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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 |