TY - JOUR
T1 - Memory Formation in Adaptive Networks
AU - Bhattacharyya, Komal
AU - Zwicker, David
AU - Alim, Karen
N1 - Publisher Copyright:
© 2022 authors. Published by the American Physical Society.
PY - 2022/7/8
Y1 - 2022/7/8
N2 - The continuous adaptation of networks like our vasculature ensures optimal network performance when challenged with changing loads. Here, we show that adaptation dynamics allow a network to memorize the position of an applied load within its network morphology. We identify that the irreversible dynamics of vanishing network links encode memory. Our analytical theory successfully predicts the role of all system parameters during memory formation, including parameter values which prevent memory formation. We thus provide analytical insight on the theory of memory formation in disordered systems.
AB - The continuous adaptation of networks like our vasculature ensures optimal network performance when challenged with changing loads. Here, we show that adaptation dynamics allow a network to memorize the position of an applied load within its network morphology. We identify that the irreversible dynamics of vanishing network links encode memory. Our analytical theory successfully predicts the role of all system parameters during memory formation, including parameter values which prevent memory formation. We thus provide analytical insight on the theory of memory formation in disordered systems.
UR - http://www.scopus.com/inward/record.url?scp=85134500901&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.129.028101
DO - 10.1103/PhysRevLett.129.028101
M3 - Article
C2 - 35867448
AN - SCOPUS:85134500901
SN - 0031-9007
VL - 129
JO - Physical Review Letters
JF - Physical Review Letters
IS - 2
M1 - 028101
ER -