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arXiv:1711.02160nlin.CD

Riddled Basins of Attraction in Systems Exhibiting Extreme Events

Arindam Saha, Ulrike Feudel

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Abstract

Using a system of two FitzHugh-Nagumo units, we demonstrate the occurrence of riddled basins of attraction in delay-coupled systems as the coupling between the units is increased. We characterize the riddled basin using the uncertainty exponent which is a measure of the dimensions of the basin boundary. Additionally, we show that the phase space can be partitioned into pure and mixed regions, where initial conditions in the pure regions certainly avoid the generation of extreme events while initial conditions in the mixed region may or may not exhibit such events. This implies, that any tiny perturbation of initial conditions in the mixed region could yield the emergence of extreme events because the latter state possesses a riddled basin of attraction.

arXiv comments
Journal reference
A. Saha and U. Feudel, "Riddled basins of attraction in systems exhibiting extreme events", Chaos 28, 033610 (2018); https://doi.org/10.1063/1.5012134
DOI
doi:10.1063/1.5012134
Subjects
nlin.CDChaotic Dynamics

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Linked by 1 identifiable commenter in 4 comments across 4 threads, from Feb 2024 to Aug 2024.

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Discussed in comments4 threads

    • If the goal was to create an artificial neural network that better approximated the biological human brain, yes the perceptron model is insufficient. If your goal is to produce a useful model on real hardware and it works...no Remember the constraints of ANNs being universal…

      nyrikkiView on HNParent context

    • dendrocentric compartmentalization, spike timing, bandpass in the dendrites, spike retiming etc... aren't covered in the above. But it is probably important to define 'computable' Typically that means being able that can take a number position as input and output the digit in…

      nyrikkiView on HNParent context

    • It is more than a wild guess, it is a guess based on the outdated perceptron model of the brain. Active dendrites the that can do operations like xor before anything reaches the soma, or use spike timing, once again before the soma are a couple of examples. SNNs, or spikey…

      nyrikkiView on HNParent context

  1. Everything is a linear model 1 comment
    • same thing with any feed forward network too. They are all piece-wise linear in respect to inputs. Layers reduce resource requirements and make some patterns easier or even practical to find, but any ANN that is a FNN supervised learning could be represented as a parametric…

      nyrikkiView on HNParent context

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