Name
Title | ||
|---|---|---|
Sensitive Finite-State Computations Using a Distributed Network With a Noisy Network Attractor. |
Abstract | ||
|---|---|---|
We exhibit a class of smooth continuous-state neural-inspired networks composed of simple nonlinear elements that can be made to function as a finite-state computational machine. We give an explicit construction of arbitrary finite-state virtual machines in the spatiotemporal dynamics of the network. The dynamics of the functional network can be completely characterized as a “noisy network attract... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2018.2813404 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Computational modeling,Mathematical model,Noise measurement,Perturbation methods,Robustness,Eigenvalues and eigenfunctions,Nonlinear dynamical systems | Attractor,Topology,Nonlinear system,Noise measurement,Pattern recognition,Computer science,Phase space,Robustness (computer science),Stochastic differential equation,Artificial intelligence,Artificial neural network,Computation | Journal |
Volume | Issue | ISSN |
29 | 12 | 2162-237X |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Ashwin | 1 | 17 | 8.26 |
| Claire M. Postlethwaite | 2 | 6 | 3.53 |