Memory capacity of adaptive flow networks
arXiv:2208.11192 · doi:10.1103/PhysRevE.107.034407
Abstract
Biological flow networks adapt their network morphology to optimise 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 is 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 ageing.
7 pages, 4 figures, 9 pages of appendix
References in corpus (5)
- Multiple transient memories in experiments on sheared non-Brownian suspensions
- Learning without neurons in physical systems
- Demonstration of Decentralized, Physics-Driven Learning
- Supervised learning in physical networks: From machine learning to learning machines
- Pruning to Increase Taylor Dispersion in Physarum polycephalum Networks