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Cited by 14Cybernetics and Technology in SocietyNeural Networks and ApplicationsCognitive Computing and Networks

Evolution of Brains and Computers: The Roads Not Taken

Open access
Solé, Ricard, Seoane, Luís F.·2022·Entropy
DOI 10.3390/e24050665PMC PMC9141356
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Abstract

When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unexpected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate from the lack of major innovations that have paved the way to biological computing (including brains) that are completely absent within the artificial domain. As it occurs within synthetic biocomputation, we can also ask whether alternative minds can emerge from A.I. designs. Here, we take an evolutionary view of the problem and discuss the remarkable convergences between living and artificial designs and what are the pre-conditions to achieve artificial intelligence.

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  • One implication of shared neural architectures is the potential for convergent minds shaped by evolution.

    “One implication of these shared architectures is the potential for convergent minds shaped by evolution [281–284].”

    From Fundamental constraints to the logic of living systems (2024) · cites this paper as [284]

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Authors
Solé, R., & Seoane, L. F.
Affiliations
  • Institució Catalana de Recerca i Estudis Avançats (ES)
  • Consejo Superior de Investigaciones Científicas (ES)
Corresponding author
Solé, Ricard
ORCID
Venue
Entropy
Publisher
MDPI AG
Published
2022-05-09
Volume
24
Issue
5
Pages
665
ISSN
1099-4300 (ISSN-L)
DOI
10.3390/e24050665
PMC
PMC9141356
OpenAlex
W4229458916
Cited by
14
References
188
Citations by year
2026: 3 · 2025: 4 · 2024: 2 · 2023: 4 · 2022: 1
FWCI
11.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Entropy (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
Funders
  • Spanish Ministry of Economy and Competitiveness (FIS2016-77447-R MINECO/AEI/FEDER, AGAUR FI-SDUR 2020, IJC2018-036694-I)
  • Spanish National Research Council (CSIC) (FIS2016-77447-R MINECO/AEI/FEDER, AGAUR FI-SDUR 2020, IJC2018-036694-I)
Topics
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
Keywords
convergent evolution, artificial neural networks, deep learning, connectomics, biological computation, artificial intelligence, neural reliability

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