Explaining Neural Transitions through Resource Constraints
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Published:2022-05-24
Issue:5
Volume:89
Page:1196-1202
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ISSN:0031-8248
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Container-title:Philosophy of Science
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language:en
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Short-container-title:Philos. sci.
Abstract
AbstractOne challenge in explaining neural evolution is the formal equivalence of different computational architectures. If a simple architecture suffices, why should more complex neural architectures evolve? The answer must involve the intense competition for resources under which brains operate. I show how recurrent neural networks can be favored when increased complexity allows for more efficient use of existing resources. Although resource constraints alone can drive a change, recurrence shifts the landscape of what is later evolvable. Hence organisms on either side of a transition boundary may have similar cognitive capacities but very different potential for evolving new capacities.
Publisher
Cambridge University Press (CUP)
Subject
History and Philosophy of Science,Philosophy,History
Cited by
1 articles.
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