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Explaining Neural Transitions through Resource Constraints.

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 ho...

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Published in:Philosophy of Science Vol. 89; no. 5; pp. 1196 - 1203
Main Author: Klein, Colin
Format: Article
Published: Cambridge University Press Dec2022
Online Access:View this record in EBSCOhost
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      dt: Dec2022
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        au: Klein, Colin
        affil: School of Philosophy, The Australian National University, Canberra, Australia
      su: Recurrent neural networks
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        subj: Recurrent neural networks
      ab: 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.
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    language: English
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