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A modular, extensible neural network library for C#/.NET projects. Designed for dynamic and evolutionary expansion, allowing the creation and growth of neural networks at runtime.

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Zediwan/Synapse.NET

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🧪 Synapse.NET

A modular, extensible neural network library for C#/.NET projects. Designed for dynamic and evolutionary expansion, allowing the creation and growth of neural networks at runtime—including adding new layers and nodes, structural mutation, and advanced visualization.


🚀 Overview

Synapse.NET empowers C# and .NET developers to:

  • Build neural networks dynamically and programmatically
  • Evolve networks using genetic algorithms (mutation, crossover)
  • Compare and visualize network structures
  • Save/load networks to/from files
  • Extend with custom layers, activation functions, and more

Use it as a reusable package in your own projects, freeing you from reimplementing neural network code every time.


🔧 Features

  • Dynamic addition and removal of layers, nodes, and connections at runtime
  • Evolutionary expansion: mutate structure by adding/removing elements
  • Ability to cross (combine) neural networks to produce child networks
  • Structural similarity comparison algorithms
  • Visualizer for inspecting architectures, connection weights, and properties
  • Saving and loading networks (serialization/deserialization)
  • Modular, extensible API for custom extensions
  • Ready for integration with any C#/.NET project

📦 Technologies

  • C#, .NET 6+
  • (Optional) Math.NET Numerics for advanced math
  • (Optional) WPF for visualization
  • Distributed as a NuGet package

📄 Roadmap

See ROADMAP.md for planned features and milestones.


📘 Documentation

  • API, usage, and conceptual docs: docs/

🏁 Getting Started

to be added


🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md (to be added) for guidelines.


📜 License

MIT License – see LICENSE for details.


🙏 Inspirations

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A modular, extensible neural network library for C#/.NET projects. Designed for dynamic and evolutionary expansion, allowing the creation and growth of neural networks at runtime.

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