Scenario 1: CANN Modeling and Simulation

Comprehensive tutorials on building, simulating, and analyzing Continuous Attractor Neural Networks.

Foundation Tutorials

Basic CANN Modeling

• Tutorial 1: Building and Using CANN Models
• Tutorial 2: Task Generation and CANN Simulation
• Tutorial 3: Analysis and Visualization Methods
• Tutorial 4: CANN Parameter Effects
• Tutorial 5: Grid Cell Velocity-Based Path Integration and Spatial Representation Analysis

Advanced Tutorials

Advanced Applications

• Tutorial 6: Hierarchical Path Integration Network
• Tutorial 7: Theta Sweep in Head Direction and Grid Cell System
• Tutorial 8: Theta Sweep Place Cell Network

Learning Path

For beginners:

1. Start with Tutorial 1-4 (Foundation)

2. Complete each tutorial in order

3. Run all code cells to understand the concepts

4. Experiment with parameters

For advanced users:

• Tutorial 5: Multi-scale spatial representation

• Tutorial 6: Theta-modulated dynamics

• Tutorial 7: Complex environments with place cells

Prerequisites

• Basic understanding of neural networks

• Python programming experience

• Familiarity with NumPy and matplotlib

Estimated Time

• Foundation tutorials (1-4): 2-3 hours

• Advanced tutorials (5-7): 2.5 hours

• Total: 5 hours