Effect of group organization on the performance of cooperative processes

Problem-solving competence at group level is influenced by the structure of the social networks and so it may shed light on the organization patterns of gregarious animals. Here we use an agent-based model to investigate whether the ubiquity of hierarchical networks in nature could be explained as the result of a selection pressure favoring problem-solving efficiency. The task of the agents is to find the global maxima of NK fitness landscapes and the agents cooperate by broadcasting messages informing on their fitness to the group. This information is then used to imitate, with a certain probability, the fittest agent in their influence networks. The performance of the group is measured by the time required to find the global maximum. For rugged landscapes, we find that the modular organization of the hierarchical network with its high degree of clustering eases the escape from the local maxima, resulting in a superior performance as compared with the scale-free and the random networks. The optimal performance in a rugged landscape is achieved by letting the main hub to be only slightly more propense to imitate the other agents than vice versa. The performance is greatly harmed when the main hub carries out the search independently of the rest of the group as well as when it compulsively imitates the other agents.     

Competitive spiking and indirect entropy minimization of rate code: efficient search for hidden components.

Our motivation, which originates from the psychological and physiological evidences of component-based representations in the brain, is to find neural methods that can efficiently search for structures. Here, an architecture made of coupled parallel working reconstruction subnetworks is presented. Each subnetwork utilizes non-negativity constraint on the generative weights and on the internal representation. 'Spikes' are generated within subnetworks via winner take all mechanism. Memory components are modified in order to directly minimize the reconstruction error and to indirectly minimize the entropy of the spike rate distribution, via a combination of a stochastic gradient search and a novel tuning method. This tuning dynamically changes the learning rate: the higher the entropy of the spike rate, the higher the learning rate of the gradient search in the subnetworks. This method effectively reduces the search space and increases the escape probability from high entropy local minima. We demonstrate that one subnetwork can develop localized and oriented components. Coupled networks can discover and sort components into the subnetworks; a problem subject to combinatorial explosion. Synergy between spike code and rate code is discussed.     

Properties of selected mutations and genotypic landscapes under Fisher's geometric model

The fitness landscape—the mapping between genotypes and fitness—determines properties of the process of adaptation. Several small genotypic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here, we generate several testable predictions for the properties of these small genotypic landscapes under Fisher's geometric model of adaptation. When the ancestral strain is far from the fitness optimum, we analytically compute the fitness effect of selected mutations and their epistatic interactions. Epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected, or coselected in an adaptive walk. Simulations show that genotypic landscapes built from Fisher's model are very close to an additive landscape when the ancestral strain is far from the optimum. However, when it is close to the optimum, a large diversity of landscape with substantial roughness and sign epistasis emerged. Strikingly, small genotypic landscapes built from several replicate adaptive walks on the same underlying landscape were highly variable, suggesting that several realizations of small genotypic landscapes are needed to gain information about the underlying architecture of the fitness landscape.     

基于理想生态系统模式与三维景观指数的徽派村落空间特征解析——以呈坎为例

徽派村落是中国传统村落的典型代表,蕴含了丰富的地域文化和生态内涵,是中国古代人与自然和谐相处的典范,其研究与保护在生态学、地理学、建筑学和美学等领域具有重要的科学价值.本文以呈坎村为例,综合风水理论、心理场理论等,从生态学视角提出理想人居环境的理想生态系统模式,并引入三维景观指数,对徽派村落空间结构进行解析,定量分析村落理想生态系统各景观要素组合结构的生态功能体现.结果表明: 呈坎村立体结构为“山-林-屋-水-林”的景观空间组合模式,形成了以人为中心、自然景观立体环绕的圈层结构,符合理想人居模式.传统基于投影距离的方法造成了景观指数偏差(如对面积与边长的低估).平均斑块面积三维指数比二维指数高6.7%;林地的面积比重三维指数比二维指数高1.0%,其他景观类型的面积比重均有所下降,其中建设用地和耕地等人工斑块的面积比重下降最多.面积与周长指标被低估,形状指标和多样性指标被高估,导致景观格局分析中自然斑块的优势度被低估、人工斑块的优势度被高估.三维景观指数表明,呈坎村落生态系统中各自然要素及组合体现出了较好的生态功能,关键要素和景观生态系统具有较高的稳定性、连接度和聚集性;定量印证了以呈坎为代表的徽派村落是理想的生态系统.     

基于景观分析的西安市生态网络构建与优化

生态网络能够连接破碎的生境斑块、增加景观之间的连接性,对应对城市化引发的景观和生态问题具有重要作用。本研究以西安市为例,运用景观格局指数和形态学空间分析等方法在景观格局现状分析的基础上构建了生态网络,并利用重力模型评估了网络结构,最后提出优化策略。结果表明: 西安市景观丰富度较高,但景观状况南北区域差异明显;生态源地主要分布在南部和东部,北部和中部综合阻力值高、连接度较低;生态廊道分布不均,基于水文分析得到的辐射道可以对生态廊道起到补充作用;通过新增生态源地、补设踏脚石及修复断裂点对生态网络进行优化,可改善生态源地分布不均、部分区域生态廊道过长及道路网阻隔等问题,提出的优化策略可为西安市城市规划提供参考。     

Designing neutral landscapes for data scarce regions in West Africa

Despite its popular adoption and use, neutral landscape models have been unexplored in data scarce areas of the Sudanian Savanna region where its application could serve as inputs for spatial ecosystem service assessment. Thus, the need for an easy to use tool to produce landscape patterns similar to real landscapes in this area is imminent. In this article, we aimed at introducing SG4GISCAME as a tool to meet this purpose by exploring it capabilities to generate landscapes similar to real agricultural landscapes of the Vea catchment area in Ghana in three steps. We used Voronoi tessellation polygons to develop the image patterns. The resulting artificial patterns were subsequently evaluated through a visual and landscape structural metric comparison between the simulated and real landscapes. Finally, we used a modified Turing Test to test the credibility of SG4GISCAME model output through expert pattern identification cues. The results show that SG4GISCAME can successfully generate agricultural landscape mosaics similar to real landscape under different parameters and user specifications. We attribute this to the tools’ intuitive and interactive user interface. Statistical test outcomes of the modified Turing Test suggested that geographic information systems and remote sensing map experts found marked pattern similarities between real and synthesis maps, resulting in challenges in identifying real maps from synthetic ones. Our approach could be replicated in other landscapes of West Africa to provide a substitute for unavailable or expensive spatial data and to test the hypothetical relationship between patchy landscape structure and ecosystem service provision through modelling.     

Comment on: "Energy landscape of a small peptide revealed by dihedral angle principal component analysis"

The dihedral angle principal component analysis method published recently by Mu, Nguyen, and Stock, is shown to produce distortions of the free energy landscape due to the neglect of constraints in the coordinates. It is further shown that these distortions can create artificial minima and energy barriers. The rugged energy landscape that the authors find for a small peptide chain might thus be an artifact of their method.     

Visualizing fitness landscapes

Fitness landscapes are a classical concept for thinking about the relationship between genotype and fitness. However, because the space of genotypes is typically high-dimensional, the structure of fitness landscapes can be difficult to understand and the heuristic approach of thinking about fitness landscapes as low-dimensional, continuous surfaces may be misleading. Here, I present a rigorous method for creating low-dimensional representations of fitness landscapes. The basic idea is to plot the genotypes in a manner that reflects the ease or difficulty of evolving from one genotype to another. Such a layout can be constructed using the eigenvectors of the transition matrix describing the evolution of a population on the fitness landscape when mutation is weak. In addition, the eigendecomposition of this transition matrix provides a new, high-level view of evolution on a fitness landscape. I demonstrate these techniques by visualizing the fitness landscape for selection for the amino acid serine and by visualizing a neutral network derived from the RNA secondary structure genotype-phenotype map.     

Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins

Water molecules and molecular chaperones efficiently help the protein folding process. Here we describe their action in the context of the energy and topological networks of proteins. In energy terms water and chaperones were suggested to decrease the activation energy between various local energy minima smoothing the energy landscape, rescuing misfolded proteins from conformational traps and stabilizing their native structure. In kinetic terms water and chaperones may make the punctuated equilibrium of conformational changes less punctuated and help protein relaxation. Finally, water and chaperones may help the convergence of multiple energy landscapes during protein-macromolecule interactions. We also discuss the possibility of the introduction of protein games to narrow the multitude of the energy landscapes when a protein binds to another macromolecule. Both water and chaperones provide a diffuse set of rapidly fluctuating weak links (low affinity and low probability interactions), which allow the generalization of all these statements to a multitude of networks.     

Structural Bridges through Fold Space

Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.     

Funneled landscape leads to robustness of cellular networks: MAPK signal transduction

We uncover the underlying potential energy landscape for a cellular network. We find that the potential energy landscape of the mitogen-activated protein-kinase signal transduction network is funneled toward the global minimum. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. Funneled landscape is a realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.     

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.     

An Exploration of the Relations between External Representations and Working Memory

It is commonly hypothesized that external representations serve as memory aids and improve task performance by means of expanding the limited capacity of working memory. However, very few studies have directly examined this memory aid hypothesis. By systematically manipulating how information is available externally versus internally in a sequential number comparison task, three experiments were designed to investigate the relation between external representations and working memory. The experimental results show that when the task requires information from both external representations and working memory, it is the interaction of information from the two sources that determines task performance. In particular, when information from the two sources does not match well, external representations hinder instead of enhance task performance. The study highlights the important role the coordination among different representations plays in distributed cognition. The general relations between external representations and working memory are discussed.     

The need for stochastic replication of ecological neural networks

Artificial neural networks are becoming increasingly popular as predictive statistical tools in ecosystem ecology and as models of signal processing in behavioural and evolutionary ecology. We demonstrate here that a commonly used network in ecology, the three-layer feed-forward network, trained with the backpropagation algorithm, can be extremely sensitive to the stochastic variation in training data that results from random sampling of the same underlying statistical distribution, with networks converging to several distinct predictive states. Using a random walk procedure to sample error-weight space, and Sammon dimensional reduction of weight arrays, we demonstrate that these different predictive states are not artefactual, due to local minima, but lie at the base of major error troughs in the error-weight surface. We further demonstrate that various gross weight compositions can produce the same predictive state, suggesting the analogy of weight space as a 'patchwork' of multiple predictive states. Our results argue for increased inclusion of stochastic training replication and analysis into ecological and behavioural applications of artificial neural networks.     

Robustness, evolvability, and optimality of evolutionary neural networks

In a typical optimization problem, the main goal is to search for the appropriate values of the variables that provide the optimal solution of the given function. In artificial neural networks (ANN), this translates to the minimization of the error surface during training such that misclassification is minimized during generalization. However, since optimal training performance does not necessarily imply optimal generalization due to the possibility of overfitting or underfitting, we developed SEPA (Structure Evolution and Parameter Adaptation) which addressed these issues by simultaneously evolving ANN structure and weights. Since SEPA primarily relies on the perturbation function to bring variation in its population, this follow-up study aims to find out SEPAs evolvability, optimality, and robustness in other perturbation functions. Our findings indicate that SEPAs optimal generalization performances are stable and robust from the effect of the different perturbation functions. This is due to the feedback loop between its architecture evolution and weight adaptation such that any shortcoming of the former is compensated by the latter, and vice versa. Our results strongly suggest that proper ANN design requires simultaneous adaptation of ANN structure and weights to avoid one-sided or bias convergence to either the weight or architecture space.     

HOW DOES SPATIAL DISPERSAL NETWORK AFFECT THE EVOLUTION OF PARASITE LOCAL ADAPTATION?

Studying patterns of parasite local adaptation can provide insights into the spatiotemporal dynamics of host–parasite coevolution. Many factors, both biotic and abiotic, have been identified that influence parasite local adaptation. In particular, dispersal and population structuring are considered important determinants of local adaptation. We investigated how the shape of the spatial dispersal network within experimental landscapes affected local adaptation of a bacteriophage parasite to its bacterial host. Regardless of landscape topology, dispersal always led to the evolution of phages with broader infectivity range. However, when the spatial dispersal network resulted in spatial variation in the breadth of phage infectivity range, significant levels of parasite local adaptation and local maladaptation were detected within the same landscape using the local versus foreign definition of local adaptation. By contrast, local adaptation was not detected using the home versus away or local versus global definitions of local adaptation. This suggests that spatial dispersal networks may play an important role in driving parasite local adaptation, particularly when the shape of the dispersal network generates nonuniform levels of host resistance or parasite infectivity throughout a species’ range.