In this review, we discuss critical dynamics of simple nonequilibrium models on large connectomes, obtained by diffusion MRI, representing the white matter of the human brain. In the first chapter, we overview graph theoretical and topological analysis of these networks, pointing out that universality allows selecting a representative network, the KKI-18, which has been used for dynamical simulation. The critical and sub-critical behaviour of simple, two- or three-state threshold models is discussed with special emphasis on rare-region effects leading to robust Griffiths phases (GP). Numerical results of synchronization phenomena, studied by the Kuramoto model, are also shown, leading to a continuous analog of the GP, termed frustrated sync...
We implement the Ising model on a structural connectivity matrix describing the brain at two differe...
The spontaneous emergence of coherent behavior through synchronization plays a key role in neural fu...
The field of neural network modelling has grown up on the premise that the massively parallel distri...
The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits uni...
Self-organized criticality is an attractive model for human brain dynamics, but there has been littl...
The spontaneous emergence of coherent behavior through synchronization plays a key role in neural fu...
Synchronization of brain activity fluctuations is believed to represent communication between spatia...
We have extended the study of the Kuramoto model with additive Gaussian noise running on the KKI-18 ...
Understanding the complex dynamics of the human brain is one of the most exciting challenges in mode...
AbstractAt the macroscopic scale, the human brain can be described as a complex network of white mat...
<div><p>We implement the Ising model on a structural connectivity matrix describing the brain at two...
We implement the Ising model on a structural connectivity matrix describing the brain at two differe...
Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a cr...
Like many systems in nature, the brain is a highly organised unit of interacting components. A natur...
Many of the amazing functional capabilities of the brain are collective properties stemming from the...
We implement the Ising model on a structural connectivity matrix describing the brain at two differe...
The spontaneous emergence of coherent behavior through synchronization plays a key role in neural fu...
The field of neural network modelling has grown up on the premise that the massively parallel distri...
The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits uni...
Self-organized criticality is an attractive model for human brain dynamics, but there has been littl...
The spontaneous emergence of coherent behavior through synchronization plays a key role in neural fu...
Synchronization of brain activity fluctuations is believed to represent communication between spatia...
We have extended the study of the Kuramoto model with additive Gaussian noise running on the KKI-18 ...
Understanding the complex dynamics of the human brain is one of the most exciting challenges in mode...
AbstractAt the macroscopic scale, the human brain can be described as a complex network of white mat...
<div><p>We implement the Ising model on a structural connectivity matrix describing the brain at two...
We implement the Ising model on a structural connectivity matrix describing the brain at two differe...
Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a cr...
Like many systems in nature, the brain is a highly organised unit of interacting components. A natur...
Many of the amazing functional capabilities of the brain are collective properties stemming from the...
We implement the Ising model on a structural connectivity matrix describing the brain at two differe...
The spontaneous emergence of coherent behavior through synchronization plays a key role in neural fu...
The field of neural network modelling has grown up on the premise that the massively parallel distri...