Using neuroscience to develop artificial intelligence

Ullman, Shimon

doi:10.1126/science.aau6595

Cited by 231Neural Networks and ApplicationsCognitive Computing and NetworksPsychiatry, Mental Health, Neuroscience

Using neuroscience to develop artificial intelligence

Ullman, Shimon·2019·Science

DOI 10.1126/science.aau6595

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Abstract

Combining deep learning with brain-like innate structures may guide network models toward human-like learning

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Crossref · 10.1126/science.aau6595

Cites

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DNNs use a limited set of highly simplified homogeneous artificial neurons, whereas biological neuronal architecture displays a heterogeneity of morphologies and functional connections.
“DNNs use a limited set of highly simplified homogeneous artificial 'neurons', whereas biological neuronal architecture displays a heterogeneity of morphologies and functional connections [17, 18].”
From Anthropomorphism in AI: hype and fallacy (2024) · cites this paper as [18]
Almost everything we know about neurons, such as structure, types and interconnectivity, has not been incorporated in DNNs.
“Shimon Ullman [18] argues that almost everything we know about neurons (e.g., structure, types, interconnectivity) has not been incorporated in these networks [17, 18].”
From Anthropomorphism in AI: hype and fallacy (2024) · cites this paper as [18]
Natural language processing can automate workflows for administrative documentation, including the creation of transcripts and patient-case summaries.
“NLP, a branch of AI that helps computers understand and process human language, can automate workflows for administrative documentation, including the creation of transcripts and patient-case summaries [39].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [39]

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Authors: Ullman, S.
Affiliations: Weizmann Institute of Science (IL)
Corresponding author: Ullman, Shimon
ORCID: Ullman, Shimon
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2019-02-15
Volume: 363
Issue: 6428
Pages: 692-693
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.aau6595
OpenAlex: W2914361849
Cited by: 231
References: 15
Citations by year: 2026: 19 · 2025: 22 · 2024: 44 · 2023: 38 · 2022: 30 · 2021: 31 · 2020: 26 · 2019: 21
Type: journal article
Language: en
Open access: closed
Licence: www.sciencemag.org/about/science-licenses-journal-article-reuse (vor)
Locations: Science (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
Keywords: deep learning, human-like learning, neuromorphic computing
MeSH terms: Deep Learning, Artificial Intelligence, Brain, Humans, Learning, Neurosciences, Neural Networks, Computer

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