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  Noise-induced artificial intelligence

Zhao, A., Ermolaeva, A., Ullner, E., Kurths, J., Gordleeva, S., Zaikin, A. (2022): Noise-induced artificial intelligence. - Physical Review Research, 4, 4, 043069.
https://doi.org/10.1103/PhysRevResearch.4.043069

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 Creators:
Zhao, Alex1, Author
Ermolaeva, Anastasia1, Author
Ullner, Ekkehard1, Author
Kurths, Jürgen2, Author              
Gordleeva, Susanna1, Author
Zaikin, Alexey1, Author
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: We show that unavoidable stochastic fluctuations are not only affecting information processing in a destructive or constructive way, but may even induce conditions necessary for the artificial intelligence itself. In this proof-of-principle paper we consider a model of a neuron-astrocyte network under the influence of multiplicative noise and show that information encoding (loading, storage, and retrieval of information patterns), one of the paradigmatic signatures of intelligent systems, can be induced by stochastic influence and astrocytes. Hence, astrocytes, recently proved to play an important role in memory and cognitive processing in mammalian brains, may play also an important role in the generation of a system's features providing artificial intelligence functions. Hence, one could conclude that intrinsic stochasticity is probably positively utilized by brains, not only to optimize the signal response but also to induce intelligence itself, and one of the key roles, played by astrocytes in information processing, could be dealing with noises.

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Language(s): eng - English
 Dates: 2022-10-312022-10-31
 Publication Status: Finally published
 Pages: 6
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevResearch.4.043069
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Gold Open Access
 Degree: -

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Title: Physical Review Research
Source Genre: Journal, other, oa
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Pages: - Volume / Issue: 4 (4) Sequence Number: 043069 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/20200302
Publisher: American Physical Society (APS)