Name
Abstract | ||
|---|---|---|
The cusp bifurcation provides one of the simplest routes leading to bistability and hysteresis in neuron dynamics. We show that weakly connected networks of neurons near cusp bifurcations that satisfy a certain adaptation condition have quite interesting and complicated dynamics. First, we prove that any such network can be transformed into a canonical model by an appropriate continuous change of variables. Then we show that the canonical model can operate as a multiple attractor neural network or as a globally asymptotically stable neural network depending on the choice of parameters. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1016/S0893-6080(97)00117-2 | Neural Networks |
Keywords | DocType | Volume |
Weakly connected neural networks,Multiple cusp bifurcations,Multiple pitchfork bifurcations,Canonical models,Hebbian learning,Bistability of perception | Journal | 11 |
Issue | ISSN | Citations |
3 | 0893-6080 | 2 |
PageRank | References | Authors |
0.44 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eugene M. Izhikevich | 1 | 1340 | 166.74 |