Ant groups optimally amplify the effect of transiently informed individuals

Bubble-Inspired Multifunctional Magnetic Microrobots for Integrated Multidimensional Targeted Biosensing

Microrobots possessing multifunctional integration are desired for therapeutics and biomedicine applications. However, existing microrobots with desired functionalities need to be fabricated through complex procedures due to their constrained volume, limited manufacturing processes, and lack of effective in vivo observation methods. Inspired by bubbles exhibiting various abilities, we report magnetic air bubble microrobots with simpler structures to simultaneously integrate multiple functions, including microcargo delivery, multimode locomotion, imaging, and biosensing. Contributed by buoyancy and magnetic actuation to overcome obstacles, flexible three-dimensional locomotion is implemented, guaranteeing the integrity of micro-objects adsorbed on the surface of the air bubble microrobot. Introducing air microbubbles enhances the ultrasound imaging capability of microrobots in the vascular system of mice in vivo, facilitating ample medical applications. Moreover, air-liquid reactions endow microrobots with rapid pH biosensing. This work provides a unique strategy to utilize relatively simple air bubbles to achieve the complex functions of microrobots for biomedical applications.

How synchronized human networks escape local minima

Finding the global minimum in complex networks while avoiding local minima is challenging in many types of networks. In human networks and communities, adapting and finding new stable states amid changing conditions due to conflicts, climate changes, or disasters, is crucial. We studied the dynamics of complex networks of violin players and observed that such human networks have different methods to avoid local minima than other non-human networks. Humans can change the coupling strength between them or change their tempo. This leads to different dynamics than other networks and makes human networks more robust and better resilient against perturbations. We observed high-order vortex states, oscillation death, and amplitude death, due to the unique dynamics of the network. This research may have implications in politics, economics, pandemic control, decision-making, and predicting the dynamics of networks with artificial intelligence.

Femtosecond laser writing of ant-inspired reconfigurable microbot collectives

Microbot collectives can cooperate to accomplish complex tasks that are difficult for a single individual. However, various force-induced microbot collectives maintained by weak magnetic, light, and electric fields still face challenges such as unstable connections, the need for a continuous external stimuli source, and imprecise individual control. Here, we construct magnetic and light-driven ant microbot collectives capable of reconfiguring multiple assembled architectures with robustness. This methodology utilizes a flexible two-photon polymerization strategy to fabricate microbots consisting of magnetic photoresist, hydrogel, and metal nanoparticles. Under the cooperation of magnetic and light fields, the microbots can reversibly and selectively assemble (e.g., 90° assembly and 180° assembly) into various morphologies. Moreover, we demonstrate the ability of assembled microbots to cross a one-body-length gap and their adaptive capability to move through a constriction and transport microcargo. Our strategy will broaden the abilities of clustered microbots, including gap traversal, micro-object manipulation, and drug delivery.

Leaderless consensus decision-making determines cooperative transport direction in weaver ants

Animal groups need to achieve and maintain consensus to minimize conflict among individuals and prevent group fragmentation. An excellent example of a consensus challenge is cooperative transport, where multiple individuals cooperate to move a large item together. This behaviour, regularly displayed by ants and humans only, requires individuals to agree on which direction to move in. Unlike humans, ants cannot use verbal communication but most likely rely on private information and/or mechanical forces sensed through the carried item to coordinate their behaviour. Here, we investigated how groups of weaver ants achieve consensus during cooperative transport using a tethered-object protocol, where ants had to transport a prey item that was tethered in place with a thin string. This protocol allows the decoupling of the movement of informed ants from that of uninformed individuals. We showed that weaver ants pool together the opinions of all group members to increase their navigational accuracy. We confirmed this result using a symmetry-breaking task, in which we challenged ants with navigating an open-ended corridor. Weaver ants are the first reported ant species to use a 'wisdom-of-the-crowd' strategy for cooperative transport, demonstrating that consensus mechanisms may differ according to the ecology of each species.

Spatiotemporal organization of ant foraging from a complex systems perspective

We use complex systems science to explore the emergent behavioral patterns that typify eusocial species, using collective ant foraging as a paradigmatic example. Our particular aim is to provide a methodology to quantify how the collective orchestration of foraging provides functional advantages to ant colonies. For this, we combine (i) a purpose-built experimental arena replicating ant foraging across realistic spatial and temporal scales, and (ii) a set of analytical tools, grounded in information theory and spin-glass approaches, to explore the resulting data. This combined approach yields computational replicas of the colonies; these are high-dimensional models that store the experimental foraging patterns through a training process, and are then able to generate statistically similar patterns, in an analogous way to machine learning tools. These in silico models are then used to explore the colony performance under different resource availability scenarios. Our findings highlight how replicas of the colonies trained under constant and predictable experimental food conditions exhibit heightened foraging efficiencies, manifested in reduced times for food discovery and gathering, and accelerated transmission of information under similar conditions. However, these same replicas demonstrate a lack of resilience when faced with new foraging conditions. Conversely, replicas of colonies trained under fluctuating and uncertain food conditions reveal lower efficiencies at specific environments but increased resilience to shifts in food location.

Multiple Magneto-Optical Microrobotic Collectives with Selective Control in Three Dimensions Under Water

Inspired by natural swarms, various methods are developed to create artificial magnetic microrobotic collectives. However, these magnetic collectives typically receive identical control inputs from a common external magnetic field, limiting their ability to operate independently. And they often rely on interfaces or boundaries for controlled movement, posing challenges for independent, three-dimensional(3D) navigation of multiple magnetic collectives. To address this challenge, self-assembled microrobotic collectives are proposed that can be selectively actuated in a combination of external magnetic and optical fields. By harnessing both actuation methods, the constraints of single actuation approaches are overcome. The magnetic field excites the self-assembly of colloids and maintains the self-assembled microrobotic collectives without disassembly, while the optical field drives selected microrobotic collectives to perform different tasks. The proposed magnetic-photo microrobotic collectives can achieve independent position and path control in the two-dimensional (2D) plane and 3D space. With this selective control strategy, the microrobotic collectives can cooperate in convection and mixing the dye in a confined space. The results present a systematic approach for realizing selective control of multiple microrobotic collectives, which can address multitasking requirements in complex environments.

Weak Ion-Exchange Based Magnetic Swarm for Targeted Drug Delivery and Chemotherapy

Swimming microrobots that are actuated by multiple stimuli/fields display various intriguing collective behaviors, ranging from phase separation to clustering and giant number fluctuation; however, it is still chanllenging to achieve multiple responses and functionalities within one colloidal system to emulate high environmental adaptability and improved tasking capability of natural swarms. In this work, a weak ion-exchange based swarm is presented that can self-organize and reconfigure by chemical, light, and magnetic fields, showing living crystal, amorphous glass, liquid, chain, and wheel-like structures. By changing the frequency and strength of the rotating magnetic field, various well-controlled and fast transformations are obtained. Experiments show the high adaptability and functionality of the microrobot swarm in delivering drugs in confined spaces, such as narrow channels with turns or obstacles. The drug-carrying swarm exhibits excellent chemtherapy for Hela and CT26 cells due to the pH-enhanced drug release and locomotion. This reconfigurable microswarm provides a new platform for biomedical and environmental applications.

Motor cognition in plants: from thought to real experiments

Motor cognition involves the process of planning and executing goal-directed movements and recognizing, anticipating, and interpreting others' actions. Motor cognitive functions are generally associated with the presence of a brain and are ascribed only to humans and other animal species. A growing body of evidence suggests that aneural organisms, like climbing plants, exhibit behaviors driven by the intention to achieve goals, challenging our understanding of cognition. Here, we propose an inclusive perspective under motor cognition to explain climbing plants' behavior. We will first review our empirical research based on kinematical analysis to understand movement in pea plants. Then, we situate this empirical research within the current theoretical debate aimed at extending the principles of cognition to aneural organisms. A novel comparative perspective that considers the perception-action cycle, involving transforming perceived environmental elements into intended movement patterns, is provided.

'United we stand, divided we fall': intertwining as evidence of joint actions in pea plants

In life, it is common for almost every kind of organism to interact with one another. In the human realm, such interactions are at the basis of joint actions, when two or more agents syntonize their actions to achieve a common goal. Shared intentionality is the theoretical construct referring to the suite of abilities that enable such coordinated and collaborative interactions. While shared intentionality has become an important concept in research on social cognition, there is controversy surrounding its evolutionary origins. An aspect still unexplored but promising to bring new insights into this open debate is the study of aneural organisms. To fill this gap, here we investigate whether climbing plants can act jointly to achieve a common goal, i.e. reaching the light. We examined Pisum Sativum plants growing intertwined when there is a need to climb but a potential support is not present in the environment. Three-dimensional kinematic analysis of their movement revealed a coordinated and complementary behaviour. They tend to coordinate their movement in time and space to achieve a joint climbing. By deliberately extending the context in which a joint action takes place, we pay tribute to the complex nature of this social phenomenon. The next challenge for the field of joint action is to generate a perspective that links coordination mechanisms to an evolutionary framework across taxa.

Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector

Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS.

Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions

A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.

Mean-shift exploration in shape assembly of robot swarms

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms. Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea is realized by adapting the mean-shift algorithm, which is an optimization technique widely used in machine learning for locating the maxima of a density function. The proposed strategy empowers robot swarms to assemble highly complex shapes with strong adaptability, as verified by experiments with swarms of 50 ground robots. The comparison between the proposed strategy and the state-of-the-art demonstrates its high efficiency especially for large-scale swarms. The proposed strategy can also be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

Conformity in mate choice, the overlooked social component of animal and human culture

Although conformity as a major driver for human cultural evolution is a well-accepted and intensely studied phenomenon, its importance for non-human animal culture has been largely overlooked until recently. This limited for decades the possibility of studying the roots of human culture. Here, we provide a historical review of the study of conformity in both humans and non-human animals. We identify gaps in knowledge and propose an evolutionary route towards the sophisticated cultural processes that characterize humanity. A landmark in the study of conformity is Solomon Asch's famous experiment on humans in 1955. By contrast, interest in conformity among evolutionary biologists has only become salient since the turn of the new millennium. A striking result of our review is that, although studies of conformity have examined many biological contexts, only one looked at mate choice. This is surprising because mate choice is probably the only context in which conformity has self-reinforcing advantages across generations. Within a metapopulation, i.e. a group of subpopulations connected by dispersing individuals, dispersers able to conform to the local preference for a given type of mate have a strong and multigenerational fitness advantage. This is because once females within one subpopulation locally show a bias for one type of males, immigrant females who do not conform to the local trend have sons, grandsons, etc. of the non-preferred phenotype, which negatively and cumulatively affects fitness over generations in a process reminiscent of the Fisher runaway process. This led us to suggest a sex-driven origin of conformity, indicating a possible evolutionary route towards animal and human culture that is rooted in the basic, and thus ancient, social constraints acting on mating preferences within a metapopulation. In a generic model, we show that dispersal among subpopulations within a metapopulation can effectively maintain independent Fisher runaway processes within subpopulations, while favouring the evolution of social learning and conformity at the metapopulation scale; both being essential for the evolution of long-lasting local traditions. The proposed evolutionary route to social learning and conformity casts surprising light on one of the major processes that much later participated in making us human. We further highlight several research avenues to define the spectrum of conformity better, and to account for its complexity. Future studies of conformity should incorporate experimental manipulation of group majority. We also encourage the study of potential links between conformity and mate copying, animal aggregations, and collective actions. Moreover, validation of the sex-driven origin of conformity will rest on the capacity of human and evolutionary sciences to investigate jointly the origin of social learning and conformity. This constitutes a stimulating common agenda and militates for a rapprochement between these two currently largely independent research areas.

Light-Powered, Fuel-Free Oscillation, Migration, and Reversible Manipulation of Multiple Cargo Types by Micromotor Swarms

Through experiments and simulations, we show that fuel-free photoactive TiO₂ microparticles can form mobile, coherent swarms in the presence of UV light, which track the subsequent movement of an irradiated spot in a fluid-filled microchamber. Multiple concurrent propulsion mechanisms (electrolyte diffusioosmotic swarming, photocatalytic expansion, and photothermal migration) control the rich collective behavior of the swarms, which provide a strategy to reversely manipulate cargo. The active swarms can autonomously pick up groups of inert particles, sort them by size, and sequentially release the sorted particles at particular locations in the microchamber. Hence, these swarms overcome three obstacles, limiting the utility of self-propelled particles. Namely, they can (1) undergo directed, long-range migration without the addition of a chemical fuel, (2) perform diverse collective behavior not possible with a single active particle, and (3) repeatedly and controllably isolate and deliver specific components of a multiparticle "cargo". Since light sources are easily fabricated, transported, and controlled, the results can facilitate the development of portable devices, providing broader access to the diagnostic and manufacturing advances enabled by microfluidics.

Evidence of Critical Dynamics in Movements of Bees inside a Hive

Social insects such as honey bees exhibit complex behavioral patterns, and their distributed behavioral coordination enables decision-making at the colony level. It has, therefore, been proposed that a high-level description of their collective behavior might share commonalities with the dynamics of neural processes in brains. Here, we investigated this proposal by focusing on the possibility that brains are poised at the edge of a critical phase transition and that such a state is enabling increased computational power and adaptability. We applied mathematical tools developed in computational neuroscience to a dataset of bee movement trajectories that were recorded within the hive during the course of many days. We found that certain characteristics of the activity of the bee hive system are consistent with the Ising model when it operates at a critical temperature, and that the system's behavioral dynamics share features with the human brain in the resting state.

Topological defect-mediated morphodynamics of active-active interfaces

Physical interfaces widely exist in nature and engineering. Although the formation of passive interfaces is well elucidated, the physical principles governing active interfaces remain largely unknown. Here, we combine simulation, theory, and cell-based experiment to investigate the evolution of an active-active interface. We adopt a biphasic framework of active nematic liquid crystals. We find that long-lived topological defects mechanically energized by activity display unanticipated dynamics nearby the interface, where defects perform "U-turns" to keep away from the interface, push the interface to develop local fingers, or penetrate the interface to enter the opposite phase, driving interfacial morphogenesis and cross-interface defect transport. We identify that the emergent interfacial morphodynamics stems from the instability of the interface and is further driven by the activity-dependent defect-interface interactions. Experiments of interacting multicellular monolayers with extensile and contractile differences in cell activity have confirmed our predictions. These findings reveal a crucial role of topological defects in active-active interfaces during, for example, boundary formation and tissue competition that underlie organogenesis and clinically relevant disorders.

Strength-mass scaling law governs mass distribution inside honey bee swarms

To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000-10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the [Formula: see text] power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.

Collective Behaviors of Magnetic Microparticle Swarms: From Dexterous Tentacles to Reconfigurable Carpets

Microrobot swarms have promising prospects in biomedical applications ranging from targeted cargo delivery to minimally invasive surgery. However, such potential is constrained by the small output force and low efficiency of the current microrobot swarms. To address this challenge, we report a tentacle-like reconfigurable microrobot swarm by programming paramagnetic microparticles into reconfigurable carpets with numerous cilia. This wirelessly controlled microrobot swarm is constructed via a strong gradient magnetic field in combination with a programmable oscillating magnetic field. The gradient magnetic field is supplied by a permanent magnet, which enables fast formation of a microrobot swarm with powerful collective behaviors via cooperative physical structures within the swarm. The oscillating magnetic field is produced by a custom-built electromagnetic coil system, which is adopted as an actuation device for conducting dexterous manipulation via controllable oscillation motion. Using the proposed microrobot swarming strategy, a milligram-level magnetic carpet achieves a millinewton-level output force. By applying different types of magnetic fields, the magnetic carpet accomplishes dexterous manipulation tasks, lesion removal, and controllable drug diffusion with a high-efficiency response in microscale executions. The formation and control mechanisms of the microrobot swarm reported here provide a practical candidate for in vivo biomedical treatment.

Synergistic Integration and Pharmacomechanical Function of Enzyme-Magnetite Nanoparticle Swarms for Low-Dose Fast Thrombolysis

Magnetic micro-/nanoparticles are extensively explored over the past decade as active diagnostic/therapeutic agents for minimally invasive medicine. However, sufficient function integration on these miniaturized bodies toward practical applications remains challenging. This work proposes a synergistic strategy via integrating particle functionalization and bioinspired swarming, demonstrated by recombinant tissue plasminogen activator modified magnetite nanoparticles (rtPA-Fe₃ O₄ NPs) for fast thrombolysis in vivo with low drug dosage. The synthesized rtPA-Fe₃ O₄ NPs exhibit superior magnetic performance, high biocompatibility, and thrombolytic enzyme activity. Benefiting from a customized magnetic operation system designed for animal experiments and preclinical development, these agglomeration-free NPs can assemble into micro-/milli-scale swarms capable of robust maneuver and reconfigurable transformation for on-demand tasks in complex biofluids. Specifically, the spinning mode of the swarm exerts focused fluid shear stresses while rubbing on the thrombus surface, constituting a mechanical force for clot breakdown. The synergy of the NPs' inherent enzymatic effect and swarming-triggered fluid forces enables amplified efficacy of thrombolysis in an in vivo occlusion model of rabbit carotid artery, using lower drug concentration than clinical dosage. Furthermore, swarming-enhanced ultrasound signals aid in imaging-guided treatment. Therefore, the pharmacomechanical NP swarms herein represent an injectable thrombolytic tool joining advantages of intravenous drug therapy and robotic intervention.

Subcritical escape waves in schooling fish

Theoretical physics predicts optimal information processing in living systems near transitions (or pseudo-critical points) in their collective dynamics. However, focusing on potential benefits of proximity to a critical point, such as maximal sensitivity to perturbations and fast dissemination of information, commonly disregards possible costs of criticality in the noisy, dynamic environmental contexts of biological systems. Here, we find that startle cascades in fish schools are subcritical (not maximally responsive to environmental cues) and that distance to criticality decreases when perceived risk increases. Considering individuals' costs related to two detection error types, associated to both true and false alarms, we argue that being subcritical, and modulating distance to criticality, can be understood as managing a trade-off between sensitivity and robustness according to the riskiness and noisiness of the environment. Our work emphasizes the need for an individual-based and context-dependent perspective on criticality and collective information processing and motivates future questions about the evolutionary forces that brought about a particular trade-off.

Neural Synchrony and Network Dynamics in Social Interaction: A Hyper-Brain Cell Assembly Hypothesis

Mounting neurophysiological evidence suggests that interpersonal interaction relies on continual communication between cell assemblies within interacting brains and continual adjustments of these neuronal dynamic states between the brains. In this Hypothesis and Theory article, a Hyper-Brain Cell Assembly Hypothesis is suggested on the basis of a conceptual review of neural synchrony and network dynamics and their roles in emerging cell assemblies within the interacting brains. The proposed hypothesis states that such cell assemblies can emerge not only within, but also between the interacting brains. More precisely, the hyper-brain cell assembly encompasses and integrates oscillatory activity within and between brains, and represents a common hyper-brain unit, which has a certain relation to social behavior and interaction. Hyper-brain modules or communities, comprising nodes across two or several brains, are considered as one of the possible representations of the hypothesized hyper-brain cell assemblies, which can also have a multidimensional or multilayer structure. It is concluded that the neuronal dynamics during interpersonal interaction is brain-wide, i.e., it is based on common neuronal activity of several brains or, more generally, of the coupled physiological systems including brains.

Vector-Controlled Wheel-Like Magnetic Swarms With Multimodal Locomotion and Reconfigurable Capabilities

Inspired by the biological collective behaviors of nature, artificial microrobotic swarms have exhibited environmental adaptability and tasking capabilities for biomedicine and micromanipulation. Complex environments are extremely relevant to the applications of microswarms, which are expected to travel in blood vessels, reproductive and digestive tracts, and microfluidic chips. Here we present a strategy that reconfigures paramagnetic nanoparticles into a vector-controlled microswarm with 3D collective motions by programming sawtooth magnetic fields. Horizontal swarms can be manipulated to stand vertically and swim like a wheel by adjusting the direction of magnetic-field plane. Compared with horizontal swarms, vertical wheel-like swarms were evaluated to be of approximately 15-fold speed increase and enhanced maneuverability, which was exhibited by striding across complex 3D confinements. Based on analysis of collective behavior of magnetic particles in flow field using molecular dynamics methods, a rotary stepping mechanism was proposed to address the formation and locomotion mechanisms of wheel-like swarm. we present a strategy that actuates swarms to stand and hover in situ under a programming swing magnetic fields, which provides suitable solutions to travel across confined space with unexpected changes, such as stepped pipes. By biomimetic design from fin motion of fish, wheel-like swarms were endowed with multi-modal locomotion and load-carrying capabilities. This design of intelligent microswarms that adapt to complicated biological environments can promote the applications ranging from the construction of smart and multifunctional materials to biomedical engineering.

Cooperative cargo transportation by a swarm of molecular machines

Cooperation is a strategy that has been adopted by groups of organisms to execute complex tasks more efficiently than single entities. Cooperation increases the robustness and flexibility of the working groups and permits sharing of the workload among individuals. However, the utilization of this strategy in artificial systems at the molecular level, which could enable substantial advances in microrobotics and nanotechnology, remains highly challenging. Here, we demonstrate molecular transportation through the cooperative action of a large number of artificial molecular machines, photoresponsive DNA-conjugated microtubules driven by kinesin motor proteins. Mechanical communication via conjugated photoresponsive DNA enables these microtubules to organize into groups upon photoirradiation. The groups of transporters load and transport cargo, and cargo unloading is achieved by dissociating the groups into single microtubules. The group formation permits the loading and transport of cargoes with larger sizes and in larger numbers over long distances compared with single transporters. We also demonstrate that cargo can be collected at user-determined locations defined by ultraviolet light exposure. This work demonstrates cooperative task performance by molecular machines, which will help to construct molecular robots with advanced functionalities in the future.

Noise resistant synchronization and collective rhythm switching in a model of animal group locomotion

Biology is suffused with rhythmic behaviour, and interacting biological oscillators often synchronize their rhythms with one another. Colonies of some ant species are able to synchronize their activity to fall into coherent bursts, but models of this phenomenon have neglected the potential effects of intrinsic noise and interspecific differences in individual-level behaviour. We investigated the individual and collective activity patterns of two Leptothorax ant species. We show that in one species (Leptothorax sp. W), ants converge onto rhythmic cycles of synchronized collective activity with a period of about 20 min. A second species (Leptothorax crassipilis) exhibits more complex collective dynamics, where dominant collective cycle periods range from 16 min to 2.8 h. Recordings that last 35 h reveal that, in both species, the same colony can exhibit multiple oscillation frequencies. We observe that workers of both species can be stimulated by nest-mates to become active after a refractory resting period, but the durations of refractory periods differ between the species and can be highly variable. We model the emergence of synchronized rhythms using an agent-based model informed by our empirical data. This simple model successfully generates synchronized group oscillations despite the addition of noise to ants' refractory periods. We also find that adding noise reduces the likelihood that the model will spontaneously switch between distinct collective cycle frequencies.

Cooperation in a fluid swarm of fuel-free micro-swimmers

While motile bacteria display rich dynamics in dense colonies, the phoretic nature of artificial micro-swimmers restricts their activity when crowded. Here we introduce a new class of synthetic micro-swimmers that are driven solely by light. By coupling a light absorbing particle to a fluid droplet we produce a colloidal chimera that transforms optical power into propulsive thermo-capillary action. The swimmers' internal drive allows them to operate for a long duration (days) and remain active when crowded, forming a high density fluid phase. We find that above a critical concentration, swimmers form a long lived crowded state that displays internal dynamics. When passive particles are introduced, the dense swimmer phase can re-arrange to spontaneously corral the passive particles. We derive a geometrical, depletion-like condition for corralling by identifying the role the passive particles play in controlling the effective concentration of the micro-swimmers.

Naïve individuals promote collective exploration in homing pigeons

Group-living animals that rely on stable foraging or migratory routes can develop behavioural traditions to pass route information down to inexperienced individuals. Striking a balance between exploitation of social information and exploration for better alternatives is essential to prevent the spread of maladaptive traditions. We investigated this balance during cumulative route development in the homing pigeon Columba livia. We quantified information transfer within pairs of birds in a transmission-chain experiment and determined how birds with different levels of experience contributed to the exploration-exploitation trade-off. Newly introduced naïve individuals were initially more likely to initiate exploration than experienced birds, but the pair soon settled into a pattern of alternating leadership with both birds contributing equally. Experimental pairs showed an oscillating pattern of exploration over generations that might facilitate the discovery of more efficient routes. Our results introduce a new perspective on the roles of leadership and information pooling in the context of collective learning.

The geometry of decision-making in individuals and collectives

Almost all animals must make decisions on the move. Here, employing an approach that integrates theory and high-throughput experiments (using state-of-the-art virtual reality), we reveal that there exist fundamental geometrical principles that result from the inherent interplay between movement and organisms’ internal representation of space. Specifically, we find that animals spontaneously reduce the world into a series of sequential binary decisions, a response that facilitates effective decision-making and is robust both to the number of options available and to context, such as whether options are static (e.g., refuges) or mobile (e.g., other animals). We present evidence that these same principles, hitherto overlooked, apply across scales of biological organization, from individual to collective decision-making.

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt “critical” transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

Reconfigurable Disk-like Microswarm under a Sawtooth Magnetic Field

Swarming robotic systems, which stem from insect swarms in nature, exhibit a high level of environmental adaptability and enhanced tasking capabilities for targeted delivery and micromanipulation. Here, we present a strategy that reconfigures paramagnetic nanoparticles into microswarms energized by a sawtooth magnetic field. A rotary-stepping magnetic-chain mechanism is proposed to address the forming principle of disk-like swarms. Based on programming the sawtooth field, the microswarm can perform reversible transformations between a disk, an ellipse and a ribbon, as well as splitting and merging. In addition, the swarms can be steered in any direction with excellent maneuverability and a high level of pattern stability. Under accurate manipulation of a magnetic microswarm, multiple microparts with complicated shapes were successfully combined into a complete assembly. This reconfigurable swarming microrobot may shed light on the understanding of complex morphological transformations in living systems and provide future practical applications of microfabrication and micromanipulation.

Bio-inspired strategies for next-generation perovskite solar mobile power sources

Smart electronic devices are becoming ubiquitous due to many appealing attributes including portability, long operational time, rechargeability and compatibility with the user-desired form factor. Integration of mobile power sources (MPS) based on photovoltaic technologies with smart electronics will continue to drive improved sustainability and independence. With high efficiency, low cost, flexibility and lightweight features, halide perovskite photovoltaics have become promising candidates for MPS. Realization of these photovoltaic MPS (PV-MPS) with unconventionally extraordinary attributes requires new 'out-of-box' designs. Natural materials have provided promising designing solutions to engineer properties under a broad range of boundary conditions, ranging from molecules, proteins, cells, tissues, apparatus to systems in animals, plants, and humans optimized through billions of years of evolution. Applying bio-inspired strategies in PV-MPS could be biomolecular modification on crystallization at the atomic/meso-scale, bio-structural duplication at the device/system level and bio-mimicking at the functional level to render efficient charge delivery, energy transport/utilization, as well as stronger resistance against environmental stimuli (e.g., self-healing and self-cleaning). In this review, we discuss the bio-inspired/-mimetic structures, experimental models, and working principles, with the goal of revealing physics and bio-microstructures relevant for PV-MPS. Here the emphasis is on identifying the strategies and material designs towards improvement of the performance of emerging halide perovskite PVs and strategizing their bridge to future MPS.

Foraging behaviours lead to spatiotemporal self-similar dynamics in grazing ecosystems

Biological behaviour‐driven self‐organized patterns have recently been confirmed to play a key role in ecosystem functioning. Here, we develop a theoretical phase‐separation model to describe spatiotemporal self‐similar dynamics, which is a consequence of behaviour‐driven trophic interactions in short‐time scales. Our framework integrates scale‐dependent feedback and density‐dependent movement into grazing ecosystems. This model derives six types of selective foraging behaviours that trigger pattern formation for top‐down grazing ecosystems, and one of which is consistent with existing foraging theories. Self‐organized patterns nucleate under moderate grazing intensity and are destroyed by overgrazing, which suggests ecosystem degradation. Theoretical results qualitatively agree with observed grazing ecosystems that display spatial heterogeneities under variable grazing intensity. Our findings potentially provide new insights into self‐organized patterns as an indicator of ecosystem transitions under a stressful environment.

Cooperative transport by flocking phototactic micromotors

Cargo delivery by micro/nanomotors provides enormous opportunities for micromanipulation, environmental cleaning, drug delivery, etc. However, due to the limited driving force, it is usually difficult for single micro/nanomotors to transport cargoes much larger or heavier than themselves. Here, we demonstrate that flocking phototactic TiO2 micromotors can cooperatively transport multiple and different types of large cargoes based on light-responsive diffusiophoresis. Utilizing spontaneous diffusiophoretic attraction, flocking TiO2 micromotors can load large cargoes. Under UV light navigation, flocking TiO2 micromotors cooperatively carry and transport cargoes via collective diffusiophoretic repulsion in open space or complex microenvironments. After reaching the destination, the carried cargoes can also be unloaded from the flock and be deployed at a predetermined destination by disassembling or reversing the flock. This study may pave the way for developing intelligent swarming micro/nanorobots for cooperative targeting micromanipulation and advancing their applications in drug delivery and microengineering.

Minimal Organizational Requirements for the Ascription of Animal Personality to Social Groups

Recently, psychological phenomena have been expanded to new domains, crisscrossing boundaries of organizational levels, with the emergence of areas such as social personality and ecosystem learning. In this contribution, we analyze the ascription of an individual-based concept (personality) to the social level. Although justified boundary crossings can boost new approaches and applications, the indiscriminate misuse of concepts refrains the growth of scientific areas. The concept of social personality is based mainly on the detection of repeated group differences across a population, in a direct transposition of personality concepts from the individual to the social level. We show that this direct transposition is problematic for avowing the nonsensical ascription of personality even to simple electronic devices. To go beyond a metaphoric use of social personality, we apply the organizational approach to a review of social insect communication networks. Our conceptual analysis shows that socially self-organized systems, such as isolated ant trails and bee's recruitment groups, are too simple to have social personality. The situation is more nuanced when measuring the collective choice between nest sites or foraging patches: some species show positive and negative feedbacks between two or more self-organized social structures so that these co-dependent structures are inter-related by second-order, social information systems, complying with a formal requirement for having social personality: the social closure of constraints. Other requirements include the decoupling between individual and social dynamics, and the self-regulation of collective decision processes. Social personality results to be sometimes a metaphorical transposition of a psychological concept to a social phenomenon. The application of this organizational approach to cases of learning ecosystems, or evolutionary learning, could help to ground theoretically the ascription of psychological properties to levels of analysis beyond the individual, up to meta-populations or ecological communities.

Collective predator evasion: Putting the criticality hypothesis to the test

According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the "criticality hypothesis", appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the "criticality hypothesis", but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.

Behavior of information flow near criticality

Recent experiments have indicated that many biological systems self-organize near their critical point, which hints at a common design principle. While it has been suggested that information transmission is optimized near the critical point, it remains unclear how information transmission depends on the dynamics of the input signal, the distance over which the information needs to be transmitted, and the distance to the critical point. Here we employ stochastic simulations of a driven two-dimensional Ising system and study the instantaneous mutual information and the information transmission rate between a driven input spin and an output spin. The instantaneous mutual information varies nonmonotonically with the temperature but increases monotonically with the correlation time of the input signal. In contrast, there exists not only an optimal temperature but also an optimal finite input correlation time that maximizes the information transmission rate. This global optimum arises from a fundamental trade-off between the need to maximize the frequency of independent input messages, the necessity to respond fast to changes in the input, and the need to respond reliably to these changes. The optimal temperature lies above the critical point but moves toward it as the distance between the input and output spin is increased.

Evolutionary advantages of turning points in human cooperative behaviour

Cooperation is crucial to overcome some of the most pressing social challenges of our times, such as the spreading of infectious diseases, corruption and environmental conservation. Yet, how cooperation emerges and persists is still a puzzle for social scientists. Since human cooperation is individually costly, cooperative attitudes should have been eliminated by natural selection in favour of selfishness. Yet, cooperation is common in human societies, so there must be some features which make it evolutionarily advantageous. Using a cognitive inspired model of human cooperation, recent work Realpe-Gómez (2018) has reported signatures of criticality in human cooperative groups. Theoretical evidence suggests that being poised at a critical point provides evolutionary advantages to groups by enhancing responsiveness of these systems to external attacks. After showing that signatures of criticality can be detected in human cooperative groups composed by Moody Conditional Cooperators, in this work we show that being poised close to a turning point enhances the fitness and make individuals more resistant to invasions by free riders.

Informational architecture across non-living and living collectives

Collective behavior is widely regarded as a hallmark property of living and intelligent systems. Yet, many examples are known of simple physical systems that are not alive, which nonetheless display collective behavior too, prompting simple physical models to often be adopted to explain living collective behaviors. To understand collective behavior as it occurs in living examples, it is important to determine whether or not there exist fundamental differences in how non-living and living systems act collectively, as well as the limits of the intuition that can be built from simpler, physical examples in explaining biological phenomenon. Here, we propose a framework for comparing non-living and living collectives as a continuum based on their information architecture: that is, how information is stored and processed across different degrees of freedom. We review diverse examples of collective phenomena, characterized from an information-theoretic perspective, and offer views on future directions for quantifying living collective behaviors based on their informational structure.

Reconfigurable magnetic microrobot swarm: Multimode transformation, locomotion, and manipulation

Swimming microrobots that are energized by external magnetic fields exhibit a variety of intriguing collective behaviors, ranging from dynamic self-organization to coherent motion; however, achieving multiple, desired collective modes within one colloidal system to emulate high environmental adaptability and enhanced tasking capabilities of natural swarms is challenging. Here, we present a strategy that uses alternating magnetic fields to program hematite colloidal particles into liquid, chain, vortex, and ribbon-like microrobotic swarms and enables fast and reversible transformations between them. The chain is characterized by passing through confined narrow channels, and the herring school-like ribbon procession is capable of large-area synchronized manipulation, whereas the colony-like vortex can aggregate at a high density toward coordinated handling of heavy loads. Using the developed discrete particle simulation methods, we investigated generation mechanisms of these four swarms, as well as the "tank-treading" motion of the chain and vortex merging. In addition, the swarms can be programmed to steer in any direction with excellent maneuverability, and the vortex's chirality can be rapidly switched with high pattern stability. This reconfigurable microrobot swarm can provide versatile collective modes to address environmental variations or multitasking requirements; it has potential to investigate fundamentals in living systems and to serve as a functional bio-microrobot system for biomedicine.

Reconfigurable Particle Swarm Robotics Powered by Acoustic Vibration Tweezer

Inspired by natural swarms such as bees and ants, various types of swarm robotic systems have been developed to work together to complete tasks that transcend individual capabilities. Autonomous robots controlled by collective algorithm and colloidal swarms energized by external field have been designed in an attempt to emulate collective behaviors in nature. However, either sophisticated hardware designs or active agents with special electromagnetic properties and microstructural designs are needed. Here, for the first time, we create a swarm robotic system that can make any granular materials an active swarm robot by acoustic vibration tweezer. It should be noted that the particles energized by only one vibration generator are ordinary sand without any microstructural design. Therefore, it is the simplest and lowest cost swarm robot. Particles can display a solid-like aggregate, which is capable of robustly carrying and transporting an object that is about 1 million times heavier than a single particle. Moreover, through the cooperation of two swarm robots, we can achieve cooperative transport of a stick with a length of 1000 times the diameter of a single particle. The particle robot can move in a fluid-like amorphous group, which can change its own shape to adapt to the surrounding environment, thus having a strong environmental adaptability. Besides, it can move quickly (about 600 times the particle diameter per second) in a discrete state. Within one certain particle system, the particle swarm robot can emulate diverse biomimetic collective behaviors through navigated locomotion, multimode transformation, and cooperative transport.

anTraX, a software package for high-throughput video tracking of color-tagged insects

Recent years have seen a surge in methods to track and analyze animal behavior. Nevertheless, tracking individuals in closely interacting, group-living organisms remains a challenge. Here, we present anTraX, an algorithm and software package for high-throughput video tracking of color-tagged insects. anTraX combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured. The use of color tags, a well-established and robust method for marking individual insects in groups, relaxes requirements for image size and quality, and makes the software broadly applicable. anTraX is readily integrated into existing tools and methods for automated image analysis of behavior to further augment its output. anTraX can handle large-scale experiments with minimal human involvement, allowing researchers to simultaneously monitor many social groups over long time periods.

Formation of stable and responsive collective states in suspensions of active colloids

Many animal species organise into disordered swarms, polarised flocks or swirls to protect from predators or optimise foraging. Previous studies suggest that such collective states are related to a critical point, which could explain their balance between robustness to noise and high responsiveness regarding external perturbations. Here we provide experimental evidence for this idea by investigating the stability of swirls formed by light-responsive active colloids which adjust their individual motion to positions and orientations of neighbours. Because their behaviour can be precisely tuned, controlled changes between different collective states can be achieved. During the transition between stable swirls and swarms we observe a maximum of the group's susceptibility indicating the vicinity of a critical point. Our results support the idea of system-independent organisation principles of collective states and provide useful strategies for the realisation of responsive yet stable ensembles in microrobotic systems.

Ant collective cognition allows for efficient navigation through disordered environments

The cognitive abilities of biological organisms only make sense in the context of their environment. Here, we study longhorn crazy ant collective navigation skills within the context of a semi-natural, randomized environment. Mapping this biological setting into the ‘Ant-in-a-Labyrinth’ framework which studies physical transport through disordered media allows us to formulate precise links between the statistics of environmental challenges and the ants’ collective navigation abilities. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the ‘Ant-in-a-Labyrinth’ problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Overall, our work demonstrates the potential advantages of group living and collective cognition in increasing a species’ habitable range.

Route reassessment by transporter ants improves speed and directional accuracy of cooperative transport in Formica japonica

Encircling cooperative transport in ants is categorized into coordinated and uncoordinated types. Coordinated cooperative transport is considered to be advantageous for ants because it transports food quickly without creating a deadlock. Contrarily, uncoordinated transport is slow and frequently becomes deadlocked. This study assessed the characteristics of uncoordinated cooperative transport, which has scarcely been studied before, through experiments performed on Formica japonica. Based on our experiment and analysis, we report that the transport speed remains unchanged with the number of transporters. We also found that pulling transporter ants often left the food item transiently as the transport speed decreased, and then went back to the item. Upon rejoining transport, the transport speed increased. This is presumably because the ants gain navigation information during the period that they leave the food. We propose that this ‘route reassessment’ behavior is important for transport coordination and navigation in F. japonica.

Effects of load mass and size on cooperative transport in ants over multiple transport challenges

Some ant species cooperatively transport a wide range of extremely large, heavy food objects of various shapes and materials. While previous studies have examined how object mass and size affect the recruitment of additional workers, less is understood about how these attributes affect the rest of the transport process. Using artificial baits with independently varying mass and size, we reveal their effects on cooperative transport in Paratrechina longicornis across two transport challenges: movement initiation and obstacle navigation. As expected, object mass was tightly correlated with number of porters as workers adjust group size to the task. Mass affected performance similarly across the two challenges, with groups carrying heavy objects having lower performance. Yet, object size had differing effects depending on the challenge. While larger objects led to reduced performance during movement initiation - groups took longer to start moving these objects and had lower velocities - there was no evidence for this during obstacle navigation, and the opposite pattern was weakly supported. If a group struggles to start moving an object, it does not necessarily predict difficulty navigating around obstacles; groups should persist in trying to move 'difficult' objects, which may be easier to transport later in the process. Additionally, groups hitting obstacles were not substantially disrupted, and started moving again sooner than at the start, despite the nest direction being blocked. Paratrechina longicornis transport groups never failed, performing well at both challenges while carrying widely varying objects, and even transported a bait weighing 1900 times the mass of an individual.

Phase Diagram and Optimal Information Use in a Collective Sensing System

We consider a population of individuals living in an uncertain environment. Individuals are able to make noisy observations of the environment and communicate using signals. We show that the model shows an order-disorder transition from an ordered phase in low communication noise in which a consensus about the environmental state is formed to a disordered phase in high communication noise in which no consensus is formed. There are different consensus states: informed consensus in which consensus on the correct belief about the environmental state is formed, and misinformed consensus in which consensus on a wrong belief is formed. Based on the consensus state reached, the ordered phase is decomposed into multistable states separated by first order transitions. We show that the inference capability of the population in a changing environment is maximized on the edge of bistability: on the border between an informed consensus phase and a bistable phase in which both informed and misinformed consensuses are stable. In addition, we show that an optimal level of noise in communication increases the responsiveness of the population to environmental changes in a resonancelike phenomenon. Furthermore, the beneficial effect of noise is the most crucial in a fast changing environment.

How bacterial cells and colonies move on solid substrates

Many bacteria rely on active cell appendages, such as type IV pili, to move over substrates and interact with neighboring cells. Here, we study the motion of individual cells and bacterial colonies, mediated by the collective interactions of multiple pili. It was shown experimentally that the substrate motility of Neisseria gonorrhoeae cells can be described as a persistent random walk with a persistence length that exceeds the mean pili length. Moreover, the persistence length increases for a higher number of pili per cell. With the help of a simple, tractable stochastic model, we test whether a tug of war without directional memory can explain the persistent motion of single Neisseria gonorrhoeae cells. While persistent motion of single cells indeed emerges naturally in the model, a tug of war alone is not capable of explaining the motility of microcolonies, which becomes weaker with increasing colony size. We suggest sliding friction between the microcolonies and the substrate as the missing ingredient. While such friction almost does not affect the general mechanism of single cell motility, it has a strong effect on colony motility. We validate the theoretical predictions by using a three-dimensional computational model that includes explicit details of the pili dynamics, force generation, and geometry of cells.

Robust collaborative object transportation using multiple MAVs

Collaborative object transportation using multiple MAV with limited communication is a challenging problem. In this paper, we address the problem of multiple MAV mechanically coupled to a bulky object for transportation purposes without explicit communication between agents. The apparent physical properties of each agent are reshaped to achieve robustly stable transportation. Parametric uncertainties and unmodeled dynamics of each agent are quantified and techniques from robust control theory are employed to choose the physical parameters of each agent to guarantee stability. Extensive simulation analysis and experimental results show that the proposed method guarantees stability in worst-case scenarios.

Using sensory discrimination in a foraging-style task to evaluate human upper-limb sensorimotor performance

Object stiffness discrimination is fundamental to shaping the way we interact with our environment. Investigating the sensorimotor mechanisms underpinning stiffness discrimination may help further our understanding of healthy and sensory-impaired upper limb function. We developed a metric that leverages sensory discrimination techniques and a foraging-based analysis to characterize participant accuracy and discrimination processes of sensorimotor control. Our metric required searching and discriminating two variants of test-object: rubber blocks and spring cells, which emphasized cutaneous-force and proprioceptive feedback, respectively. We measured the number of test-objects handled, selection accuracy, and foraging duration. These values were used to derive six indicators of performance. We observed higher discrimination accuracies, with quicker search and handling durations, for blocks compared to spring cells. Correlative analyses of accuracy, error rates, and foraging times suggested that the block and spring variants were, in fact, unique sensory tasks. These results provide evidence that our metric is sensitive to the contributions of sensory feedback, motor control, and task performance strategy, and will likely be effective in further characterizing the impact of sensory feedback on motor control in healthy and sensory-impaired populations.