Advanced Computational Models of Neural Systems
( 26 Modules )

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

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Advanced Computational Models of Neural Systems ( 26 Modules )

Advanced Computational Models of Neural Systems
( 26 Modules )