Review of Community Detection in Complex Brain Networks

The brain network community detection algorithm has become a highly regarded topic in recent years within the fields of neuroscience and network science, widely employed to unveil patterns of structural and functional connectivity in the brain. Due to the complexity of the brain networks and the need to handle multiple subjects and various task scenarios, it significantly increases the difficulty of community detection in this field. This paper focuses on functional magnetic resonance imaging (fMRI) technology and comprehensively reviews the advancements in research regarding algorithms for detecting communities within brain functional networks. Firstly, the basic process, task categories, and method types of brain network community detection algorithms are described. Next, various brain network community detection algorithms are classified in different task scenarios, including separate communities, overlapping communities, hierarchical communities, and dynamic community detection algorithms. A detailed analysis of the advantages and disadvantages of different methods is provided, along with their applicable scopes. Finally, the future directions of brain network community detection algorithms are discussed, including the problem of community detection in multi-subject networks, robustness issues in brain network community detection, and studies on brain network community detection algorithms for multimodal imaging data. This paper can serve as a methodological guide for future research on brain network community structures

‘The uses of ethnography in the science of cultural evolution’. Commentary on Mesoudi, A., Whiten, A. and K. Laland ‘Toward a unified science of cultural evolution’

There is considerable scope for developing a more explicit role for ethnography within the research program proposed in the article. Ethnographic studies of cultural micro-evolution would complement experimental approaches by providing insights into the “natural” settings in which cultural behaviours occur. Ethnography can also contribute to the study of cultural macro-evolution by shedding light on the conditions that generate and maintain cultural lineages

Docitive Networks. A Step Beyond Cognition

Projecte fet en col.laboració amb Centre Tecnològic de Telecomunicacions de CatalunyaCatalà: En les Xarxes Docents es por ta més enllà la idea d'elaborar decisions intel ligents. Per mitjà de compartir informació entre els nodes, amb l'objectiu primordial de reduir la complexitat i millorar el rendiment de les Xarxes Cognitives. Per a això es revisen alguns conceptes importants de les bases de l'Aprenentatge Automàtic, prestant especial atenció a l'aprenentatge per reforç. També es fa una visió de la Teoria de Jocs Evolutius i de la dinàmica de rèpliques. Finalment, simulacions ,basades en el projecte TIC-BUNGEE, es mostren per validar els conceptes introduïts.Castellano: Las Redes Docentes llevan más alla la idea de elaborar decisiones inteligentes, por medio de compartir información entre los nodos, con el objetivo primordial de reducir la complejidad y mejorar el rendimiento de las Redes Cognitiva. Para ello se revisan algunos conceptos importantes de las bases del Aprendizaje Automático, prestando especial atencion al aprendizaje por refuerzo, también damos una visón de la Teoría de Juegos Evolutivos y de la replicación de dinamicas. Por último, las simulaciones basadas en el proyecto TIC-BUNGEE se muestran para validar los conceptos introducidos.English: The Docitive Networks further use the idea of drawing intelligent decisions by means of sharing information between nodes with the prime aim of reduce complexity and enhance performance of Congnitive Networks. To this end we review some important concepts form Machine Learning, paying special atention to Reinforcement Learning, we also go insight Evolutionary Game Theory and Replicator Dynamics. Finally, simulations Based on ICT-BUNGEE project are shown to validate the introduced concepts

ALID: Scalable Dominant Cluster Detection

Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as the dominant clusters. However, the time and space complexity of those methods are dominated by the construction of the affinity graph, which is quadratic with respect to the number of data points, and thus impractical on large data sets. To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT) and develop a scalable algorithm, Approximate Localized Infection Immunization Dynamics (ALID). The major idea is to perform Localized Infection Immunization Dynamics (LID) to find dense subgraph within local range of the affinity graph. LID is further scaled up with guaranteed high efficiency and detection quality by an estimated Region of Interest (ROI) and a carefully designed Candidate Infective Vertex Search method (CIVS). ALID only constructs small local affinity graphs and has a time complexity of O(C(a^*+ {\delta})n) and a space complexity of O(a^*(a^*+ {\delta})), where a^* is the size of the largest dominant cluster and C << n and {\delta} << n are small constants. We demonstrate by extensive experiments on both synthetic data and real world data that ALID achieves state-of-the-art detection quality with much lower time and space cost on single machine. We also demonstrate the encouraging parallelization performance of ALID by implementing the Parallel ALID (PALID) on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours, achieving a speedup ratio of 7.51 with 8 executors

Modelling religious signalling

The origins of human social cooperation confound simple evolutionary explanation. But from Darwin and Durkheim onwards, theorists (anthropologists and sociologists especially) have posited a potential link with another curious and distinctively human social trait that cries out for explanation: religion. This dissertation explores one contemporary theory of the co-evolution of religion and human social cooperation: the signalling theory of religion, or religious signalling theory (RST). According to the signalling theory, participation in social religion (and its associated rituals and sanctions) acts as an honest signal of one's commitment to a religiously demarcated community and its way of doing things. This signal would allow prosocial individuals to positively assort with one another for mutual advantage, to the exclusion of more exploitative individuals. In effect, the theory offers a way that religion and cooperation might explain one another, but which that stays within an individualist adaptive paradigm. My approach is not to assess the empirical adequacy of the religious signalling explanation or contrast it with other explanations, but rather to deal with the theory in its own terms - isolating and fleshing out its core commitments, explanatory potential, and limitations. The key to this is acknowledging the internal complexities of signalling theory, with respect to the available models of honest signalling and the extent of their fit (or otherwise) with religion as a target system. The method is to take seriously the findings of formal modelling in animal signalling and other disciplines, and to apply these (and methods from the philosophy of biology more generally) to progressively build up a comprehensive picture of the theory, its inherent strengths and weaknesses. The first two chapters outline the dual explanatory problems that cooperation and religion present for evolutionary human science, and surveys contemporary approaches toward explaining them. Chapter three articulates an evolutionary conception of the signalling theory, and chapters four to six make the case for a series of requirements, limitations, and principles of application. Chapters seven and eight argue for the value of formal modelling to further flesh out the theory's commitments and potential and describe some simple simulation results which make progress in this regard. Though the inquiry often problematizes the signalling theory, it also shows that it should not be dismissed outright, and that it makes predictions which are apt for empirical testing

The hologenome concept of evolution: a philosophical and biological study

The hologenome concept of evolution is a hypothesis about the evolution of animals and plants. It asserts that the evolution of animals and plants was partially triggered by their interactions with their symbiotic microbiomes. In that vein, the hologenome concept posits that the holobiont (animal host + symbionts of the microbiome) is a unit of selection. The hologenome concept has been severely criticized on the basis that selection on holobionts would only be possible if there were a tight transgenerational host-genotype-to-symbiont-genotype connection. As our current evidence suggests that this is not the case for most of the symbiont species that compose the microbiome of animals and plants, the opportunity for holobiont selection is very low in relation to the opportunity for selection on each of the species that compose the host microbiome. Therefore, holobiont selection will always be disrupted ‘from below’, by selection on each of the species that compose the microbiome. This thesis constitutes a conceptual effort to defend philosophically the hologenome concept. I argue that the criticism according to which holobiont selection requires tight transgenerational host-genotype-to-symbiont-genotype connection is grounded on a metaphysical view of the world according to which the biological hierarchy needs to be nested, such that each new level of selection includes every entity from below. Applied to hologenomes, it entails that the hologenome is a collection of genomes, and selection of hologenomes is assumed to entail cospeciation of the host with the species that constitute its microbiome. Against that interpretation, I propose the ‘stability of traits’ account, according to which hologenome evolution is the result of the action of natural selection in a non-nested hierarchical world. In that vein, hologenome evolution does not entail cospeciation, and thus it does not require tight transgenerational host-genotype-to-symbiont-genotype connection. By embracing a multilevel selection perspective, I argue that hologenome evolution results from the simultaneous action of natural selection on each of the lineages that compose the microbiome, and on the assemblage composed by the host genome plus the functional traits of its microbiome. Hologenome selection occurs when the evolution of the traits of the microbiome result from their effects on the fitness of the host, and it can take the form of multilevel selection 1, or multilevel selection 2. In both cases, hologenome selection entails the evolution of microbiome traits, as well as evolution of the host genome, rather than cospeciation of lineages

Virus-cell interactions in the replication cycle of bovine papillomavirus type 1

http://www.ester.ee/record=b4341428*es

Music in Evolution and Evolution in Music

Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

Evolution of direct reciprocity in group-structured populations

People tend to have their social interactions with members of their owncommunity. Such group-structured interactions can have a profound impact on thebehaviors that evolve. Group structure affects the way people cooperate, andhow they reciprocate each other's cooperative actions. Past work has shown thatpopulation structure and reciprocity can both promote the evolution ofcooperation. Yet the impact of these mechanisms has been typically studied inisolation. In this work, we study how the two mechanisms interact. Using agame-theoretic model, we explore how people engage in reciprocal cooperation ingroup-structured populations, compared to well-mixed populations of equal size.To derive analytical results, we focus on two scenarios. In the first scenario,we assume a complete separation of time scales. Mutations are rare compared tobetween-group comparisons, which themselves are rare compared to within-groupcomparisons. In the second scenario, there is a partial separation of timescales, where mutations and between-group comparisons occur at a comparablerate. In both scenarios, we find that the effect of population structuredepends on the benefit of cooperation. When this benefit is small,group-structured populations are more cooperative. But when the benefit islarge, well-mixed populations result in more cooperation. Overall, our resultsreveal how group structure can sometimes enhance and sometimes suppress theevolution of cooperation.<br