TY - JOUR
T1 - Neural network model for path-finding problems with the self-recovery property
AU - Ueda, Kei Ichi
AU - Kitajo, Keiichi
AU - Yamaguchi, Yoko
AU - Nishiura, Yasumasa
N1 - Publisher Copyright:
© 2019 American Physical Society.
PY - 2019/3/8
Y1 - 2019/3/8
N2 - The large-scale synchronization of neural oscillations is crucial in the functional integration of brain modules, but the combination of modules changes depending on the task. A mathematical description of this flexibility is a key to elucidating the mechanism of such spontaneous neural activity. We present a model that finds the loop structure of a network whose nodes are connected by unidirectional links. Using this model, we propose a path-finding system that spontaneously finds a path connecting two specified nodes. The solution path is represented by phase-synchronized oscillatory solutions. The model has the self-recovery property: that is, it is a system with the ability to find a new path when one of the connections in the existing path is suddenly removed. We show that the model construction procedure is applicable to a wide class of nonlinear systems arising in chemical reactions and neural networks.
AB - The large-scale synchronization of neural oscillations is crucial in the functional integration of brain modules, but the combination of modules changes depending on the task. A mathematical description of this flexibility is a key to elucidating the mechanism of such spontaneous neural activity. We present a model that finds the loop structure of a network whose nodes are connected by unidirectional links. Using this model, we propose a path-finding system that spontaneously finds a path connecting two specified nodes. The solution path is represented by phase-synchronized oscillatory solutions. The model has the self-recovery property: that is, it is a system with the ability to find a new path when one of the connections in the existing path is suddenly removed. We show that the model construction procedure is applicable to a wide class of nonlinear systems arising in chemical reactions and neural networks.
UR - http://www.scopus.com/inward/record.url?scp=85062879461&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.99.032207
DO - 10.1103/PhysRevE.99.032207
M3 - 学術論文
C2 - 30999455
AN - SCOPUS:85062879461
SN - 2470-0045
VL - 99
JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
IS - 3
M1 - 032207
ER -