Interactions often require the proximity between particles. The movement of particles, thus, drives the change of the neighbors which are located in their proximity, leading to a sequence of interactions. In pathogenic contagion, infections occur through proximal interactions, but at the same time, the movement facilitates the co-location of different strains. We analyze how the particle velocity impacts on the phase transitions on the contagion process of both a single infection and two cooperative infections. First, we identify an optimal velocity (close to half of the interaction range normalized by the recovery time) associated with the largest epidemic threshold, such that decreasing the velocity below the optimal value leads to larger outbreaks. Second, in the cooperative case, the system displays a continuous transition for low velocities, which becomes discontinuous for velocities of the order of three times the optimal velocity. Finally, we describe these characteristic regimes and explain the mechanisms driving the dynamics.
Particle velocity controls phase transitions in contagion dynamics
Jorge P. Rodríguez, Fakhteh Ghanbarnejad & Víctor M. Eguíluz
Scientific Reports volume 9, Article number: 6463 (2019)
15-17 July 2019
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
Approaches and techniques from Physics, Mathematics and Computational Science are increasingly becoming critical for understanding and modelling the brain, and also for designing and interpreting experiments. Modeling is an essential tool to cut through the vast complexity of neurobiological systems and their many interacting elements.
ConTaMiNEURO 2019 wants to convey central ideas, methods, and practices of modern computational neuroscience through a combination of lectures, tutorials, and seminars. During the course’s mornings, distinguished international faculty deliver lectures and seminars on selected topics in computational neuroscience (see below). For the remainder of the time, students work on research projects in teams of 3-4 people under close supervision of expert tutors and faculty. Research projects will include data analyses and the development of theories to explain experimental observations.
Thanks to the support of our sponsor, the early bird registration fee for Ph.Ds students and Post Docs is 200 Euro, the deadline is 30 June 2019. MAX 40 participants.
An amazing line uo of Invited Speakers:
Ken Miller, Columbia University, USA. Yiota Poirazi, IMBB-FORTH, Creta. Nicolas Brunel*, Duke University, USA. Andrea Brovelli, University of Marsille. Jordi Soriano, University of Barcellona Mazzuccato Luca, University of Oregon Stefano Panzeri, IIT Rovereto Jesus Cortes, IKERBASQUE: The Basque Foundation for Science Lucilla De Arcangelis, University of Campania Raffaella Burioni, University of Parma Gustavo Deco, Pompeu Fabra University, Barcelona Jonathan Touboul, Brandeis University (USA) Susanne Ditlevsen, University of Copenaghen Alessandro Treves, Trieste SISSA Misha Tsodyks, Weizmann Institute of Science Eleni Vasilaki, University of Shieffield Marcello Pelillo, European Center of Living Techonology & University of Venice Tommaso Fellin, IIT Genova Maria Victoria Sánchez Vives , ICREA-IDIBAPS (Barcelona) Marco dal Maschio, University of Padova Serena del Santo, University of Granada Lorenzo Fontolan, Janelia Research Campus
(…) In this paper I review some of this recent work on the ‘stochastic thermodynamics of computation’. After reviewing the salient parts of information theory, computer science theory, and stochastic thermodynamics, I summarize what has been learned about the entropic costs of performing a broad range of computations, extending from bit erasure to loop-free circuits to logically reversible circuits to information ratchets to Turing machines. These results reveal new, challenging engineering problems for how to design computers to have minimal thermodynamic costs. They also allow us to start to combine computer science theory and stochastic thermodynamics at a foundational level, thereby expanding both.
The stochastic thermodynamics of computation
David H Wolpert
Journal of Physics A: Mathematical and Theoretical, Volume 52, Number 19
Shannon’s Information Theory 70 years on
The theory of autopoiesis holds that an organism can be defined as a network of processes. However, an organism also has a physical body. The relationship between these two things—network and body—has been raised in this issue of Adaptive Behaviour, with reference to an extended interpretation of autopoiesis. This perspective holds that the network and the body are distinct things, and that the network should be thought of as extending beyond the boundaries of the body. The relationship between body and network is subtle, and I revisit it here from the extended perspective. I conclude that from an organism = network perspective, the body is a biological solution to the problem of maintaining both the distinctness of an organism, separate from but engaged with its environment and other organisms, and its distinctiveness as a particular individual.
The necessity of extended autopoiesis