Optimizing Potential Information Transfer with Self-referential Memory

  • Mikhail Prokopenko
  • Daniel Polani
  • Peter Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4135)


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.


Information Transfer Cellular Automaton Associative Memory Memory Structure Conditional Mutual Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mikhail Prokopenko
    • 1
  • Daniel Polani
    • 2
  • Peter Wang
    • 1
  1. 1.CSIRO Information and Communication Technology CentreNorth RydeAustralia
  2. 2.Department of Computer ScienceUniversity of HertfordshireHatfieldUnited Kingdom

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