Brain Science and AI

Autumn 2025 | Brain Science and Artificial Intelligence

Course Description

This course explores the integration of brain science and AI through theory and hands-on practice. Students will learn how AI models help understand the brain and how neuroscience inspires AI algorithm development. Using visual information processing as a foundation, the course covers neural coding, information representation, computational neuroscience, and modern AI architectures including transformers and large language models.

Course Information

• Instructor: Dr. Xiaoxuan Jia

Learning Objectives

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

• Master fundamental concepts in computational neuroscience and neural coding
• Apply AI models (CNNs, RNNs, transformers) to analyze brain data
• Build practical skills in neural data analysis and modeling
• Understand how biological intelligence shapes AI development

Repository Structure

brain-science-ai/
├── docs/                          # Course website
│   ├── index.html                 # Main course page
│   ├── schedule.html              # Weekly schedule
│   ├── project.html               # Project details
│   └── images/
├── lectures/                      # Reading materials
│   ├── week01/
│   ├── week02/
│   └── ...
├── tutorials/                     # Programming tutorials
│   ├── week02-python-data/
│   ├── week04-glm/
│   ├── week05-cnn/
│   └── ...
├── resources/                     # Additional resources
│   └── datasets.md
└── README.md

Course Schedule

Week	Date	Topic	Tutorial
1	09/18	Introduction, Course Setup, Brain & AI Overview	Python Bootcamp
2	09/25	NeuroAI	Data Access with Python
3	09/28 (Sun)	Neural Coding 1: Foundation; Information Theory; Efficient Coding	-
4	10/09	Neural Coding 2: Encoding models: GLM	GLM
5	10/16	Neural Coding 3: Latent Variable Models; DNN; Supervised vs self-supervised models	CNN
6	10/23	Transformer; Foundation model; Divisive Normalization; Variability	Transformer
7	10/30	Representation: Dimensionality Reduction	Dimensionality Reduction
8	11/06	Representation: Manifolds; RDM; Representation alignment	Project Team Finalization
9	11/13	Dynamical Systems; Continuous attractors; Hopfield network	LDS
10	11/20	RNN and State Space Models	HMM
11	11/27	Network: low rank RNN and motifs	-
12	12/04	Continual learning	-
13	12/11	Bayesian Inference and its applications in Neuroscience	-
14	12/18	AI agent for science & Project presentation	-
15	12/25	Project Presentations	-

Getting Started

Prerequisites

• Basic knowledge of Python programming
• Familiarity with linear algebra and calculus
• Understanding of basic statistics and probability

Setup Instructions

1. Clone the repository:

   git clone https://github.com/NeuralCoding-Lab/BSAI.git
   cd BSAI

2. Install required packages:

   pip install -r requirements.txt

3. Set up Jupyter environment:

   jupyter notebook

Required Software

check README.md for each tutorial

Tutorials

Weekly programming tutorials are located in the tutorials/ directory:

• Week 1: Python bootcamp
• Week 2: Python for neural data access
• Week 4: Generalized Linear Models (GLM)
• Week 5: Convolutional Neural Networks (CNN)
• Week 6: Transformer
• Week 7: Dimensionality reduction
• Week 9: Linear dynamical system (LDS)
• Week 10: Hidden Markov Model (HMM)

Each tutorial includes:

• Jupyter notebooks with step-by-step instructions
• Sample datasets
• Exercise solutions
• Additional resources

Final Project

Project Types

A. Data Analysis and Modeling: Conduct original modeling work, simulations, or data analysis based on course material. B. Research Summary: Provide an in-depth exposition of a relevant research topic.

Timeline

• Week 8 (11/6): Project proposal and team formation
• Week 11 (11/27): Progress check-in with TA
• Weeks 14-15: Final presentations

See docs/project.html for detailed project requirements and examples.

Datasets

The course uses several key datasets:

• Allen Brain Observatory: Visual cortex recordings during behavioral tasks
• International Brain Laboratory (IBL): Decision-making task data
• Primate datasets: Various repositories (see Week 2 tutorial)
• Public repositories: CRCNS, DANDI, and others

Data access scripts and instructions are provided in the datasets/ directory.

Course Website

Visit the course website for announcements, detailed schedule, and additional resources: https://neuralcoding-lab.github.io/BSAI/

Contributing

For Students

• Report issues or suggest improvements via GitHub Issues

For Instructors and TAs

• Use pull requests for content updates
• Tag releases for each week's materials
• Maintain issue templates for common student questions

Academic Integrity

All work must be original. Collaboration is encouraged for tutorials but discouraged for individual assignments. See course website for detailed policy.

Accessibility

If you need accommodations, please contact the instructor as early as possible.

Support

• Technical Issues: Create a GitHub issue or attend office hours
• Course Content: Attend office hours or discussion sessions
• Project Questions: Contact TAs during progress check-ins

License

This course material is provided for educational purposes as part of the BSAI course by Jiaxx. Course materials are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0.

Acknowledgments

This course builds upon resources and inspiration from:

• python bootcamp from the 2016 Dynamic Brain Summer Course at Friday Harbor
• GLM tutorials from Neuromatch Academy
• COSYNE tutorials
• CORnet and Brain-score
• SSM package by Scott Linderman

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