Month: August 2018

Geometrical effects on mobility

In this paper we analyze the effect of randomly deleting streets of a synthetic city on the statistics of displacements. Our city is constituted initially by a set of streets that form a regular tessellation of the euclidean plane. Therefore we will have three types of cities, formed by squares, triangles or hexagons. We studied the complementary cumulative distribution function for displacements (CCDF). For the whole set of streets the CCDF is a stretched exponential, and as streets are deleted this function becomes a linear function and then two clear different exponentials. This behavior is qualitatively the same for all the tessellations. Most of this functions has been reported in the literature when studying the displacements of individuals based on cell data trajectories and GPS information. However, in the light of this work, the appearance of different functions for displacements CCDF can be attributed to the connectivity of the underlying street network. It is remarkably that for some proportion of streets we got a linear function for such function, and as far as we know this behavior has not been reported nor considered. Therefore, it is advisable to analyze experimental in the light of connectivity of the street network to make correlations with the present work.


Geometrical effects on mobility
Jorge H. Lopez


On the networked architecture of genotype spaces and its critical effects on molecular evolution

Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a network-of-networks multilayered structure of the map from genotype to function that we begin to unveil.


On the networked architecture of genotype spaces and its critical effects on molecular evolution
Jacobo Aguirre, Pablo Catalán, José A. Cuesta, Susanna Manrubia

Open Biology

Published 4 July 2018.DOI: 10.1098/rsob.180069


Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

Active inference is an ambitious theory that treats perception, inference, and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g., different environments or agent morphologies. In the literature, paradigms that share this independence have been summarized under the notion of intrinsic motivations. In general and in contrast to active inference, these models of motivation come without a commitment to particular inference and action selection mechanisms. In this article, we study if the inference and action selection machinery of active inference can also be used by alternatives to the originally included intrinsic motivation. The perception-action loop explicitly relates inference and action selection to the environment and agent memory, and is consequently used as foundation for our analysis. We reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact. Furthermore, we illustrate the connection to universal reinforcement learning by means of our formalism. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference.


Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

Martin Biehl, Christian Guckelsberger, Christoph Salge, Simón C. Smith and Daniel Polani

Front. Neurorobot., 30 August 2018 |


Social transmission in networks: global efficiency peaks with intermediate levels of modularity

In myriad biological systems, multiple lines of evidence indicate that modularity, wherein parts of a network are organized into modules such as subgroups in animal networks, may affect social transmission processes. In animal societies, there is increased interest in understanding variation in the effects of modularity on transmission as it may provide important insight into a given network’s performance, in addition to the evolutionary consequences the structure of the network may have for individual fitness. Yet, to our knowledge, the degree to which network efficiency is modularity dependent has not yet been investigated in great detail in behavioral and evolutionary ecology. Here, we investigated to what degree network efficiency, as a proxy for social transmission, is modularity dependent. We created 2798 networks varying in group size and density, and tested whether network structure (density, Newman’s modularity, eigenvector centralization) and group size shape network efficiency. We also used published data from 41 primate social networks to test whether the predictions generated in our simulations were supported by empirical observations. Our results show a non-linear relationship between modularity and global efficiency, with the latter peaking at intermediate values of modularity in both theoretical and empirical networks. This phenomenon may have relevance for observed variation in social structure and its link with network performance. Our results may thus provide a basis from which to discuss the evolution of complex systems such as animal societies.


Social transmission in networks: global efficiency peaks with intermediate levels of modularity

Valéria Romano, Mengyu Shen, Jérôme Pansanel, Andrew J. J. MacIntosh

Behavioral Ecology and Sociobiology
September 2018, 72:154