Optimizing potential information transfer with self-referential memory

Mikhail Prokopenko*, Daniel Polani, Peter Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

This paper investigates an information-theoretic design principle, intended to support an evolution of a memory structure fitting a specific selection pressure: potential information transfer through the structure. The proposed criteria measure how much does associativity in memory add to the information transfer in terms of precision, recall and effectiveness. Maximization of the latter results in holographic memory structures that can be interpreted in self-referential terms. The study introduces an analogy between self-replication and memory retrieval, with DNA as a partially-associative memory containing relevant information. DNA decoding by a complicated protein machinery ("cues" or "keys") may corresponds to an associative recall: i.e., a replicated offspring is an associatively-recalled prototype. The proposed information-theoretic criteria intend to formalize the notion of information transfer involved in self-replication, and enable bio-inspired design of more effective memory structures.

Original languageEnglish
Title of host publicationUnconventional Computation - 5th International Conference, UC 2006, Proceedings
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Pages228-242
Number of pages15
Volume4135 LNCS
ISBN (Print)3540385932, 9783540385936
Publication statusPublished - 2006
Externally publishedYes
Event5th International Conference on Unconventional Computation, UC 2006 - York, United Kingdom
Duration: 4 Sep 20068 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4135 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International Conference on Unconventional Computation, UC 2006
CountryUnited Kingdom
CityYork
Period4/09/068/09/06

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