This repository serves as an ongoing effort to create a systems and computational neuroscience course. I hope to develop a two-semester course for USTC biophysics students. The first semester will cover some basic materials, and the second semester will introduce more advanced topics for undergraduate and graduate students.
When: Thursday 9:45 am - 11:20 am, Fall 2024
Where: 东区第二教学楼2402
Teacher: 温泉 qwen@ustc.edu.cn
Teaching Fellow: 冼奇琪 xianqiqi@mail.ustc.edu.cn 程玉锟 cyk_phy@mail.ustc.edu.cn 李懿轩 yixuanli@mail.ustc.edu.cn
Recommended Textbooks:
- Theoretical Neuroscience: Computational and Mathematical Modeling the Neural System
- Principles of Neural Design
For a general reader:
Course Performance Evaluation:
- Homework: 70%
- Final: 30% (take-home exam)
Prerequisite:
- High school knowledge of biology and neuroscience
- Proficient in Python/MATLAB/Julia
- Working knowledge of Multivariate Calculus, Probability Theory, Linear Algebra, and Differential Equations
The emergence of intelligence and behavior from the complex interactions within the brain remains one of the most significant and unsolved mysteries in modern science. This is an exciting era. In the last decade, we have witnessed rapid advancements in experimental tools that now enable us to monitor and manipulate brain circuits with unprecedented precision. However, it is also a perplexing time. Neuroscientists are navigating the intricate landscapes of brain structures and dynamics. Mathematical theory has become crucial for integrating seemingly unrelated evidence, providing new insights, guiding new experiments, and identifying concepts and principles of brain function.
In this course, we will explore how physics, engineering, and mathematics have shaped our understanding of the brain. In particular, we will investigate the relationship between structure, dynamics, representation, and behavior. Special topics may include wiring optimization in neural circuit, attractor and chaotic dynamics in neural network, sensory and motor representation, biological learning rules, Hopfield network, and hierarchical control of behaviors. We will also discuss connections with modern machine learning methods. I will strive to balance breadth and depth, highlighting the challenges we face in developing quantitative theories or models of the mind.