Realization of Associative Memory in an Enzymatic Process: Towards Biomolecular Networks with Learning and Unlearning Functionalities

Abstract

We report a realization of an associative memory signal/information processing system based on simple enzyme-catalyzed biochemical reactions. Optically detected chemical output is always obtained in response to the triggering input, but the system can also "learn" by association, to later respond to the second input if it is initially applied in combination with the triggering input as the "training" step. This second chemical input is not self-reinforcing in the present system, which therefore can later "unlearn" to react to the second input if it is applied several times on its own. Such processing steps realized with (bio)chemical kinetics promise applications of bio-inspired/memory-involving components in "networked" (concatenated) biomolecular processes for multi-signal sensing and complex information processing.

References in corpus (7)

• Circuit elements with memory: memristors, memcapacitors and meminductors
• Memory effects in complex materials and nanoscale systems
• Enzyme-Based Logic Systems for Information Processing
• Memristive model of amoeba's learning
• Spin memristive systems
• Network Analysis of Biochemical Logic for Noise Reduction and Stability: A System of Three Coupled Enzymatic AND Gates
• Control of Noise in Chemical and Biochemical Information Processing

Cited by in corpus (4)

• Networked Enzymatic Logic Gates with Filtering: New Theoretical Modeling Expressions and Their Experimental Application
• Bioinspired Associative Memory System Based on Enzymatic Cascades
• Can bio-inspired information processing steps be realized as synthetic biochemical processes?
• Theoretical Modeling Expressions for Networked Enzymatic Signal Processing Steps as Logic Gates Optimized by Filtering