Introduction to Computational Neuroscience

Course Overview

This guest lecture series provides an introduction to computational approaches in neuroscience research. Designed for PhD students and postdocs with basic neuroscience background, the course covers essential mathematical and computational tools for understanding brain function.

Learning Objectives

By the end of this lecture series, students will be able to:

1. Understand neural modeling fundamentals
2. Describe different types of neuron models
3. Explain the relationship between biophysics and computation

4. Choose appropriate modeling approaches for different questions

5. Analyze neural network dynamics

6. Simulate small neural circuits
7. Understand concepts of stability and oscillations

8. Relate network structure to function

9. Apply data analysis techniques

10. Process and analyze neural time series data
11. Implement basic machine learning approaches
12. Interpret results in biological context

Lecture Topics

Lecture 1: Single Neuron Models (2 hours)

The Hodgkin-Huxley Model

• Biophysical foundation: Ion channels and membrane dynamics
• Mathematical formulation: Differential equations and parameters
• Computational implementation: Numerical integration methods
• Biological interpretation: Action potentials and spike patterns

Simplified Models

• Integrate-and-fire models: Computational efficiency vs. biological realism
• FitzHugh-Nagumo model: Oscillations and excitability
• Model selection: Choosing the right level of detail

Course Materials: All lecture slides, code examples, and datasets are available on the course website.

Student Contact: Several students continue to collaborate on research projects, demonstrating the lasting impact of effective teaching.