Literature DB >> 26286763

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

Vera Bocharova1, Kevin MacVittie, Soujanya Chinnapareddy, Jan Halámek, Vladimir Privman, Evgeny Katz.   

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 bioinspired/memory-involving components in "networked" (concatenated) biomolecular processes for multisignal sensing and complex information processing.

Entities:  

Keywords:  associative memory; biocomputing; biomolecular information processing; biomolecular network; enzyme; unconventional computing

Year:  2012        PMID: 26286763     DOI: 10.1021/jz300098b

Source DB:  PubMed          Journal:  J Phys Chem Lett        ISSN: 1948-7185            Impact factor:   6.475


  1 in total

1.  Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications.

Authors:  Arjun Verma; Brian E Fratto; Vladimir Privman; Evgeny Katz
Journal:  Sensors (Basel)       Date:  2016-07-05       Impact factor: 3.576

  1 in total

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