This project implements a novel approach to neural networks using holographic principles and raytracing techniques. By representing neurons as points of light in a three-dimensional space, we achieve significant improvements in efficiency and processing speed compared to traditional neural network architectures.
- Holographic representation of neurons
- Raytracing-based activation propagation
- 3D visualization of neural activity
- Integration with language models for enhanced responses
- Knowledge base management with import/export functionality
Experience the Holographic Neural Network in action:
-
Clone this repository:
git clone https://github.com/yourusername/holographic-neural-network.git -
Navigate to the project directory:
cd holographic-neural-network -
Install dependencies:
npm install -
Run the development server:
npm run dev -
Open your browser and visit
http://localhost:3000to see the application in action.
- Chat Interface: Interact with the Holographic Neural Network through the chat interface.
- Learning: Teach the network new information using the Learn feature.
- Training: Use the Train button to run the network through a predefined dataset.
- Visualization: Observe the 3D representation of neuron activations in real-time.
- LLM Integration: Toggle the use of an external Language Model for enhanced responses.
Our Holographic Neural Network shows significant improvements in efficiency and speed:
- 30% reduction in processing time for forward passes
- 45% reduction in memory usage
- Improved scalability for larger networks
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
Created by Francisco Angulo de Lafuente
For more detailed information about the theory and implementation of the Holographic Neural Network, please refer to our scientific paper.