Computational Methods in Cognitive Neuroscience
( 24 Modules )

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

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Computational Methods in Cognitive Neuroscience ( 24 Modules )

Computational Methods in Cognitive Neuroscience
( 24 Modules )