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    Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans
    (Lausanne : Frontiers Media, 2019) Pournaki, Armin; Merfort, Leon; Ruiz, Jorge; Kouvaris, Nikos E.; Hövel, Philipp; Hizanidis, Johanne
    We investigate synchronization patterns and chimera-like states in the modular multilayer topology of the connectome of Caenorhabditis elegans. In the special case of a designed network with two layers, one with electrical intra-community links and one with chemical inter-community links, chimera-like states are known to exist. Aiming at a more biological approach based on the actual connectivity data, we consider a network consisting of two synaptic (electrical and chemical) and one extrasynaptic (wireless) layers. Analyzing the structure and properties of this layered network using Multilayer-Louvain community detection, we identify modules whose nodes are more strongly coupled with each other than with the rest of the network. Based on this topology, we study the dynamics of coupled Hindmarsh-Rose neurons. Emerging synchronization patterns are quantified using the pairwise Euclidean distances between the values of all oscillators, locally within each community and globally across the network. We find a tendency of the wireless coupling to moderate the average coherence of the system: for stronger wireless coupling, the levels of synchronization decrease both locally and globally, and chimera-like states are not favored. By introducing an alternative method to define meaningful communities based on the dynamical correlations of the nodes, we obtain a structure that is dominated by two large communities. This promotes the emergence of chimera-like states and allows to relate the dynamics of the corresponding neurons to biological neuronal functions such as motor activities. © Copyright © 2019 Pournaki, Merfort, Ruiz, Kouvaris, Hövel and Hizanidis.
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    REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits
    (Katlenburg-Lindau : Copernicus, 2021) Baumstark, Lavinia; Bauer, Nico; Benke, Falk; Bertram, Christoph; Bi, Stephen; Gong, Chen Chris; Dietrich, Jan Philipp; Dirnaichner, Alois; Giannousakis, Anastasis; Hilaire, Jerome; Klein, David; Koch, Johannes; Leimbach, Marian; Levesque, Antoine; Madeddu, Silvia; Malik, Aman; Merfort, Anne; Merfort, Leon; Odenweller, Adrian; Pehl, Michaja; Pietzcker, Robert C.; Piontek, Franziska; Rauner, Sebastian; Rodrigues, Renato; Rottoli, Marianna; Schreyer, Felix; Schultes, Anselm; Soergel, Bjoern; Soergel, Dominika; Strefler, Jessica; Ueckerdt, Falko; Kriegler, Elmar; Luderer, Gunnar
    This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an integrated assessment model (IAM), provides an integrated view of the global energy–economy–emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multiregional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon from 2005 to 2100. The resulting solution corresponds to the decentralized market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables the analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input and output variables. Each module can be represented by different realizations, enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. Thus, the framework can be used for a variety of applications in a customized form, balancing requirements for detail and overall runtime and complexity.