학술논문

Consistency Hierarchy of Reservoir Computers
Document Type
Periodical
Source
IEEE Transactions on Neural Networks and Learning Systems IEEE Trans. Neural Netw. Learning Syst. Neural Networks and Learning Systems, IEEE Transactions on. 33(6):2586-2595 Jun, 2022
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
General Topics for Engineers
Reservoirs
Correlation
Computers
Australia
Time series analysis
Nonlinear dynamical systems
Matrix decomposition
Complex networks
multivariate time series
nonlinear dynamics
reservoir computing (RC)
Language
ISSN
2162-237X
2162-2388
Abstract
We study the propagation and distribution of information-carrying signals injected in dynamical systems serving as reservoir computers. Through different combinations of repeated input signals, a multivariate correlation analysis reveals measures known as the consistency spectrum and consistency capacity. These are high-dimensional portraits of the nonlinear functional dependence between input and reservoir state. For multiple inputs, a hierarchy of capacities characterizes the interference of signals from each source. For an individual input, the time-resolved capacities form a profile of the reservoir’s nonlinear fading memory. We illustrate this methodology for a range of echo state networks.