The University of Southampton
University of Southampton Institutional Repository

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in dvo-devo, evo-eco and evolutionary transitions

Watson, Richard, Mills, Rob, Buckley, C.L., Kouvaris, Konstantinos, Jackson, Adam, Powers, Simon T., Cox, Chris, Tudge, Simon, Davies, Adam, Kounios, Loizos and Power, Daniel (2015) Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in dvo-devo, evo-eco and evolutionary transitions Evolutionary Biology, pp. 1-31. (doi:10.1007/s11692-015-9358-z).

Record type: Article

Abstract

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Text Evolutionary Connectionism PREPRINT.pdf - Other
Download (1MB)
Text art%3A10.1007%2Fs11692-015-9358-z.pdf - Version of Record
Available under License Creative Commons Attribution.
Download (989kB)

More information

Submitted date: 3 April 2015
Published date: 8 December 2015
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 383509
URI: http://eprints.soton.ac.uk/id/eprint/383509
ISBN: 978-0-262-29075-3
ISSN: 0071-3260
PURE UUID: 177280f5-b30f-4e19-bd18-f849a2acc7e1

Catalogue record

Date deposited: 03 Nov 2015 19:01
Last modified: 01 Sep 2017 16:32

Export record

Altmetrics

Contributors

Author: Richard Watson
Author: Rob Mills
Author: C.L. Buckley
Author: Konstantinos Kouvaris
Author: Adam Jackson
Author: Simon T. Powers
Author: Chris Cox
Author: Simon Tudge
Author: Adam Davies
Author: Loizos Kounios
Author: Daniel Power

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×