Memory capacity of adaptive flow networks

Komal Bhattacharyya, David Zwicker, Karen Alim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number034407
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume107
Issue number3
DOIs
StatePublished - Mar 2023

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