Wang S, Herzog ED, Kiss IZ, Schwartz WJ, Bloch G, Sebek M, Granados-Fuentes D, Wang L, Li J-S.
Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks. Proceedings of the National Academy of Sciences [Internet]. 2018.
Publisher's VersionAbstractInferring connections forms a critical step toward understanding large and diverse complex networks. To date, reliable and efficient methods for the reconstruction of network topology from measurement data remain a challenge due to the high complexity and nonlinearity of the system dynamics. These obstacles also form a bottleneck for analyzing and controlling the dynamic structures (e.g., synchrony) and collective behavior in such complex networks. The novel contribution of this work is to develop a unified data-driven approach to reliably and efficiently reveal the dynamic topology of complex networks in different scales—from cells to societies. The developed technique provides guidelines for the refinement of experimental designs toward a comprehensive understanding of complex heterogeneous networks.Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.
Yirmiya K, Segal NL, Bloch G, Knafo‐Noam A.
Prosocial and self‐interested intra‐twin pair behavior in monozygotic and dizygotic twins in the early to middle childhood transition. Developmental Science [Internet]. 2018;21 (6) :e12665.
Publisher's VersionAbstractAbstract Several related and complementary theoretical frameworks have been proposed to explain the existence of prosocial behavior, despite its potential fitness cost to the individual. These include kin selection theory, proposing that organisms have a propensity to help those to whom they are genetically related, and reciprocity, referring to the benefit of being prosocial, depending on past and future mutual interactions. A useful paradigm to examine prosociality is to compare mean levels of this behavior between monozygotic (MZ) and dizygotic (DZ) twins. Here, we examined the performance of 883 6.5‐year‐old twins (139 MZ and 302 DZ same‐sex 6.5‐year‐old full twin pairs) in the Differential Productivity Task. In this task, the twins’ behaviors were observed under two conditions: working for themselves vs. working for their co‐twin. There were no significant differences between the performances of MZ and DZ twins in the prosocial condition of the task. Correlations within the twin dyads were significantly higher in MZ than DZ twins in the self‐interested condition. However, similar MZ and DZ correlations were found in the prosocial condition, supporting the role of reciprocity in twins’ prosociality towards each other.
Nakayama S, Diner D, Holland JG, Bloch G, Porfiri M, Nov O.
The Influence of Social Information and Self-expertise on Emergent Task Allocation in Virtual Groups. Frontiers in Ecology and Evolution [Internet]. 2018;6 :16.
Publisher's VersionAbstractDynamic group coordination facilitates adaptive division of labor in response to group-level changes. Yet, little is known about how it can be operationalized in online collaborations among individuals with limited information about each other. We hypothesized that simple social information about the task distribution of others can elicit emergent task allocation. We conducted an online experiment where participants analyze images of a polluted canal by freely switching between two tasks: creating keyword-based tags for images and categorizing existing tags. During the task execution, we presented experimentally manipulated information about the contrasting group-level task distributions. Participants did not change the effort allocation between the tasks when they were notified that the group deficits workers in the task they intrinsically prefer. By contrast, they allocated more effort to the less preferred task than they would intrinsically do when their intrinsic effort allocation counterbalances the current distribution of workers in the group. Such behavioral changes were observed more strongly among those with higher skills in the less preferred task. Our results demonstrate the possibility of optimizing group coordination through design interventions at the individual level that lead to spontaneous adaption of division of labor at the group level. When participants were provided information about the group-level task distribution, they tend to allocate more effort to the task against their intrinsic preference.
Beer K, Kolbe E, Kahana NB, Yayon N, Weiss R, Menegazzi P, Bloch G, Helfrich-Förster C.
Pigment-Dispersing Factor-expressing neurons convey circadian information in the honey bee brain. Open Biology [Internet]. 2018;8 (1).
Publisher's VersionAbstractPigment-Dispersing Factor (PDF) is an important neuropeptide in the brain circadian network of Drosophila and other insects, but its role in bees in which the circadian clock influences complex behaviour is not well understood. We combined high-resolution neuroanatomical characterizations, quantification of PDF levels over the day and brain injections of synthetic PDF peptide to study the role of PDF in the honey bee Apis mellifera. We show that PDF co-localizes with the clock protein Period (PER) in a cluster of laterally located neurons and that the widespread arborizations of these PER/PDF neurons are in close vicinity to other PER-positive cells (neurons and glia). PDF-immunostaining intensity oscillates in a diurnal and circadian manner with possible influences for age or worker task on synchrony of oscillations in different brain areas. Finally, PDF injection into the area between optic lobes and the central brain at the end of the subjective day produced a consistent trend of phase-delayed circadian rhythms in locomotor activity. Altogether, these results are consistent with the hypothesis that PDF is a neuromodulator that conveys circadian information from pacemaker cells to brain centres involved in diverse functions including locomotion, time memory and sun-compass orientation.
openbiology18.pdf