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...
| Publicado en: | Philosophy of Science Vol. 89; no. 5; pp. 1196 - 1203 |
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| Autor principal: | |
| Formato: | Artículo |
| Publicado: |
Cambridge University Press
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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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