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Explaining Neural Transitions through Resource Constraints

Published online by Cambridge University Press:  24 May 2022

Colin Klein Open the ORCID record for Colin Klein [Opens in a new window]
Colin Klein*
Affiliation:
School of Philosophy, The Australian National University, Canberra, Australia
*
E-mail: colin.klein@anu.edu.au
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Abstract

One 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.

Type
Symposia Paper
Information
Philosophy of Science , Volume 89 , Issue 5 , December 2022 , pp. 1196 - 1202
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Philosophy of Science Association

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