Biomolecular computing: from the brain-machine disanalogy to the brain-machine analogy

Area of Science:

• Neuroscience and Computational Science
• Information Theory
• Non-linear Dynamics

Background:

• The brain's information processing relies on complex, non-discrete mechanisms.
• Existing computational devices often struggle with the inherent fuzziness of 'soft' humanistic systems.
• Non-linear dynamic mechanisms offer a potential framework for understanding biological computation.

Purpose of the Study:

• To draw an analogy between the brain's information processing features and molecular non-discrete information processing devices.
• To explore the computational capabilities of devices based on non-linear dynamic mechanisms.
• To investigate how these mechanisms can naturally embody fuzzy information processing.

Main Methods:

• Conceptual analysis of information processing in biological and artificial systems.
• Examination of non-linear dynamic principles in computation.
• Comparative study of primitive operations in brain and molecular devices.

Main Results:

• Non-linear dynamic mechanisms in molecular devices mirror key information features of the brain.
• These mechanisms enable the devices to perform primitive operations suited for high computational complexity.
• The non-linear dynamics naturally accommodate fuzzy information, characteristic of humanistic systems.

Conclusions:

• Molecular devices utilizing non-linear dynamics present a promising paradigm for brain-inspired computing.
• Such devices can potentially solve complex computational problems and process fuzzy information effectively.
• This approach bridges the gap between biological information processing and artificial computational systems.