Energy saving mechanisms, collective behavior and the variation range hypothesis in biological systems: A review

Why collective behaviours self-organize to criticality: a primer on information-theoretic and thermodynamic utility measures

Collective behaviours are frequently observed to self-organize to criticality. Existing proposals to explain these phenomena are fragmented across disciplines and only partially answer the question. This primer compares the underlying, intrinsic, utilities that may explain the self-organization of collective behaviours near criticality. We focus on information-driven approaches (predictive information, empowerment and active inference), as well as an approach incorporating both information theory and thermodynamics (thermodynamic efficiency). By interpreting the Ising model as a perception-action loop, we compare how different intrinsic utilities shape collective behaviour and analyse the distinct characteristics that arise when each is optimized. In particular, we highlight that thermodynamic efficiency-measuring the ratio of predictability gained by the system to its energy costs-reaches its maximum at the critical regime. Finally, we propose the Principle of Super-efficiency, suggesting that collective behaviours self-organize to the critical regime where optimal efficiency is achieved with respect to the entropy reduction relative to the thermodynamic costs.

Aerodynamic Characteristics of a Tandem Flapping Wing in Inclined Stroke Plane Hovering with Ground Effect

The present two-dimensional study investigates the ground effect on the aerodynamic characteristics of a tandem flapping wing in inclined stroke plane hovering using ANSYS Fluent. The role of various wing kinematics parameters (flapping frequency f, stroke amplitude Ao/c, and phase difference ψ = 0° and 180°), in combination with ground distance (D* = D/c), is studied. The results reveal that a large stroke amplitude Ao/c decreases vertical force generation for both in-phase and counter-stroking patterns. The vertical force notably increases for both in-phase and counter-stroking wings when D* is extremely small (D* = 0.5). A maximum vertical force enhancement of approximately 65% and 35% is observed for in-phase and counter-stroking patterns, respectively, at D* = 0.5. This enhancement is primarily attributed to the strengthening of detached vortices on the lower surface of the wings during the middle of the downstroke when flapping at extremely small ground distances. In addition, the wing-wing interaction and secondary rebound vortex, caused by wing-ground interaction, also play a key role in vertical force generation. The wing-ground interaction positively influences both vertical and thrust force generation for in-phase and counter-stroking wings at small ground distances. In general, the vertical and thrust forces generated by in-phase stroking wings are greater than those produced by counter-stroking wings.

A Josephson junction-coupled neuron with double capacitive membranes

The channel currents have distinct magnetic field effect and any changes of the electromagnetic field or electirc stimulus will change the membrane potential effectively. A feasible neuron model considers the distinct physical characteristic is more suitable to mimic the neural activities accompanying with shift in energy level. A Josephson junction (JJ) is connected to a neural circuit for estimating the effect of external magnetic field and two capacitors are connected via a linear resistor for mimicing the capacitive field beside two sides of the cell membrane. Its equivalent Hamilton energy is calculated to show the relation between firing mode and energy level. Noisy disturbance is imposed to predict the occurrence of coherence resonance, and the biophysical neuron is excited to present higher energy level. This new neuron model can address the field effect and the biophysical property of cell membrane considered as combination of capacitive fields in double capacitors. It can mimic the physical property of outer and inner membranes, and energy exchange across the double membranes explains the energy mechanism in neural activities. Time-varying energy diveristy between capacitive field is crucial for supporting continuous firing activities. The JJ channel discerns slight changes in external magnetic field and regularity is stabilized under coherence resonance in presence of noisy excitation on the membrane or ion channels.

Trust Is for the Strong: How Health Status May Influence Generalized and Personalized Trust

In the trust-health relationship, how trusting other people in society may promote good health is a topic often examined. However, the other direction of influence-how health may affect trust-has not been well explored. In order to investigate this possible effect, we employed the Bayesian Mindsponge Framework (BMF) analytics to go deeper into the information processing mechanisms underlying the expressions of trust. Conducting a Bayesian analysis on a dataset of 1237 residents from Cali, Colombia, we found that general health status is positively associated with generalized trust, but recent experiences of illnesses/injuries have a negative moderating effect. Personalized trust is largely unchanged across different general health conditions, but the trust level becomes higher with recent experiences of illnesses/injuries. Psychophysiological mechanisms of increasing information filtering intensity toward unfamiliar sources during a vulnerable state of health is a plausible explanation of found patterns in generalized trust. Because established personal relationships are reinforced information channels, personalized trust is not affected as much. Rather, the results suggest that people may rely even more on loved ones when they are in bad health conditions. This exploratory study shows that the trust-health relationship can be examined from a different angle that may provide new insights.

Scale-free correlations and potential criticality in weakly ordered populations of brain cancer cells

Collective behavior spans several orders of magnitude of biological organization, from cell colonies to flocks of birds. We used time-resolved tracking of individual glioblastoma cells to investigate collective motion in an ex vivo model of glioblastoma. At the population level, glioblastoma cells display weakly polarized motion in the (directional) velocities of single cells. Unexpectedly, fluctuations in velocities are correlated over distances many times the size of a cell. Correlation lengths scale linearly with the maximum end-to-end length of the population, indicating that they are scale-free and lack a characteristic decay scale other than the size of the system. Last, a data-driven maximum entropy model captures statistical features of the experimental data with only two free parameters: the effective length scale (nc) and strength (J) of local pairwise interactions between tumor cells. These results show that glioblastoma assemblies exhibit scale-free correlations in the absence of polarization, suggesting that they may be poised near a critical point.

Dynamics and risk sharing in groups of selfish individuals

Understanding why animals organize in collective states is a central question of current research in, e.g., biology, physics, and psychology. More than 50 years ago, W.D. Hamilton postulated that the formation of animal herds may simply result from the individual's selfish motivation to minimize their predation risk. The latter is quantified by the domain of danger (DOD) which is given by the Voronoi area around each individual. In fact, simulations show that individuals aiming to reduce their DODs form compact groups similar to what is observed in many living systems. However, despite the apparent simplicity of this problem, it is not clear what motional strategy is required to find an optimal solution. Here, we use the framework of Multi Agent Reinforcement Learning (MARL) which gives the unbiased and optimal strategy of individuals to solve the selfish herd problem. We demonstrate that the motivation of individuals to reduce their predation risk naturally leads to pronounced collective behaviors including the formation of cohesive swirls. We reveal a previously unexplored rather complex intra-group motion which eventually leads to a evenly shared predation risk amongst selfish individuals.

Energy-guided synapse coupling between neurons under noise

From a physical viewpoint, any external stimuli including noise disturbance can inject energy into the media, and the electric response is regulated by the equivalent electric stimulus. For example, mode transition in electric activities in neurons occurs and kinds of spatial patterns are formed during the wave propagation. In this paper, a feasible criterion is suggested to explain and control the growth of electric synapse and memristive synapse between Hindmarsh-Rose neurons in the presence of noise. It is claimed that synaptic coupling can be enhanced adaptively due to energy diversity, and the coupling intensity is increased to a saturation value until two neurons reach certain energy balance. Two identical neurons can reach perfect synchronization when electric synapse coupling is further increased. This scheme is also considered in a chain neural network and uniform noise is applied on all neurons. However, reaching synchronization becomes difficult for neurons in presenting spiking, bursting, and chaotic and periodic patterns, even when the local energy balance is corrupted to continue further growth of the coupling intensity. In the presence of noise, energy diversity becomes uncertain because of spatial diversity in excitability, and development of regular patterns is blocked. The similar scheme is used to control the growth of memristive synapse for neurons, and the synchronization stability and pattern formation are controlled by the energy diversity among neurons effectively. These results provide possible guidance for knowing the biophysical mechanism for synapse growth and energy flow can be applied to control the synchronous patterns between neurons.

Dominating lengthscales of zebrafish collective behaviour

Collective behaviour in living systems is observed across many scales, from bacteria to insects, to fish shoals. Zebrafish have emerged as a model system amenable to laboratory study. Here we report a three-dimensional study of the collective dynamics of fifty zebrafish. We observed the emergence of collective behaviour changing between ordered to randomised, upon adaptation to new environmental conditions. We quantify the spatial and temporal correlation functions of the fish and identify two length scales, the persistence length and the nearest neighbour distance, that capture the essence of the behavioural changes. The ratio of the two length scales correlates robustly with the polarisation of collective motion that we explain with a reductionist model of self-propelled particles with alignment interactions.

Using a robotic platform to study the influence of relative tailbeat phase on the energetic costs of side-by-side swimming in fish

A potential benefit of swimming together in coordinated schools is to allow fish to extract energy from vortices shed by their neighbours, thus reducing the costs of locomotion. This hypothesis has been very hard to test in real fish schools, and it has proven very difficult to replicate the complex hydrodynamics at relevant Reynolds numbers using computational simulations. A complementary approach, and the one we adopt here, is to develop and analyse the performance of biomimetic autonomous robotic models that capture the salient kinematics of fish-like swimming, and also interact via hydrodynamic forces. We developed bio-inspired robotic fish which perform sub-carangiform locomotion, and measured the speed and power consumption of robots when swimming in isolation and when swimming side-by-side in pairs. We found that swimming side-by-side confers a substantial increase in both the speed and efficiency of locomotion of both fish regardless of the relative phase relationship of their body undulations. However, we also find that each individual can slightly increase their own power efficiency if they change relative tailbeat phase by approximately 0.25π with respect to, and at the energetic expense of, their neighbour. This suggests the possibility of a competitive game-theoretic dynamic between individuals in swimming groups. Our results also demonstrate the potential applicability of our platform, and provide a natural connection between the biology and robotics of collective motion.

Under stress conditions, pacu Piaractus mesopotamicus modulates the metabolic allostatic load even after Dolops carvalhoi challenge to maintain self-protection mechanisms

Fish metabolic allostatic dynamics, when animal present physiological modifications that can be strategies to survive, are important for promoting changes to ensure whole body self-protection and survival in chronic states of stress. To determine the impact of sequential stressors on pacu (Piaractus mesopotamicus), fish were subjected to two trials of stressful treatments, administration of exogenous dietary cortisol, and parasite challenge. The first experiment consisted of a two-day acute stress trial and the second, an eight-day chronic stress trial, and after both experiments, fish parasite susceptibility was assessed with the ectoparasite Dolops carvalhoi challenge. Physiological changes in response to acute trial were observed in glycogen, cortisol, glucose, osmolarity, sodium, calcium, chloride, potassium, hematocrit, hemoglobin, red blood cells and mean corpuscular volume, and white blood cell (P < 0.05), whereas response to chronic trial were observed in glycogen, osmolarity, potassium, calcium, chloride, mean corpuscular volume, white blood cell, neutrophil, and lymphocyte (P < 0.05). Acute trials caused physiological changes, however those changes did not induce the consumption of hepatic glycogen. Chronic stress caused physiological changes that induced hepatic glycogen consumption. Under acute trial, stress experience was important to fish to achieve homeostasis after chronic stress. Changes were important to modulate the response to stressor, improve body health status, and overcome the extra stressor with D. carvalhoi challenge. The experiments demonstrate that pacu initiate strategic self-protective metabolic dynamics in acute states of stress that ensure the maintenance of important life processes in front of sequential stressors.

Path following control for towing system of cylindrical drilling platform in presence of disturbances and uncertainties

Towing is a critical process to deploy a cylindrical drilling platform. However, the towing process faces a great variety of risks from a complex nautical environment, the dynamics in towing and maneuvering, to unexpected events. Therefore, safely navigating the towing system following a planned route to a target sea area is essential. To tackle the time-varying disturbances induced by wind, current and system parametric uncertainties, a path following control method for a towing system of cylindrical drilling platform is designed based on linear active disturbance rejection control. By utilizing Maneuvering Modeling Group model as well as a catenary model, we develop a three degree-of-freedom dynamic mathematical model of the towing system under external environmental disturbances and internal uncertainties. Furthermore, we design a linear active disturbance rejection control path following controller for real-time tracking error correction based on a guidance method combining cross-track error and parallax. Finally, the path following performance of the towing system is evaluated in a simulation environment under various disturbances and internal uncertainties, where the corresponding tracking error is analyzed. The results show that the linear active disturbance rejection control performs well under both the external disturbance and inherent uncertainties, and better satisfy the tracking performance criteria than a traditional proportional-integral-derivative controller.

Reconstructing commuters network using machine learning and urban indicators

Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-inspired models have been proposed to quantify the number of people migrating from one city to the other. Despite the advances made by these models, our ability to predict the number of commuters and reconstruct mobility networks remains limited. Here, we propose an alternative approach using machine learning and 22 urban indicators to predict the flow of people and reconstruct the intercity commuters network. Our results reveal that predictions based on machine learning algorithms and urban indicators can reconstruct the commuters network with 90.4% of accuracy and describe 77.6% of the variance observed in the flow of people between cities. We also identify essential features to recover the network structure and the urban indicators mostly related to commuting patterns. As previously reported, distance plays a significant role in commuting, but other indicators, such as Gross Domestic Product (GDP) and unemployment rate, are also driven-forces for people to commute. We believe that our results shed new lights on the modeling of migration and reinforce the role of urban indicators on commuting patterns. Also, because link-prediction and network reconstruction are still open challenges in network science, our results have implications in other areas, like economics, social sciences, and biology, where node attributes can give us information about the existence of links connecting entities in the network.

How vision governs the collective behaviour of dense cycling pelotons

In densely packed groups demonstrating collective behaviour, such as bird flocks, fish schools or packs of bicycle racers (cycling pelotons), information propagates over a network, with individuals sensing and reacting to stimuli over relatively short space and time scales. What remains elusive is a robust, mechanistic understanding of how sensory system properties affect interactions, information propagation and emergent behaviour. Here, we show through direct observation how the spatio-temporal limits of the human visual sensory system govern local interactions and set the network structure in large, dense collections of cyclists. We found that cyclists align in patterns within a ± 30° arc corresponding to the human near-peripheral visual field, in order to safely accommodate motion perturbations. Furthermore, the group structure changes near the end of the race, suggesting a narrowing of the used field of vision. This change is consistent with established theory in psychology linking increased physical exertion to the decreased field of perception. Our results show how vision, modulated by arousal-dependent neurological effects, sets the local arrangement of cyclists, the mechanisms of interaction and the implicit communication across the group. We furthermore describe information propagation phenomena with an analogous elastic solid mechanics model. We anticipate our mechanistic description will enable a more detailed understanding of the interaction principles for collective behaviour in a variety of animals.

Social flocculation in plant-animal worms

Individual animals can often move more safely or more efficiently as members of a group. This can be as simple as safety in numbers or as sophisticated as aerodynamic or hydrodynamic cooperation. Here, we show that individual plant-animal worms (Symsagittifera roscoffensis) can move to safety more quickly through flocculation. Flocs form in response to turbulence that might otherwise carry these beach-dwelling worms out to sea. They allow the worms to descend much more quickly to the safety of the substrate than single worms could swim. Descent speed increases with floc size such that larger flocs can catch up with smaller ones and engulf them to become even larger and faster. To our knowledge, this is the first demonstration of social flocculation in a wild, multicellular organism. It is also remarkable that such effective flocculation occurs where the components are comparatively large multicellular organisms organized as entangled ensembles.

Cell pelotons: A model of early evolutionary cell sorting, with application to slime mold Dictyostelium discoideum

A theoretical model is presented for early evolutionary cell sorting within cellular aggregates. The model involves an energy-saving mechanism and principles of collective self-organization analogous to those observed in bicycle pelotons (groups of cyclists). The theoretical framework is applied to slime-mold slugs (Dictyostelium discoideum) and incorporated into a computer simulation which demonstrates principally the sorting of cells between the anterior and posterior slug regions. The simulation relies on an existing simulation of bicycle peloton dynamics which is modified to incorporate a limited range of cell metabolic capacities among heterogeneous cells, along with a tunable energy-expenditure parameter, referred to as an "output-level" or "starvation-level" to reflect diminishing energetic supply. Proto-cellular dynamics are modeled for three output phases: "active", "suffering", and "dying or dead." Adjusting the starvation parameter causes cell differentiation and sorting into sub-groups within the cellular aggregate. Tuning of the starvation parameter demonstrates how weak or expired cells shuffle backward within the cellular aggregate.

Does communication help people coordinate?

Theoretical and experimental investigations have consistently demonstrated that collective performance in a variety of tasks can be significantly improved by allowing communication. We present the results of the first experiment systematically investigating the value of communication in networked consensus. The goal of all tasks in our experiments is for subjects to reach global consensus, even though nodes can only observe choices of their immediate neighbors. Unlike previous networked consensus tasks, our experiments allow subjects to communicate either with their immediate neighbors (locally) or with the entire network (globally). Moreover, we consider treatments in which essentially arbitrary messages can be sent, as well as those in which only one type of message is allowed, informing others about a node’s local state. We find that local communication adds minimal value: fraction of games solved is essentially identical to treatments with no communication. Ability to communicate globally, in contrast, offers a significant performance improvement. In addition, we find that constraining people to only exchange messages about local state is significantly better than unconstrained communication. We observe that individual behavior is qualitatively consistent across settings: people clearly react to messages they receive in all communication settings. However, we find that messages received in local communication treatments are relatively uninformative, whereas global communication offers substantial information advantage. Exploring mixed communication settings, in which only a subset of agents are global communicators, we find that a significant number of global communicators is needed for performance to approach success when everyone communicates globally. However, global communicators have a significant advantage: a small tightly connected minority of globally communicating nodes can successfully steer outcomes towards their preferences, although this can be significantly mitigated when all other nodes have the ability to communicate locally with their neighbors.