Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 86 tok/s
Gemini 2.5 Pro 51 tok/s Pro
GPT-5 Medium 43 tok/s
GPT-5 High 37 tok/s Pro
GPT-4o 98 tok/s
GPT OSS 120B 466 tok/s Pro
Kimi K2 225 tok/s Pro
2000 character limit reached

What can ecosystems learn? Expanding evolutionary ecology with learning theory (1506.06374v1)

Published 21 Jun 2015 in q-bio.PE

Abstract: Understanding how the structure of community interactions is modified by coevolution is vital for understanding system responses to change at all scales. However, in absence of a group selection process, collective community behaviours cannot be organised or adapted in a Darwinian sense. An open question thus persists: are there alternative organising principles that enable us to understand how coevolution of component species creates complex collective behaviours exhibited at the community level? We address this issue using principles from connectionist learning, a discipline with well-developed theories of emergent behaviours in simple networks. We identify conditions where selection on ecological interactions is equivalent to 'unsupervised learning' (a simple type of connectionist learning) and observe that this enables communities to self organize without community-level selection. Despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal organisation that habituates to past environmental conditions and actively recalling those conditions.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.