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Memory capacity of adaptive flow networks

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Bhattacharyya,  Komal
Max Planck Research Group Biological Physics and Morphogenesis, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zwicker,  David
Max Planck Research Group Theory of Biological Fluids, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Alim,  Karen
Max Planck Research Group Biological Physics and Morphogenesis, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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PhysRevE.107.034407.pdf
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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-000D-10AF-C
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.