2026春 课程号：ISTE7002P01
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| 选课类别：基础 | 教学类型：理论课 |
| 课程类别：研究生课程 | 开课单位：人工智能与数据科学学院 |
| 课程层次：未知 | 学分：2.0 |
The emergence of intelligence and behavior from the complex interactions within the brain remains one of the most significant unsolved mysteries in modern science. In the last decade, rapid advancements in experimental tools have enabled us to monitor and manipulate brain circuits with unprecedented precision. Yet neuroscientists are still navigating the intricate landscapes of brain structures and dynamics. Mathematical theory has become essential for integrating seemingly unrelated evidence, generating new insights, guiding experiments, and identifying organizing principles of brain function.
This course explores how physics, engineering, and mathematics have shaped our understanding of the brain — in particular, the relationship between structure, dynamics, representation, and behavior. A central theme is comparing biological learning rules and architectures with modern machine learning methods. Special topics may include wiring optimization in neural circuits, attractor and chaotic dynamics in neural networks, sensory and motor representations, biological learning rules, Hopfield networks, and hierarchical control of behavior.
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