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A classical example in biophysical models where such a delayed loss of stability plays an important role is given by elliptic bursting (see, e.g., [29, 40]).
In this study, we formulate a certain engineering-based style of interpretation in which a given neural network is represented as a collection of local linear models where such models are developed around a collection of linearization nodes.
A first analysis showed that this set contains many models where such behavior was intended.
Hence, the pinning density can be much lower compared to models where such a coupling is absent.
On the other hand, the GBD 2000 starts with data on the level of all-cause mortality, and uses all available data on cause of death and cause of death models where such data is not available, to estimate the distribution of major cause groups, including cancers.
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Michael Chwe proposes a model where such a situation is modeled game-theoretically.
This is in contrast to the expectation from the ODE model where such optimization is not selected.
We find that our model, where such circuits are coupled in space, can indeed produce re-emerging waves.
These authors suggested that a bone marrow stem cell abnormality could underlie RA and proposed a disease model where such cells could, under the influence of TNF, differentiate into fibroblast-like cells, and travel to the synovial membrane where they might appear as type B synoviocytes and promote synovitis [ 23].
These transient features might become important when one models coupled cells where such intrinsic transient dynamics might play a role in forming new attractors.
Thus, ZVL transmission models (in communities where such treatment strategy is implemented) that do not explicitly incorporate the effect of such treatment may be over-estimating the disease burden (as measured in terms of R0) in the community.
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