Network dynamics | EPFL Graph Search

Network dynamics is a research field for the study of networks whose status changes in time. The dynamics may refer to the structure of connections of the units of a network, to the collective internal state of the network, or both. The networked systems could be from the fields of biology, chemistry, physics, sociology, economics, computer science, etc. Networked systems are typically characterized as complex systems consisting of many units coupled by specific, potentially changing, interaction topologies. For a dynamical systems' approach to discrete network dynamics, see sequential dynamical system. See also

Engineering Dynamic Hydrogels For In Vitro Organoid Culture

Delphine Lorraine Perle Blondel

Over the past decade, methods to culture stem cells in three dimensions have opened up a plethora of new opportunities for basic and translational research in the life sciences. In particular, the use of natural extracellular matrix (ECM) surrogates, such as mouse tumour-derived Matrigel, unleashed the possibility to recapitulate complex multicellular behaviours in vitro, culminating in the successful derivation of miniaturized organs, termed organoids. While organoids hold tremendous potential as model systems in basic biology, the translational potential of these systems is hampered by their strict dependency on animal-derived matrices that suffer from batch-to-batch variability, potential immunogenicity, and ethical concerns. Recent efforts in engineering covalently crosslinked synthetic hydrogels have attempted to substitute these ill-defined organoid culture systems. However, although these bio-artificial matrices have shown significant potential for 3D organoid culture, their stability and their inherent elastic nature hamper organoid development. Indeed, the native ECM, just like Matrigel, is a highly viscoelastic and dynamic 3D milieu that can relax in response to tissue-induced stress by breaking and subsequently rearranging its network, thus permitting cellular remodelling without compromising the macroscopic stability of the material over time. Therefore, there is a need to develop the next generation of synthetic organoid culture matrices, exhibiting in vivo-like stress-relaxation properties. This thesis provides a perspective on how chemically defined, bio-inspired hydrogels can be designed as potential replacements for native ECM-based matrices for 3D organoid culture. In contrast to traditional synthetic hydrogels that cannot accommodate the dynamic mechanical changes occurring during organoid development, four types of synthetic matrices were developed that are crosslinked, either fully or partially, through dynamic physical bonds. The introduction of such network dynamics is shown to facilitate key morphogenetic processes, such as tissue budding, that are normally absent in purely elastic networks. A unifying hypothesis emerging from these results is that stress relaxation is a crucial requirement for 3D organoid culture. The mechanisms by which stress relaxation influence ISC fate and organoid development remain to be further studied. Preliminary experiments indicated that the stem cells may sense and respond to these characteristics through differential activation of mechanosensing pathways including the transcriptional co-activator YAP. The dynamic hydrogels developed in this thesis hold great promise as tunable environments for stem cell-based self-organization, enabling morphogenetic processes that may be suppressed in conventional synthetic hydrogels systems predominantly used in 3D cell culture applications.

EPFL2019

Slow dynamics in structured neural network models

Samuel Pavio Muscinelli

Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of tens of milliseconds, is well-suited to support motor control of rapid movements. In contrast, to coordinate movements on the order of seconds, a neural network should produce reliable dynamics on a similarly âslowâ timescale. Neurons and synapses exhibit biophysical mechanisms whose timescales range from tens of milliseconds to hours, which suggests a possible role of these mechanisms in producing slow reliable dynamics. However, how such mechanisms influence network dynamics is not yet understood. An alternative approach to achieve slow dynamics in a neural network consists in modifying its connectivity structure. Still, the limitations of this approach and in particular to what degree the weights require fine-tuning, remain unclear. Understanding how both the single neuron mechanisms and the connectivity structure might influence the network dynamics to produce slow timescales is the main goal of this thesis. We first consider the possibility of obtaining slow dynamics in binary networks by tuning their connectivity. It is known that binary networks can produce sequential dynamics. However, if the sequences consist of random patterns, the typical length of the longest sequence that can be produced grows linearly with the number of units. Here, we show that we can overcome this limitation by carefully designing the sequence structure. More precisely, we obtain a constructive proof that allows to obtain sequences whose length scales exponentially with the number of units. To achieve this however, one needs to exponentially fine-tune the connectivity matrix. Next, we focus on the interaction between single neuron mechanisms and recurrent dynamics. Particular attention is dedicated to adaptation, which is known to have a broad range of timescales and is therefore particularly interesting for the subject of this thesis. We study the dynamics of a random network with adaptation using mean-field techniques, and we show that the network can enter a state of resonant chaos. Interestingly, the resonance frequency of this state is independent of the connectivity strength and depends only on the properties of the single neuron model. The approach used to study networks with adaptation can also be applied when considering linear rate units with an arbitrary number of auxiliary variables. Based on a qualitative analysis of the mean-field theory for a random network whose neurons are described by a D -dimensional rate model, we conclude that the statistics of the chaotic dynamics are strongly influenced by the single neuron model under investigation. Using a reservoir computing approach, we show preliminary evidence that slow adaptation can be beneficial when performing tasks that require slow timescales. The positive impact of adaptation on the network performance is particularly strong in the presence of noise. Finally, we propose a network architecture in which the slowing-down effect due to adaptation is combined with a hierarchical structure, with the purpose of efficiently generate sequences that require multiple, hierarchically organized timescales.

EPFL2018

Topological exploration of artificial neuronal network dynamics

Kathryn Hess Bellwald, Gard Spreemann

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics.We propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike train distances systematically improves the performance of our method.