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Computing with probabilistic cellular automata


Schüle, M; Ott, T; Stoop, R (2009). Computing with probabilistic cellular automata. In: Alippi, C. Artificial Neural Networks – ICANN 2009. 19th international conference, Limassol, Cyprus, September 14 - 17, 2009; proceedings, part II. Berlin: Springer, 525-533.

Abstract

We investigate the computational capabilities of probabilistic cellular automata by means of the density classification problem. We find that a specific probabilistic cellular automata rule is able to solve the density classification problem, i.e. classifies binary input strings according to the number of 1’s and 0’s in the string, and show that its computational abilities are related to critical behaviour at a phase transition.

Abstract

We investigate the computational capabilities of probabilistic cellular automata by means of the density classification problem. We find that a specific probabilistic cellular automata rule is able to solve the density classification problem, i.e. classifies binary input strings according to the number of 1’s and 0’s in the string, and show that its computational abilities are related to critical behaviour at a phase transition.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:17 September 2009
Deposited On:19 Mar 2010 11:42
Last Modified:05 Apr 2016 14:00
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:5769
ISSN:0302-9743 (P) 1611-3349 (E)
ISBN:978-3-642-04276-8 (P) 978-3-642-04277-5 (E)
Additional Information:International Conference on Artificial Neural Networks ; 19 (Limassol) : 2009.09.14-17
Publisher DOI:https://doi.org/10.1007/978-3-642-04277-5_53
Related URLs:http://www.ini.uzh.ch/node/22237

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