Jürgen Jost
Max Planck Society
574 Papers
3.1K Citations
Jürgen Jost is an academic researcher from Max Planck Society. The author has contributed to research in topics: Harmonic map & Curvature. The author has an hindex of 59, co-authored 544 publications. Previous affiliations of Jürgen Jost include Santa Fe Institute & Australian National University.
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Papers
The maximum principle and the Dirichlet problem for Dirac-harmonic maps
TL;DR: In this article, the authors established a maximum principle and uniqueness for Dirac-harmonic maps from a Riemannian spin manifold with boundary into a regular ball in any N-mani-fold manifold.
Symbolic synchronization and the detection of global properties of coupled dynamics from local information
TL;DR: It turns out that the global qualitative properties of the coupled dynamics can be classified into three different phases based on the synchronization of the variables and the homogeneity of the symbolic dynamics, of particular interest is the homogeneous unsynchronized phase.
A notion of nonpositive curvature for general metric spaces
TL;DR: In this article, a new definition of non-positive curvature in metric spaces and its relation to the existing notions of nonpositive curvatures in comparison geometry are studied. But the main feature of this definition is that it applies to all metric spaces, and does not rely on geodesics.
Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity
TL;DR: An adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling is presented that is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.
Self-organized criticality in a mesoscopic model of excitatory-inhibitory neuronal populations by short-term and long-term synaptic plasticity
Masud Ehsani,Jürgen Jost +1 more
TL;DR: This paper proposes an effective stochastic neural field model which captures the dynamics of the mean-field model and shows how the network tunes itself through local long- term synaptic plasticity by STDP and short-term synaptic depression to be close to this bifurcation point.