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  Memory capacity of adaptive flow networks

Bhattacharyya, K., Zwicker, D., & Alim, K. (2023). Memory capacity of adaptive flow networks. Physical Review E, 107(3): 034407. doi:10.1103/PhysRevE.107.034407.

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Bhattacharyya, Komal1, Author           
Zwicker, David2, Author           
Alim, Karen1, Author           
Affiliations:
1Max Planck Research Group Biological Physics and Morphogenesis, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2266692              
2Max Planck Research Group Theory of Biological Fluids, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2516693              

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 Abstract: Biological flow networks adapt their network morphology to optimize flow while being exposed to external stimuli from different spatial locations in their environment. These adaptive flow networks retain a memory of the stimulus location in the network morphology. Yet, what limits this memory and how many stimuli can be stored are unknown. Here, we study a numerical model of adaptive flow networks by applying multiple stimuli subsequently. We find strong memory signals for stimuli imprinted for a long time into young networks. Consequently, networks can store many stimuli for intermediate stimulus duration, which balance imprinting and aging.

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Language(s): eng - English
 Dates: 2023-03-302023
 Publication Status: Issued
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevE.107.034407
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Project name : This work was supported by the Max Planck Society. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 947630, FlowMem).
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Project name : FlowMem
Grant ID : 947630
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Physical Review E
  Other : Phys. Rev. E
Source Genre: Journal
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Publ. Info: Melville, NY : American Physical Society
Pages: 8 Volume / Issue: 107 (3) Sequence Number: 034407 Start / End Page: - Identifier: ISSN: 1539-3755
CoNE: https://pure.mpg.de/cone/journals/resource/954925225012