Module #1 Introduction to Neural Systems Modeling Overview of the course, importance of computational models in neuroscience, and introduction to key concepts
Module #2 Mathematical Preliminaries Review of mathematical concepts essential for neural systems modeling, including differential equations and linear algebra
Module #3 Integrate-and-Fire Models Introduction to simple neural models, including integrate-and-fire neurons and their limitations
Module #4 Hodgkin-Huxley Model In-depth exploration of the Hodgkin-Huxley model, including its mathematical formulation and applications
Module #5 Spike Response Model Introduction to the spike response model, its advantages, and limitations
Module #6 Point Neuron Models Exploration of point neuron models, including their mathematical formulation and applications
Module #7 Compartmental Models Introduction to compartmental models, including their mathematical formulation and applications
Module #8 Neural Networks and Synaptic Plasticity Introduction to neural networks, synaptic plasticity, and their computational models
Module #9 Rate-Based Models Exploration of rate-based models, including their mathematical formulation and applications
Module #10 Mean-Field Models Introduction to mean-field models, including their mathematical formulation and applications
Module #11 Neural Oscillations and Rhythms Exploration of neural oscillations and rhythms, including their computational models and applications
Module #12 Computational Models of Cognitive Processes Introduction to computational models of cognitive processes, including attention, memory, and decision-making
Module #13 Bayesian Inference in Neural Systems Exploration of Bayesian inference in neural systems, including its applications and limitations
Module #14 Machine Learning and Deep Learning in Neuroscience Introduction to machine learning and deep learning techniques in neuroscience, including their applications and limitations
Module #15 Neural Decoding and Encoding Exploration of neural decoding and encoding, including their computational models and applications
Module #16 Brain-Computer Interfaces Introduction to brain-computer interfaces, including their computational models and applications
Module #17 Neural Systems and Neurological Disorders Exploration of computational models of neurological disorders, including epilepsy, Parkinsons disease, and Alzheimers disease
Module #18 Model Validation and Model Selection Introduction to model validation and model selection techniques in neural systems modeling
Module #19 Computational Neuroanatomy Exploration of computational models of neural anatomy, including neural circuits and brain connectivity
Module #20 Neural Systems Modeling Tools and Software Overview of popular tools and software for neural systems modeling, including NEURON, NEST, and Brian
Module #21 Advanced Topics in Neural Systems Modeling In-depth exploration of advanced topics in neural systems modeling, including stochastic models and nonlinear dynamics
Module #22 Applications of Neural Systems Modeling Exploration of applications of neural systems modeling, including neuroprosthetics, brain-machine interfaces, and neurostimulation
Module #23 Current Challenges and Future Directions Discussion of current challenges and future directions in neural systems modeling
Module #24 Project Development and Presentation Guided project development and presentation, applying computational models to a specific research question
Module #25 Review and Practice Review of key concepts and practice problems to reinforce understanding
Module #26 Course Wrap-Up & Conclusion Planning next steps in Advanced Computational Models of Neural Systems career
