Output details
11 - Computer Science and Informatics
University of Sussex
Article title
Evolution of associative learning in chemical networks
Type
D - Journal article
Title of journal
PLoS Computational Biology
Article number
-
Volume number
8
Issue number
11
First page of article
e1002739
ISSN of journal
1553-734X
Year of publication
2012
Number of additional authors
3
Additional information
<28>PLoS Computational Biology is the leading specialist international outlet for research in computational biology. It has a high IF (5.3) and a very high rejection rate. This paper is part of a programme of work in adaptive properties of chemical systems which has resulted in several significant publications (e.g. Dale & Husbands, Artif. Life 16(1):1-19, 2010 ). A major new biological implication of this work is that there appears to be no lack of variation that would prevent associative learning from evolving and being implemented within cell signalling networks.
Interdisciplinary
-
Cross-referral requested
-
Research group
F - Evolutionary and Adaptive Systems
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
