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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26857
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dc.contributor.authorvon der Malsburg, Christoph-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorGrewe, Benjamin F.-
dc.date.accessioned2023-02-09T13:33:44Z-
dc.date.available2023-02-09T13:33:44Z-
dc.date.issued2022-04-22-
dc.identifier.otherarXiv:2205.00002de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26857-
dc.description.abstractIntroduction: In contrast to current AI technology, natural intelligence – the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata – is far superior in terms of learning speed, generalization capabilities, autonomy and creativity. How are these strengths, by what means are ideas and imagination produced in natural neural networks? Methods: Reviewing the literature, we put forward the argument that both our natural environment and the brain are of low complexity, that is, require for their generation very little information and are consequently both highly structured. We further argue that the structures of brain and natural environment are closely related. Results: We propose that the structural regularity of the brain takes the form of net fragments (self-organized network patterns) and that these serve as the powerful inductive bias that enables the brain to learn quickly, generalize from few examples and bridge the gap between abstractly defined general goals and concrete situations. Conclusions: Our results have important bearings on open problems in artificial neural network researchde_CH
dc.format.extent17de_CH
dc.language.isoende_CH
dc.publisherarXivde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectOntogenesisde_CH
dc.subjectEmergencede_CH
dc.subjectStructural regularityde_CH
dc.subjectNet fragmentde_CH
dc.subjectVisual perceptionde_CH
dc.subjectScene representationde_CH
dc.subjectHomeomorphic mappingde_CH
dc.subjectInductive biasde_CH
dc.subjectAutonomous behaviorde_CH
dc.subject.ddc590: Tiere (Zoologie)de_CH
dc.subject.ddc610: Medizin und Gesundheitde_CH
dc.titleA theory of natural intelligencede_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.48550/ARXIV.2205.00002de_CH
dc.identifier.doi10.21256/zhaw-26857-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDIZH Fellowshipde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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von der Malsburg, C., Stadelmann, T., & Grewe, B. F. (2022). A theory of natural intelligence. arXiv. https://doi.org/10.48550/ARXIV.2205.00002
von der Malsburg, C., Stadelmann, T. and Grewe, B.F. (2022) A theory of natural intelligence. arXiv. Available at: https://doi.org/10.48550/ARXIV.2205.00002.
C. von der Malsburg, T. Stadelmann, and B. F. Grewe, “A theory of natural intelligence,” arXiv, Apr. 2022. doi: 10.48550/ARXIV.2205.00002.
VON DER MALSBURG, Christoph, Thilo STADELMANN und Benjamin F. GREWE, 2022. A theory of natural intelligence. arXiv
von der Malsburg, Christoph, Thilo Stadelmann, and Benjamin F. Grewe. 2022. “A Theory of Natural Intelligence.” arXiv. https://doi.org/10.48550/ARXIV.2205.00002.
von der Malsburg, Christoph, et al. A Theory of Natural Intelligence. arXiv, 22 Apr. 2022, https://doi.org/10.48550/ARXIV.2205.00002.


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