Toward a theory of collective resource distribution: a study of a dynamic morphogenesis controller

Visual Functional-Structural Plant Modeling Innovatively as a Compound Eye: Opening a New Way for Advancing the Scientific Cognition of Plant Vision

Plant vision is an interesting interdisciplinary branch of botany and vision science, and its emerging studies have composed an epic journey of discovery. However, there are few endeavors on modeling how a plant as an integrity sees. Inspired by the similarity between those discovered laws of plant vision and the visual performance of some insect species with compound eyes, the visual functional-structural plant modeling as a compound eye is innovatively proposed. Using this adapted basic-pattern-oriented modeling, we tried to validate its feasibility in terms of the structural support, visual pathway, and functional performance. First, for a diversity of woody plants, their crowns proved to show self-similar profiles, which render the omnidirectional surfaces for structurally supporting the proposed model. Second, for many plant species, their branching proved to abide by the Pareto front, which ensures the optimality of assuming the visual pathway along the branching network. Third, in canopies the varying, but existing horizontal and vertical modes of crown shyness are detected, which in functional performance accords with the panoramic visibility of the proposed model. Overall, the feasibility of compound eye modeling is validated preliminarily, with the implication of opening a way for advancing the scientific cognition of plant vision.

Social Integrating Robots Suggest Mitigation Strategies for Ecosystem Decay

We develop here a novel hypothesis that may generate a general research framework of how autonomous robots may act as a future contingency to counteract the ongoing ecological mass extinction process. We showcase several research projects that have undertaken first steps to generate the required prerequisites for such a technology-based conservation biology approach. Our main idea is to stabilise and support broken ecosystems by introducing artificial members, robots, that are able to blend into the ecosystem's regulatory feedback loops and can modulate natural organisms' local densities through participation in those feedback loops. These robots are able to inject information that can be gathered using technology and to help the system in processing available information with technology. In order to understand the key principles of how these robots are capable of modulating the behaviour of large populations of living organisms based on interacting with just a few individuals, we develop novel mathematical models that focus on important behavioural feedback loops. These loops produce relevant group-level effects, allowing for robotic modulation of collective decision making in social organisms. A general understanding of such systems through mathematical models is necessary for designing future organism-interacting robots in an informed and structured way, which maximises the desired output from a minimum of intervention. Such models also help to unveil the commonalities and specificities of the individual implementations and allow predicting the outcomes of microscopic behavioural mechanisms on the ultimate macroscopic-level effects. We found that very similar models of interaction can be successfully used in multiple very different organism groups and behaviour types (honeybee aggregation, fish shoaling, and plant growth). Here we also report experimental data from biohybrid systems of robots and living organisms. Our mathematical models serve as building blocks for a deep understanding of these biohybrid systems. Only if the effects of autonomous robots onto the environment can be sufficiently well predicted can such robotic systems leave the safe space of the lab and can be applied in the wild to be able to unfold their ecosystem-stabilising potential.

CONNECTOME or COLLECTOME? A NEUROPHILOSOPHICAL Perspective

Human beings exist in a biological and social system from a micro to a macro level, by means of "collectivity", a dynamic collaboration that they have established together with the elements in that system in a way to complement each other and realize a common goal. Many neuroscientific concepts used today to explain neuronal processes from which mental functions originate are far from searching answers to traditional philosophical questions. However, the brain - as the generator of highly abstract concepts - is so complex that it cannot be explained by minimalistic approaches. The concept of connectome used in recent years to describe neuronal connections from which brain functions originate exemplifies this minimalistic approach, because it only describes structural and functional connections but does not look at brain functions in a holistic view. For this reason, we propose the concept of collectome - to replace the concept of connectome - that describes a homeomorphic and homotopic neuronal framework that has a bicontinuous style of work from micro to macroscale which is based on fractal rules.