Some aspects of interrelations between ants (Hymenoptera,Formicidae) and polistine wasps (Hymenoptera,Vespidae)

Embodied, situated and enactive aspects of relationships of polistine wasps with ants are considered within the framework of the theory of autopoiesis. The idea of the embodied interaction implies specific nestbuilding and protective behavior in polistine wasps. The paper examines the adaptive role of applying ant repellent on the petiole and nest and the latitude gradient of such behavior in re-social wasp species. The situated interaction is considered in the environmental context: the mortality of Polistes gallicus (L.) colonies as a result of predatory attacks of ants Myrmica bergi Ruzsky is analyzed in the Lower Dnieper basin (Ukraine). The enactive interaction includes both spontaneous autonomous activity of its participants as a result of self-organization and specific features of the spatial structure of the prey’s population forming under the impact of the predator. The applicability of some “predator-prey” models is discussed.     

Behavioural robustness: a link between distributed mechanisms and coupled transient dynamics

The emergence of a unified cognitive behaviour relies on the coordination of specialized components that distribute across a ‘brain’, body and environment. Although a general dynamical mechanism involved in agent-environment integration is still largely unknown for behavioural robustness, discussions here are focussed on one of the most plausible candidate: the formation of distributed mechanisms working in transient during agent-environment coupling. This article provides discussions on this sort of coordination based on a mobile object-tracking task with situated, embodied and minimal agents, and tests for robust yet adaptive behaviour. The proposed scenario provides examples of behavioural mechanisms that counterbalance the functional organization of internal control activity and agents’ situatedness to enable the evolution of a two-agent interaction task. Discussions in this article suggest that future studies of distributed cognition should take into account that there are at least two possible modes of interpreting distributed mechanisms and that these have a qualitatively different effect on behavioural robustness.     

Variants of guided self-organization for robot control

Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop. We show that the behavioral manifold that is covered in this way can be modified in a goal-dependent way without reducing the self-induced activity of the robot. We present three strategies for guided self-organization, namely by using external rewards, a problem-specific error function, or assumptions about the symmetries of the desired behavior. The strategies are analyzed for two different robots in a physically realistic simulation.     

Flexibility in starfish behavior by multi-layered mechanism of self-organization

Understanding animal behavior as a product of natural selection sometimes result in an underestimation of the animal's adaptability: lower animals with poor mental capabilities are usually considered to simply exhibit innate behavioral patterns. Self-organized behavior may exhibit both stability of certain behavioral patterns and flexibility in adopting those patterns. Thus, the self-organization processes of starfish arm and tube feet movements are investigated, by observing obstacle avoidance behavior and tube feet of moving starfish. As starfish have no central nervous systems, their behaviors are the result of certain self-organization processes. Starfish have hierarchically constructed motor organs consisting of arms and tube feet. The collective behavior of the tube feet does not function only as simple fluctuations in the arms' coordination. As a result, starfish seem to exhibit more versatile behavioral changes than expected from the original model of a self-organized behavior.     

Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment

It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties ("multiple timescales"). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems.     

Self-organization in biological systems

Biological systems are considered that are capable of dynamic self-organization, i.e., spontaneous emergence of spatio-temporal order with the formation of various spatio-temporal patterns. A cell is involved in the organization of ontogenesis of all stages. Embryonic cells exhibit coordinated social behavior and generate ordered morphological patterns displaying variability and equifinality of development. Physical and topological patterns are essential for biological systems as an imperative that restricts and directs biological morphogenesis. Biological self-organization is directed and fixed by natural selection during which selection of the most sustainable, flexible, modular systems capable of adaptive self-organization occurs.     

Self-organization of spiking neural network that generates autonomous behavior in a real mobile robot

In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot for generation of adoptive and goal-directed behavior. First, we formulated a SNN model whose inputs and outputs were analog and the hidden unites are interconnected each other. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot. In the environment, the robot incrementally organized the network and the given tasks were successfully performed. The time needed to acquire the desired adoptive and goal-directed behavior using the proposed self-organization method was much less than that with the genetic evolution, approximately one fifth.     

微生物相互作用中的空间自组织

微生物在全球生态系统中占据着重要地位, 其中一个重要的研究领域是微生物与环境(包括无机环境与生物环境)之间的相互作用。在生态相互作用过程中, 微生物常常通过自组织形成特定的空间模式。微生物的空间模式在种群稳定性、群落动态变化以及维持合作行为方面具有重要作用。本文中, 我们梳理了当下对微生物空间自组织及其所形成的空间模式的研究内容, 首先介绍什么是空间自组织, 再根据生态相互作用类型对自组织的空间模式进行描述, 其中重点讨论合作与竞争中的空间模式, 接着关注微生物空间自组织的过程, 最后我们指出空间自组织对整个群体的结构和功能稳定具有重要意义。研究微生物种群间相互作用中的空间模式, 有助于探索维持合作行为的新机制, 进而为微生物共生系统的构建提供新的理解。     

Cells in the system of multicelular organism from positions of non-linear dynamics

The organism physiological systems forming a hierarchic network with mutual dependence and subordination can be considered as systems with non-linear dynamics including positive and negative feedbacks. In the course of evolution there occurred selection of robust, flexible, modular systems capable for adaptive self-organization by non-linear interaction of components, which leads to formation of the ordered in space and time robust and plastic organization of the whole. Cells of multicellular organisms are capable for coordinated “social” behavior with formation of ordered cell assemblies, which provides a possibility of morphological and functional variability correlating with manifestations of the large spectrum of adaptive reactions. The multicellular organism is the multilevel system with hierarchy of numerous subsystems capable for adaptive self-organization; disturbance of their homeostasis can lead to pathological changes. The healthy organism regulates homeostasis, self-renewal, differentiation, and apoptosis of cells serving its parts and construction blocks by preserving its integrity and controlling behavior of cells. The systemic approach taking into account biological regularities of the appearance and development of functions in evolution of multicellular organisms opens new possibilities for diagnostics and treatment of many diseases.     

Self-organization and productivity in semi-arid ecosystems: implications of seasonality in rainfall

Spatial self-organization including striking vegetation patterns observed in arid ecosystems has been studied in models with uniform rainfall. In this paper, we present a fully seasonal rainfall model that produces vegetation patterns found in nature by including the natural adaptation of plants to scarcity of water and the consequent seasonal variation in their growth and metabolic rate. We present results for the mean-field and spatially extended versions of the model. We find that the patterns depend on the duration of the wet season even with fixed total annual precipitation (PPT) showing how seasonality affects spatial self-organization. We observe that the productivity can vary for fixed PPT as a function of the duration thereby providing another source of observed variations. We compute the maximum vegetation cover as function of PPT and find that the behavior is consistent with observations. We comment on the implications for regime shifts due to increased interannual fluctuations caused by climatic changes. Our specific model calculations provide more general conclusions for ecosystems with competition for scarce resources due to seasonal variations in the resource, especially for self-organization and productivity.     

Perception–action loops of multiple agents: informational aspects and the impact of coordination

Embodied agents can be conceived as entities perceiving and acting upon an external environment. Probabilistic models of this perception-action loop have paved the way to the investigation of information-theoretic aspects of embodied cognition. This formalism allows (i) to identify information flows and their limits under various scenarios and constraints, and (ii) to use informational quantities in order to induce the self-organization of the agent's behavior without any externally specified drives. This article extends the perception-action loop formalism to multiple agents. The multiple-access channel model is presented and used to identify the relationships between informational quantities of two agents interacting in the same environment. The central question investigated in this article is the impact of coordination. Information-theoretic limits on what can be achieved with and without coordination are identified. For this purpose, different abstract channels are studied, along with a concrete example of agents interacting in space. It is shown that, under some conditions, self-organizing systems based on information-theoretic quantities have a tendency to spontaneously generate coordinated behavior. Moreover, in the perspective of engineering such systems to achieve specific tasks, these information-theoretic limits put constraints on the amount of coordination that is required to perform the task, and consequently on the mechanisms that underlie self-organization in the system.     

Emerging concepts of self-organization and the living state. Special issue

This special issue present papers that examine the concept of self-organization in the origin of life. Concepts explored include chaos and order in open systems, the origin of biochemical organization, non-cellular phases of life on clay, biodynamics necessary for the emergence of energy consumers, molecular evolution, order in self-oscillators, self-organization of semi-conductor physics, self-organizing behavior of microtubules in the cytoskeleton, biogenesis and physics, perceptive funcion, and an overview of the experimental realization of artificial intelligence.     

Pattern formation on the combs of honeybees: increasing fitness by coupling self-organization with templates

Biological patterns are often constructed via a combination of mechanisms including self-organization, templates and recipes. Our understanding of self-organization is becoming increasingly clear, yet how multiple mechanisms work together and what selective advantage they confer over simpler mechanisms is poorly understood. Honeybee (Apis mellifera) combs exhibit a pattern of brood at the bottom, pollen in a band next to it and honey at the top. This study constructs an agent-based model, derived from experimental studies, to determine both how self-organization interacts with two templates and to elucidate a selective basis for the use of multiple mechanisms. The vertical pattern of honey and brood is shown to be dependent on a gravity-based template, while the pollen band is shown to form via the interaction of a queen-based template and self-organization. The study suggests that the selective basis for this complex mechanism may be that colonies have higher growth rates when multiple mechanisms are used as opposed to self-organization alone. As self-organization is used in many contexts in which the addition of supplemental mechanisms could be advantageous, this result may be of general significance to many biological systems.     

Self-organization of collective escape in pigeon flocks

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons’ collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior.     

A neural network model for the self-organization of cortical grating cells

A neural network model with incremental Hebbian learning of afferent and lateral synaptic couplings is proposed,which simulates the activity-dependent self-organization of grating cells in upper layers of striate cortex. These cells, found in areas V1 and V2 of the visual cortex of monkeys, respond vigorously and exclusively to bar gratings of a preferred orientation and periodicity. Response behavior to varying contrast and to an increasing number of bars in the grating show threshold and saturation effects. Their location with respect to the underlying orientation map and their nonlinear response behavior are investigated. The number of emerging grating cells is controlled in the model by the range and strength of the lateral coupling structure.     

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

Collective behavior based on self-organization has been observed in populations of animals from insects to vertebrates. These findings have motivated engineers to investigate approaches to control autonomous multi-robot systems able to reproduce collective animal behaviors, and even to collectively interact with groups of animals. In this article, we show collective decision making by a group of autonomous robots and a group of zebrafish, leading to a shared decision about swimming direction. The robots can also modulate the collective decision-making process in biased and non-biased experimental setups. These results demonstrate the possibility of creating mixed societies of vertebrates and robots in order to study or control animal behavior.     

Psychoneuroimmunology: The problem of the situatedness of illness and the conceptualization of healing

Psychoneuroimmunology claims to go beyond narrow biological perspectives of illness to consider behavioral components as an integral part of health and disease. The conceptualization of the embodiment of behavioral dimensions and how they may be represented in terms of interaction between the central nervous system and the immune system are therefore central theoretical issues. Psychoneuroimmunology is thus an arena in which questions about the body and person in context should come to the fore. There are multiple approaches in the psychoneuroimmunological literature, including those which attempt to address in some fashion the issue of the situatedness of illness. It is argued here that the problem of the representation of situatedness is the primary axis of tension in current research and writing in psychoneuroimmunology. Diverse attempts to represent extremely complex (and non-linear) relationships between behavioral and biological dimensions of immune system functioning drive a number of researchers, though they operate under disciplinary, institutional, and funding constraints in the U.S. which tend to work against the development of competing or radical models within psychoneuroimmunology itself.     

Reconciling classical and individual-based approaches in theoretical population ecology: a protocol for extracting population parameters from individual-based models

The two main approaches in theoretical population ecology-the classical approach using differential equations and the approach using individual-based modeling-seem to be incompatible. Linked to these two approaches are two different timescales: population dynamics and behavior or physiology. Thus, the question of the relationship between classical and individual-based approaches is related to the question of the mutual relationship between processes on the population and the behavioral timescales. We present a simple protocol that allows the two different approaches to be reconciled by making explicit use of the fact that processes operating on two different timescales can be treated separately. Using an individual-based model of nomadic birds as an example, we extract the population growth rate by deactivating all demographic processes-in other words, the individuals behave but do not age, die, or reproduce. The growth rate closely matches the logistic growth rate for a wide range of parameters. The implications of this result and the conditions for applying the protocol to other individual-based models are discussed. Since in physics the technique of separating timescales is linked to some concepts of self-organization, we believe that the protocol will also help to develop concepts of self-organization in ecology.     

Barriers and facilitators of implementation of a community cardiovascular disease prevention programme in Mukono and Buikwe districts in Uganda using the Consolidated Framework for Implementation Research

There is a need to unpack the empirical, practical, and personal challenges within participatory approaches advocated to optimize implementation. The unpredictable, chaotic nature of participatory approaches complicates application of implementation theories, methods, and strategies which do not address researchers’ situatedness within participatory processes. As an implementation scientist, addressing one’s own situatedness through critical reflection is important to unearth how conscious and unconscious approaches, including ontological and epistemological underpinnings, influence the participatory context, process, and outcomes. Therefore, the aim of this exploratory work is to investigate the heretofore blind spot toward the lived experience of implementation researchers within the participatory process. We developed an integrated research-practice partnership (IRPP) to inform the implementation of a gestational weight gain (GWG) control program. Within this IRPP, one investigator conducted a 12-month autoethnography. Data collection and triangulation included field notes, cultural artifacts, and systematic timeline tracking. Data analysis included ethnographic-theoretical dialogue and restorying to synthesize key events and epiphanies into a narrative. Analysis revealed the unpredicted evolution of the GWG program into a maternal health fair and three themes within the researchers’ lived experience: (1) permeable work boundaries, (2) individual and collective blind spots toward the ontological and epistemological underpinnings of implementation paradigms, and (3) maladaptive behaviors seemingly reinforced by the research culture. These themes contributed to the chaos of implementation and to researchers’ experience of inadequate recovery from cognitive, emotional, and practical demands. These themes also demonstrated the importance of contextual factors, subjectivity, and value-based judgments within implementation research. Building on extant qualitative research guidelines, we suggest that researchers anchor their approach to implementation in reflexivity, intentionally and iteratively reflecting on their own situatedness. Through this autoethnography, we have elucidated several strategies based on critical reflection including examining philosophical underpinnings of research, adopting restorative practices that align with one’s values, and embracing personal presence as a foundation of scientific productivity. Within the predominant (post-) positivism paradigms, autoethnography may be criticized as unscientifically subjective or self-indulgent. However, this work demonstrates that autoethnography is a vehicle for third-person observation and first-person critical reflection that is transformative in understanding and optimizing implementation contexts, processes, and outcomes.     

Population behavior and self-organization in the genesis of spontaneous rhythmic activity by developing spinal networks

During development spinal networks generate recurring episodes of rhythmic bursting that can be recorded from motoneurons and interneurons. Optical imaging has identified a set of propriospinal interneurons that may be important in the production of this activity. These neurons are rhythmically active, are recurrently interconnected and have powerful projections to motoneurons. The excitability of this propriospinal network is depressed by activity and recovers in the interval between episodes. These and other observations have been formulated into a qualitative model in which population behavior and self-organization are responsible for the spontaneous activity generated by developing spinal networks.