and Applications Introduction to brain-computer interfaces, including their principles, applications, and limitations
Module #16 Advanced Topics in Computational Neuroscience In-depth exploration of advanced topics, including deep learning, graph neural networks, and cognitive architectures
Module #17 Case Studies in Computational Cognitive Neuroscience Real-world case studies of computational models applied to cognitive neuroscience research questions
Module #18 Computational Tools for Cognitive Neuroscience Introduction to popular computational tools, including Python, MATLAB, and R, and their applications
Module #19 Statistical Analysis for Cognitive Neuroscience Introduction to statistical analysis methods, including hypothesis testing and model selection
Module #20 Neural Decoding and Encoding Computational methods for neural decoding and encoding, including neural representing and neural reconstruction
Module #21 Computational Models of Social Cognition Theoretical and computational models of social cognition, including social decision-making and social learning
Module #22 Computational Models of Emotion and Motivation Theoretical and computational models of emotion and motivation, including neural mechanisms and behavioral outcomes
Module #23 Current Research and Future Directions Overview of current research trends and future directions in computational cognitive neuroscience
Module #24 Course Wrap-Up & Conclusion Planning next steps in Computational Methods in Cognitive Neuroscience career
