Surprise Minimization Liquid Neural Network

Implementation of a real-time learning AI model combining Liquid Time Constant Neural Networks with surprise minimization-based learning rules.

Project Overview

This project aims to create an AI model that combines Liquid Time Constant (LTC) Neural Networks with a surprise minimization-based learning rule to achieve real-time, unsupervised learning capable of human-like problem-solving routines.

Key Components

1. Liquid Time Constant Neural Networks
2. Surprise Minimization Learning Rules
3. Real-time, Online Learning
4. STDP-like Plasticity Implementation

Getting Started

Documentation and setup instructions will be added as the project develops.

Project Structure

• /src - Source code
• /docs - Documentation
• /tests - Test suite
• /data - Data handling utilities
• /experiments - Experimental notebooks and scripts

License

This project is licensed under the MIT License - see the LICENSE file for details.