Distributed information encoding and decoding using self-organized spatial patterns.

Area of Science:

• Complex Systems
• Machine Learning
• Information Theory

Background:

• Dynamical systems generate distinct outputs from different initial conditions, a principle fundamental to information encoding and decoding.
• Natural pattern-formation systems exhibit inherent randomness, leading to similar yet not identical outputs across repeated realizations.

Purpose of the Study:

• To demonstrate a novel approach for distributed information encoding and decoding using self-organized patterns and machine learning.
• To explore the trade-off between encoding capacity and security by modulating pattern generation and machine learning models.

Main Methods:

• Utilizing self-organized patterns that produce high-dimensional outputs.
• Integrating machine learning algorithms for pattern recognition and decoding.
• Exploiting the distinguishability of pattern groups arising from different initial configurations under controlled randomness.

Main Results:

• Successfully demonstrated distributed information encoding and decoding.
• Showcased the scalability of the method by encoding and decoding all standard English keyboard characters.
• Established that modulating pattern generation and machine learning training tunes the encoding capacity-security trade-off.

Conclusions:

• Self-organized patterns combined with machine learning offer a viable strategy for distributed information processing.
• The inherent randomness in pattern formation can be harnessed for robust information encoding and decoding.
• The proposed method is scalable and adaptable for various information processing tasks.