Full Detail Tutorials

Comprehensive tutorials covering all aspects of the CANNs library, organized by research scenarios.

Overview

These tutorials provide in-depth, hands-on guidance for using the CANNs library across different research scenarios. Each tutorial is a complete Jupyter notebook that you can run and modify.

Scenarios

Tutorial Scenarios:

Scenario Descriptions

Scenario 1: CANN Modeling and Simulation (7 tutorials)

Build, simulate, and analyze continuous attractor neural networks from scratch. Learn network dynamics, parameter effects, and advanced architectures.

  • Foundation: Basic models, tasks, visualization, parameters

  • Advanced: Hierarchical networks, theta sweeps, complex environments

Scenario 2: Data Analysis and Neural Decoding (Coming Soon)

Analyze experimental neural recordings, decode spatial representations, and validate model predictions against real data.

Scenario 3: Brain-Inspired Learning (1 tutorial)

Implement biologically-inspired learning rules including Hebbian plasticity and associative memory mechanisms.

  • Pattern storage and recall with Hopfield networks

Scenario 4: End-to-End Research Workflows (1 tutorial)

Use high-level pipelines for complete analyses without detailed implementation knowledge. Perfect for experimental neuroscientists.

  • Theta sweep pipeline for trajectory analysis

Learning Paths

For Computational Neuroscientists:

  1. Start with Scenario 1 (CANN Modeling)—Learn the foundations

  2. Explore Scenario 3 (Brain-Inspired Learning)—Understand learning mechanisms

  3. Use Scenario 4 (Pipelines) for rapid analysis

For Experimental Neuroscientists:

  1. Begin with Scenario 4 (Pipelines)—Quick analysis of your data

  2. Optionally explore Scenario 1—Understand what’s happening under the hood

  3. Explore Scenario 3 for learning-based models

For Method Developers:

  1. Master Scenario 1 (CANN Modeling)—Deep understanding of models

  2. Study Scenario 3 (Brain-Inspired Learning)—Extend learning rules

  3. Use Scenario 4 code as reference for creating new pipelines

Prerequisites

  • Programming: Basic Python knowledge (NumPy, matplotlib)

  • Math: Linear algebra, differential equations (helpful but not required)

  • Neuroscience: Basic understanding of neural coding (recommended)

Each scenario has specific prerequisites listed in its index page.

Time Commitment

  • Scenario 1: 5 hours (7 tutorials)

  • Scenario 2: Coming soon

  • Scenario 3: 35 minutes (1 tutorial)

  • Scenario 4: 60 minutes (1 tutorial)

Total estimated time: 6.5 hours for all available tutorials

Getting Started

New to CANNs?

  1. Complete the Quick Starts first

  2. Read Core Concepts for background

  3. Then dive into these detailed tutorials

Have experience?

Jump directly to the scenario that matches your needs.

Running the Tutorials

All tutorials are provided as Jupyter notebooks (.ipynb files).

To run locally:

# Clone the repository
git clone https://github.com/routhleck/canns.git
cd canns

# Install dependencies
pip install -e .[dev]

# Launch Jupyter
jupyter notebook docs/en/3_full_detail_tutorials/

Online:

  • Open notebooks directly on GitHub

  • Use Google Colab (upload the notebook)

  • Use Binder (link coming soon)

Support and Feedback

  • Documentation: Full API reference available

  • Examples: Additional examples in the examples/ directory

  • Issues: Report problems on GitHub Issues

  • Discussions: Ask questions in GitHub Discussions

Contributing

We welcome contributions! If you:

  • Find errors or improvements for existing tutorials

  • Want to add new tutorials

  • Have suggestions for better explanations

Please submit a pull request or open an issue.

Next Steps

Choose a scenario above and start learning! Each scenario page provides detailed information about its content and learning objectives.