Update README.md

README.md

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----
-license: mit
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+# 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