The evolution of representation in simple cognitive networks

Marstaller, Lars, Hintze, Arend and Adami, Christoph (2013) The evolution of representation in simple cognitive networks. Neural Computation, 25 8: 2079-2107. doi:10.1162/NECO_a_00475

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ329637_OA.pdf Full text (open access) application/pdf 2.22MB 0

Author Marstaller, Lars
Hintze, Arend
Adami, Christoph
Title The evolution of representation in simple cognitive networks
Journal name Neural Computation   Check publisher's open access policy
ISSN 0899-7667
Publication date 2013
Sub-type Article (original research)
DOI 10.1162/NECO_a_00475
Open Access Status File (Publisher version)
Volume 25
Issue 8
Start page 2079
End page 2107
Total pages 29
Place of publication Cambridge, MA, United States
Publisher M I T Press
Language eng
Abstract Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks—an artificial neural network and a network of hidden Markov gates—to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Centre for Advanced Imaging Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Mon, 12 May 2014, 00:56:13 EST by Lars Marstaller on behalf of Centre for Advanced Imaging