| # Cognitive Network | |
| A PyTorch implementation of a differentiable cognitive network with dynamic structure learning, memory consolidation, and neurotransmitter-modulated plasticity. | |
| ## Features | |
| - 🧠 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 | |
| The memory system implements: | |
| - Importance-based memory storage | |
| - Adaptive consolidation | |
| - Attention-based retrieval | |
| ### CognitiveNode | |
| 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 | |
| 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 |