I teach brain science and artificial intelligence at Tsinghua University and in summer schools.
Brain Science and Artificial Intelligence (BSAI)
Course materials: BSAI
This course examines how neural systems represent, transform, and compute information, and how these principles relate to modern artificial intelligence.
The course is centered on core concepts in computational and systems neuroscience, with an emphasis on neural coding, population representations, and dynamical systems approaches to neural computation.
Topics include:
Neural coding and representation
Generalized linear models (GLMs), information theory, population coding, and high-dimensional representations.Dynamical systems and state-space models
Latent dynamics, linear dynamical systems (LDS), and hidden Markov models (HMMs).Computation in neural networks
Excitatory–inhibitory (E–I) balanced networks, low-rank recurrent neural networks, and network motifs.Selectivity, invariance, and hierarchy
Principles underlying generalization and robust sensory representations.NeuroAI
Connections between biological and artificial neural networks, including deep learning models as tools for understanding neural computation.Data and methods
Large-scale neural recordings, encoding and decoding models, and machine learning approaches.
The course integrates theoretical, computational, and data-driven perspectives, with an emphasis on hands-on coding.