README.md

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

pip install cognitive-net

Or install from source:

git clone https://github.com/yourusername/cognitive-net.git
cd cognitive-net
pip install -e .

Quick Start

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

# 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

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:

@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