| # Cognitive Network |
|
|
| A PyTorch implementation of a differentiable cognitive network with dynamic structure learning, memory consolidation, and neurotransmitter-modulated plasticity. |
|
|
| ## Features |
|
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| - 🧠 Dynamic network structure that evolves based on performance |
| - 💭 Differentiable memory system with importance-based consolidation |
| - 🔄 Hebbian plasticity with neurotransmitter modulation |
| - 🎯 Self-organizing architecture with adaptive connections |
| - 💡 Emotional context integration for learning modulation |
|
|
| ## Installation |
|
|
| ```bash |
| pip install cognitive-net |
| ``` |
|
|
| Or install from source: |
|
|
| ```bash |
| git clone https://github.com/yourusername/cognitive-net.git |
| cd cognitive-net |
| pip install -e . |
| ``` |
|
|
| ## Quick Start |
|
|
| ```python |
| import torch |
| from cognitive_net import DynamicCognitiveNet |
| |
| # Initialize network |
| net = DynamicCognitiveNet(input_size=10, output_size=2) |
| |
| # Sample data |
| x = torch.randn(10) |
| y = torch.randn(2) |
| |
| # Training step |
| loss = net.train_step(x, y) |
| print(f"Training loss: {loss:.4f}") |
| ``` |
|
|
| ## Components |
|
|
| ### CognitiveMemory |
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| The memory system implements: |
| - Importance-based memory storage |
| - Adaptive consolidation |
| - Attention-based retrieval |
|
|
| ### CognitiveNode |
|
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| Individual nodes feature: |
| - Dynamic weight plasticity |
| - Neurotransmitter modulation |
| - Local memory systems |
|
|
| ### DynamicCognitiveNet |
|
|
| The network provides: |
| - Self-organizing structure |
| - Performance-based connection updates |
| - Emotional context integration |
| - Adaptive learning mechanisms |
|
|
| ## Usage Examples |
|
|
| ### Basic Training Loop |
|
|
| ```python |
| # Initialize network |
| net = DynamicCognitiveNet(input_size=5, output_size=1) |
| |
| # Training data |
| X = torch.randn(100, 5) |
| y = torch.randn(100, 1) |
| |
| # Training loop |
| for epoch in range(10): |
| total_loss = 0 |
| for i in range(len(X)): |
| loss = net.train_step(X[i], y[i]) |
| total_loss += loss |
| print(f"Epoch {epoch+1}, Average Loss: {total_loss/len(X):.4f}") |
| ``` |
|
|
| ### Memory Usage |
|
|
| ```python |
| from cognitive_net import CognitiveMemory |
| |
| # Initialize memory system |
| memory = CognitiveMemory(context_size=64) |
| |
| # Store new memory |
| context = torch.randn(64) |
| memory.add_memory(context, activation=0.8) |
| |
| # Retrieve similar contexts |
| query = torch.randn(64) |
| retrieved = memory.retrieve(query) |
| ``` |
|
|
| ## Contributing |
|
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| Contributions are welcome! Please feel free to submit a Pull Request. |
|
|
| ## License |
|
|
| This project is licensed under the MIT License - see the LICENSE file for details. |
|
|
| ## Citation |
|
|
| If you use this code in your research, please cite: |
|
|
| ```bibtex |
| @software{cognitive_net2024, |
| title = {Cognitive Network: Dynamic Structure Learning with Memory}, |
| author = {Your Name}, |
| year = {2024}, |
| publisher = {GitHub}, |
| url = {https://github.com/yourusername/cognitive-net} |
| } |
| ``` |
|
|
| ## Acknowledgments |
|
|
| - PyTorch team for the excellent deep learning framework |
| - Research community for inspiration and feedback |
