Publications

Biological ensembles use collective intelligence to tackle challenges together, but suboptimal coordination can undermine the effectiveness of group cognition. Testing whether collective cognition exceeds that of the individual is often impractical since different organizational scales tend to face disjoint problems. One exception is the problem of navigating large loads through complex environments and toward a given target. People and ants stand out in their ability to efficiently perform this task not just individually but also as a group. This provides a rare opportunity to empirically compare problem-solving skills and cognitive traits across species and group sizes. Here, we challenge people and ants with the same "piano-movers" load maneuvering puzzle and show that while ants perform more efficiently in larger groups, the opposite is true for humans. We find that although individual ants cannot grasp the global nature of the puzzle, their collective motion translates into emergent cognitive skills. They encode short-term memory in their internally ordered state and this allows for enhanced group performance. People comprehend the puzzle in a way that allows them to explore a reduced search space and, on average, outperform ants. However, when communication is restricted, groups of people resort to the most obvious maneuvers to facilitate consensus. This is reminiscent of ant behavior, and negatively impacts their performance. Our results exemplify how simple minds can easily enjoy scalability while complex brains require extensive communication to cooperate efficiently.

The concept of emergence, or synergy in its simplest form, is widely used but lacks a rigorous definition. Our work connects information and set theory to uncover the mathematical nature of synergy as the failure of distributivity. For the trivial case of discrete random variables, we explore whether and how it is possible to get more information out of lesser parts. The approach is inspired by the role of set theory as the fundamental description of partwhole relations. If taken unaltered, synergistic behavior is forbidden by the set-theoretic axioms. However, random variables are not a perfect analogy of sets: we formalize the distinction, highlighting a single broken axiomunion/intersection distributivity. Nevertheless, it remains possible to describe information using Venn-type diagrams. The proposed multivariate theory resolves the persistent self-contradiction of partial information decomposition and reinstates it as a primary route toward a rigorous definition of emergence. Our results suggest that non-distributive variants of set theory may be used to describe emergent physical systems.

Animal groups need to achieve and maintain consensus to minimise 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 behavior, 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.Competing Interest StatementThe authors have declared no competing interest.

The physicality of the world in which the animal actsits anatomical structure, physiology, perception, emotional states, and cognitive capabilitiesdetermines the boundaries of the behavioral space within which the animal can operate. Behavior, therefore, can be considered as the subspace that remains after secluding all actions that are not available to the animal due to constraints. The very signature of being a certain creature is reflected in these limitations that shape its behavior. A major goal of ethology is to expose those constraints that carve the intricate structure of animal behavior and reveal both uniqueness and commonalities between animals within and across taxa. Exploratory behavior in an empty arena seems to be stochastic; nevertheless, it does not mean that the moving animal is a random walker. In this study, we present how, by adding constraints to the animals locomotion, one can gradually retain the mousiness that characterizes the behaving mouse. We then introduce a novel phenomenon of high mirror symmetry along the locomotion of mice, which highlights another constraint that further compresses the complex nature of exploratory behavior in these animals. We link these findings to a known neural mechanism that could explain this phenomenon. Finally, we suggest our novel finding and derived methods to be used in the search for commonalities in the motion trajectories of various organisms across taxa.

Some ant species are known as efficient transporters that can cooperatively carry food items which would be too large for a single ant. Previous studies of cooperative transport focused on the role of individual ants and their behavioral rules that allow for efficient coordination. However, the resulting detailed microscopic description requires a numerical treatment in order to extract macroscopic features of the object's trajectory. Here, we instead treat the carried object as a single active swimmer whose movement is characterized by two variables: velocity amplitude and direction. We experimentally observe Paratrechina longicornis ants cooperatively transporting loads of varying sizes. By analyzing the statistical features of the load's movement, we show how the salient properties of the cooperative transport are encoded in its deterministic and random accelerations. We find that while the autocorrelation time of the velocity direction increases with group size, the autocorrelation time of the speed has a maximum at an intermediate group size, corresponding to the critical slow down close to the previously identified phase transition. Our statistical model for cooperative ant transport demonstrates that an active swimmer model can be employed to describe a system of interacting individuals.

Ant colonies regulate foraging in response to their collective hunger, yet the mechanism behind this distributed regulation remains unclear. Previously, by imaging food flow within ant colonies we showed that the frequency of foraging events declines linearly with colony sati-ation (Greenwald et al., 2018). Our analysis implied that as a forager distributes food in the nest, two factors affect her decision to exit for another foraging trip: her current food load and its rate of change. Sensing these variables can be attributed to the foragers individual cognitive ability. Here, new analyses of the foragers trajectories within the nest imply a different way to achieve the observed regulation. Instead of an explicit decision to exit, foragers merely tend toward the depth of the nest when their food load is high and toward the nest exit when it is low. Thus, the colony shapes the foragers trajectory by controlling her unloading rate, while she senses only her current food load. Using an agent-based model and mathematical analysis, we show that this simple mechanism robustly yields emergent regulation of foraging frequency. These findings demonstrate how the embedding of individuals in physical space can reduce their cognitive demands without compro-mising their computational role in the group.

The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person's opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, we apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.

Social groups often need to overcome differences in individual interests and knowledge to reach consensus decisions. Here, we combine experiments and modeling to study conflict resolution in emigrating ant colonies during binary nest selection. We find that cohesive emigration, without fragmentation, is achieved only by intermediate-sized colonies. We then impose a conflict regarding the desired emigration target between colony subgroups. This is achieved using an automated selective gate system that manipulates the information accessible to each ant. Under this conflict, we find that individuals concede their potential benefit to promote social consensus. In particular, colonies resolve the conflict imposed by a persistent minority through \u201cmajority concession,\u201d wherein a majority of ants that hold first-hand knowledge regarding the superior quality nest choose to reside in the inferior one. This outcome is unlikely in social groups of selfish individuals and emphasizes the importance of group cohesion in eusocial societies.

1. Much like a single organism, colonies of social insects regulate their collective nutrition towards a specific intake of nutrients. Yet, the mechanisms behind this colony-level regulation are not fully understood. Although foraging behaviour in social insects has been studied extensively, not much is known on its relation with feeding processes that occur within the nest. In the nest, food is commonly transferred between individuals in numerous oral feeding interactions (trophallaxis). Studies on the properties of these trophallactic networks have so far been limited by: (a) the difficulty to non-intrusively measure food inside individual ants, let alone its nutritional composition, and (b) the meticulous manual labour involved in detecting trophallactic events. 2. Our dual-fluorescence imaging set-up is designed to track two food sources, each labelled with a different fluorophore, as they are disseminated throughout a freely behaving colony of individually tagged ants. Additionally, our image-based deep learning algorithm for automatic detection of ant trophallaxis events efficiently yields a detailed record of all food-transfer interactions. 3. Using a series of calibration experiments, we demonstrate the reliability of our measurements. We then exemplify the capabilities of our new method by tracking food dissemination in a colony of Camponotus sanctus ants supplied with two nutritionally distinct food sources. We follow the amount of each food type in the crop of each ant in the colony. Our data reveal the path in nutrient space that the colony took to reach its final nutrient intake, the contributions of the different forager ants to this path and the dynamic nutritional states of all ants in the colony. We further demonstrate the potential applicability of dual-fluorescence food imaging to other insect species. 4. Our system provides access to data that were previously unavailable and is crucial for a complete description of nutrient regulation by social insects. More broadly, the ability to simultaneously and efficiently track two material flows in a behaving colony provides new opportunities for tackling many open and emerging questions on topics other than nutrition, such as disease spread and social regulation of colony development.

Cooperative transport of large food loads by Paratrechina longicornis ants demands repeated decision-making. Inspired by the Evidence Accumulation (EA) model classically used to describe decision-making in the brain, we conducted a binary choice experiment where carrying ants rely on social information to choose between two paths. We found that the carried load performs a biased random walk that continuously alternates between the two options. We show that this motion constitutes a physical realization of the abstract EA model and exhibits an emergent version of the psychophysical Webers law. In contrast to the EA model, we found that the loads random step size is not fixed but, rather, varies with both evidence and circumstances. Using theoretical modeling we show that variable step size expands the scope of the EA model from isolated to sequential decisions. We hypothesize that this phenomenon may also be relevant in neuronal circuits that perform sequential decisions.

A new study relates the properties of Drosophila melanogaster social networks to group composition and demonstrates how they may be altered using behavioral priming and genetic manipulations.

Complex systems are made up of many entities, whose interactions emerge into distinct collective patterns. Computational modeling platforms can provide a powerful means to investigate emergent phenomena in complex systems. Some research has been carried out in recent years about promoting students' modeling practices, specifically using technologically advanced tools and approaches that allow students to create, manipulate, and test computational models. However, not much research had been carried out on the integration of several modeling approaches when investigating complex phenomena. In this paper, we describe the design principles used to develop a middle school unit about ants' collective behavior that integrates three modeling approaches: conceptual drawn models, agent-based models, and system dynamics models. We provide results from an initial implementation of an 8th grade curricular unit, indicating that students engaged with several aspects of the modeling practice. Students' conceptual knowledge about ant pheromone communication increased following learning the unit. We also found gains in students' metamodeling knowledge about models as tools for investigating phenomena. We discuss the affordances and challenges of engaging students with several modeling approaches in science classroom.

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.

Insect societies headed by multiple queens (polygyny) raise evolutionary questions, such as how does genetic heterogeneity among colony members affect innest interactions; or, are all queens equally reproductive or equally treated by workers? Answering such questions requires intensive and continuous observations of innest behavior. Here, we addressed these questions in the polygyne supercolonial ant, Cataglyphis niger, using a barcoding system that enables the tracking of individual interactions, together with polymorphic DNA microsatellite markers that indicate the matriline and patriline of all individuals. Our findings that both queens and workers have low interrelatedness corroborate earlier reports regarding the supercolony structure of C. niger. Ovary inspection and worker genotyping revealed that all the queens contribute similarly to nest demography. Tracking positions of individual workers through time revealed that only a small proportion of them are constantly engaged in tending the queens and can be considered as retinue workers. However, genotyping these workers and the attended queens revealed no relationship to genetic relatedness, again typical of a true polygyne and supercolonial species. Unlike invasive supercolonial species, C. niger is native to Israel, enabling us to address questions regarding the driving forces, other than kin selection, that stabilize this society.

Allocation of goods is a key feature in defining the connection between the individual and the collective scale in any society. Both the process by which goods are to be distributed, and the resulting allocation to the members of the society may affect the success of the population as a whole. One of the most striking natural examples of a highly successful cooperative society is the ant colony which often acts as a single superorganism. In particular, each individual within the ant colony has a "communal stomach" which is used to store and share food with the other colony members by mouth to mouth feeding. Sharing food between communal stomachs allows the colony as a whole to get its food requirements and, more so, allows each individual within the colony to reach its nutritional intake target. The vast majority of colony members do not forage independently but obtain their food through secondary interactions in which food is exchanged between individuals. The global effect of this exchange is not well understood. To gain better understanding into this process we used fluorescence imaging to measure how food from a single external source is distributed and mixed within a Camponotus sanctus ant colony. Using entropic measures to quantify food-blending, we show that while collected food flows into all parts of the colony it mixes only partly. We show that mixing is controlled by the ants' interaction rule which implies that only a fraction of the maximal potential is actually transferred. This rule leads to a robust blending process: i.e., neither the exact food volume that is transferred, nor the interaction schedule are essential to generate the global outcome. Finally, we show how the ants' interaction rules may optimize a trade-off between fast dissemination and efficient mixing. Our results regarding the distribution of a single food source provide a baseline for future studies on distributed regulation of multiple food sources in social insect colonies.

Animal navigation relies on the available environmental cues and, where present, visual cues typically dominate. While much is known about vision-assisted navigation, knowledge of navigation in the dark is scarce. Here, we combine individual tracking, dynamic modular nest structures, and spatially resolved chemical profiling to study how Camponotus fellah ants navigate within the dark labyrinth of their nest. We find that, contrary to ant navigation above ground, underground navigation cannot rely on long-range information. This limitation emphasizes the ants' capabilities associated with other navigational strategies. Indeed, apart from gravity, underground navigation relies on self-referenced memories of multiple locations and on socially generated chemical cues placed at decision points away from the target. Moreover, the ants quickly readjust the weights attributed to these information sources in response to environmental changes. Generally, studying well-known behaviors in a variety of environmental contexts holds the potential of revealing new insights into animal cognition.

Anyone who has moved furniture together with friends will appreciate that cooperative transport requires some non-trivial communication. Yet ants are adept at collectively moving objects several times their size. How they do so has long been a subject of research, but recent advances have suggested that this communication occurs through the forces the ants exert on the load. This implies that the collective transport problem can be mapped to an Ising model, in which decisions by individual ants are described by spin flips. Within this framework, the group is poised in the vicinity of the transition between uncoordinated and coordinated motion. It thus profits from both internal coordination and maximal responsiveness to external information, mediated by temporarily informed leader ants. Here, we review the implications of these findings for cooperative transport, and discuss the way in which a more complete multiscale understanding of such systems would require the development of a new formalism that combines statistical physics of interacting particles with the cognitive capabilities of individuals.

Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. We study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task. We present an impossibility result which strongly restricts reliable rumor spreading in such groups. Namely, we prove that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources-a process which requires linear time in the population size. Our results imply that in order to achieve efficient rumor spread a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. We then corroborate this claim by providing new analyses of experimental data regarding recruitment in Cataglyphis niger desert ants. Finally, in light of our theoretical results, we discuss strategies to overcome noise in other biological systems.

To cooperatively carry large food items to the nest, individual ants conform their efforts and coordinate their motion. Throughout this expedition, collective motion is driven both by internal interactions between the carrying ants and a response to newly arrived informed ants that orient the cargo towards the nest. During the transport process, the carrying group must overcome obstacles that block their path to the nest. Here, we investigate the dynamics of cooperative transport, when the motion of the ants is frustrated by a linear obstacle that obstructs the motion of the cargo. The obstacle contains a narrow opening that serves as the only available passage to the nest, and through which single ants can pass but not with the cargo. We provide an analytical model for the ant-cargo system in the constrained environment that predicts a bi-stable dynamic behavior between an oscillatory mode of motion along the obstacle and a convergent mode of motion near the opening. Using both experiments and simulations, we show how for small cargo sizes, the system exhibits spontaneous transitions between these two modes of motion due to fluctuations in the applied force on the cargo. The bi-stability provides two possible problem solving strategies for overcoming the obstacle, either by attempting to pass through the opening, or take large excursions to circumvent the obstacle.

Nutritional regulation by ants emerges from a distributed process: food is collected by a small fraction of workers, stored within the crops of individuals, and spread via local ant-to-ant interactions. The precise individual-level underpinnings of this collective regulation have remained unclear mainly due to difficulties in measuring food within ants' crops. Here we image fluorescent liquid food in individually tagged Camponotus sanctus ants and track the real-time food flow from foragers to their gradually satiating colonies. We show how the feedback between colony satiation level and food inflow is mediated by individual crop loads; specifically, the crop loads of recipient ants control food flow rates, while those of foragers regulate the frequency of foraging-trips. Interestingly, these effects do not rise from pure physical limitations of crop capacity. Our findings suggest that the emergence of food intake regulation does not require individual foragers to assess the global state of the colony.

Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems, wherein messages are often distorted before reaching their destination, is quite limited. In this study we take a first step towards reducing this gap by rigorously analyzing a model of communication in large anonymous populations composed of simple agents which interact through short and highly unreliable messages. We focus on the broadcast problem and the majority-consensus problem. Both are fundamental information dissemination problems in distributed computing, in which the goal of agents is to converge to some prescribed desired opinion. We initiate the study of these problems in the presence of communication noise. Our model for communication is extremely weak and follows the push gossip communication paradigm: In each round each agent that wishes to send information delivers a message to a random anonymous agent. This communication is further restricted to contain only one bit (essentially representing an opinion). Lastly, the system is assumed to be so noisy that the bit in each message sent is flipped independently with probability 1 / 2 - ϵ, for some small ϵ> 0. Even in this severely restricted, stochastic and noisy setting we give natural protocols that solve the noisy broadcast and majority-consensus problems efficiently. Our protocols run in O(log n/ ϵ²) rounds and use O(nlog n/ ϵ²) messages/bits in total, where n is the number of agents. These bounds are asymptotically optimal and, in fact, are as fast and message efficient as if each agent would have been simultaneously informed directly by an agent that knows the prescribed desired opinion. Our efficient, robust, and simple algorithms suggest balancing between silence and transmission, synchronization, and majority-based decisions as important ingredients towards understanding collective communication schemes in anonymous and noisy populations.

The difference between the speed of the actions of different processes is typically considered as an obstacle that makes the achievement of cooperative goals more difficult. In this work, we aim to highlight potential benefits of such asynchrony phenomena to tasks involving symmetry breaking. Specifically, in this paper, identical (except for their speeds) mobile agents are placed at arbitrary locations on a (continuous) cycle of length n and use their speed difference in order to rendezvous fast. We normalize the speed of the slower agent to be 1, and fix the speed of the faster agent to be some c>1. (An agent does not know whether it is the slower agent or the faster one.) The straightforward distributed-race (DR) algorithm is the one in which both agents simply start walking until rendezvous is achieved. It is easy to show that, in the worst case, the rendezvous time of DR is n/(c−1). Note that in the interesting case, where c is very close to 1 (e.g., c=1+1/n^k), this bound becomes huge. Our first result is a lower bound showing that, up to a multiplicative factor of 2, this bound is unavoidable, even in a model that allows agents to leave arbitrary marks (the white board model), even assuming sense of direction, and even assuming n and c are known to agents. That is, we show that under such assumptions, the rendezvous time of any algorithm is at least [Formula presented] if c⩽3 and slightly larger (specifically, [Formula presented]) if c>3. We then manage to construct an algorithm that precisely matches the lower bound for the case c⩽2, and almost matches it when c>2. Moreover, our algorithm performs under weaker assumptions than those stated above, as it does not assume sense of direction, and it allows agents to leave only a single mark (a pebble) and only at the place where they start the execution. Finally, we investigate the setting in which no marks can be used at all, and show tight bounds for c⩽2, and almost tight bounds for c>2.

Communication provides the basis for social life. In ant colonies, the prevalence of local, often chemically mediated, interactions introduces strong links between communication networks and the spatial distribution of ants. It is, however, unknown how ants identify and maintain nest chambers with distinct functions. Here, we combine individual tracking, chemical analysis and machine learning to decipher the chemical signatures present on multiple nest surfaces. We present evidence for several distinct chemical 'road-signs' that guide the ants' movements within the dark nest. These chemical signatures can be used to classify nest chambers with different functional roles. Using behavioural manipulations, we demonstrate that at least three of these chemical signatures are functionally meaningful and allow ants from different task groups to identify their specific nest destinations, thus facilitating colony coordination and stabilization. The use of multiple chemicals that assist spatiotemporal guidance, segregation and pattern formation is abundant in multi-cellular organisms. Here, we provide a rare example for the use of these principles in the ant colony.

We introduce the Ants Nearby Treasure Search problem, which models natural cooperative foraging behavior such as that performed by ants around their nest. In this problem, k probabilistic agents, initially placed at a central location, collectively search for a treasure on the two-dimensional grid. The treasure is placed at a target location by an adversary and the agents goal is to find it as fast as possible as a function of both k and D, where D is the (unknown) distance between the central location and the target. We concentrate on the case in which agents cannot communicate while searching. It is straightforward to see that the time until at least one agent finds the target is at least Ω(D+ D²/ k) , even for very sophisticated agents, with unrestricted memory. Our algorithmic analysis aims at establishing connections between the time complexity and the initial knowledge held by agents (e.g., regarding their total number k), as they commence the search. We provide a range of both upper and lower bounds for the initial knowledge required for obtaining fast running time. For example, we prove that log log k+ Θ(1) bits of initial information are both necessary and sufficient to obtain asymptotically optimal running time, i.e., O(D+ D²/ k). We also we prove that for every 0 < ϵ< 1 , running in time O(log ^{1 - ϵ}k· (D+ D²/ k)) requires that agents have the capacity for storing Ω(log ^ϵk) different states as they leave the nest to start the search. To the best of our knowledge, the lower bounds presented in this paper provide the first non-trivial lower bounds on the memory complexity of probabilistic agents in the context of search problems. We view this paper as a \u201cproof of concept\u201d for a new type of interdisciplinary methodology. To fully demonstrate this methodology, the theoretical tradeoff presented here (or a similar one) should be combined with measurements of the time performance of searching ants.

The concerted responses of eusocial insects to environmental stimuli are often referred to as collective cognition at the level of the colony. To achieve collective cognition, a group can draw on two different sources: Individual cognition and the connectivity between individuals. Computation in neural networks, for example, is attributed more to sophisticated communication schemes than to the complexity of individual neurons. The case of social insects, however, can be expected to differ. This is because individual insects are cognitively capable units that are often able to process information that is directly relevant at the level of the colony. Furthermore, involved communication patterns seem difficult to implement in a group of insects as they lack a clear network structure. This review discusses links between the cognition of an individual insect and that of the colony. We provide examples for collective cognition whose sources span the full spectrum between amplification of individual insect cognition and emergent group-level processes.

Collective motion by animal groups is affected by internal interactions, external constraints, and the influx of information. A quantitative understanding of how these different factors give rise to different modes of collective motion is, at present, lacking. Here, we study how ants that cooperatively transport a large food item react to an obstacle blocking their path. Combining experiments with a statistical physics model of mechanically coupled active agents, we show that the constraint induces a deterministic collective oscillatory mode that facilitates obstacle circumvention. We provide direct experimental evidence, backed by theory, that this motion is an emergent group effect that does not require any behavioral changes at the individual level. We trace these relaxation oscillations to the interplay between two forces; informed ants pull the load toward the nest whereas uninformed ants contribute to the motions persistence along the tangential direction. The models predictions that oscillations appear above a critical system size, that the group can spontaneously transition into its ordered phase, and that the system can exhibit complete rotations are all verified experimentally. We expect that similar oscillatory modes emerge in collective motion scenarios where the structure of the environment imposes conflicts between individually held information and the groups tendency for cohesiveness.

Any organism faces sensory and cognitive limitations which may result in maladaptive decisions. Such limitations are prominent in the context of groups where the relevant information at the individual level may not coincide with collective requirements. Here, we study the navigational decisions exhibited by Paratrechina longicornis ants as they cooperatively transport a large food item. These decisions hinge on the perception of individuals which often restricts them from providing the group with reliable directional information. We find that, to achieve efficient navigation despite partial and even misleading information, these ants employ a locally-blazed trail. This trail significantly deviates from the classical notion of an ant trail: First, instead of systematically marking the full path, ants mark short segments originating at the load. Second, the carrying team constantly loses the guiding trail. We experimentally and theoretically show that the locally-blazed trail optimally and robustly exploits useful knowledge while avoiding the pitfalls of misleading information.

Animals may face a tradeoff between enhanced cognitive ability and the cost of maintaining larger and more energetically expensive brains. In social species, this tradeoff could be influenced by energy-reducing benefits of cooperation and collective cognition. Greater social complexity in ants is hypothesized to be associated with smaller brain size, a pattern opposite that found in some social vertebrates. Ants vary in worker and colony size, and worker size-frequency distributions; larger workers have larger, more costly, brains. Colony-level foraging performance and its energetic consequence to fitness depend on individual cognitive capabilities as well as collective behaviors. To explore the impact of diet, behavioral ability, and social organization on brain evolution, we developed a model incorporating food availability, foraging behavior and related energetic gain, colony size, worker size and polymorphism, and brain size. Colonies could increase energy intake through foraging performance by increasing worker size, and by correlation, brain size and expanded task capability, or by increasing colony size and collective foraging. Results show that resource-poor environments favored small colonies of relatively large-bodied and thus large-brained pluripotent workers that had higher energetic costs. In contrast, large colonies of relatively small-bodied and small-brained workers in resource-rich environments harvested food with lower brain investment and hence decreased metabolic costs. Worker size-related behavioral specialization and covarying brain investment also influenced fitness. The energetic advantage of polymorphism depended on the degree of association of the navigational abilities employed during foraging and worker brain size. Our study suggests that diet, colony size, and worker size may influence tradeoffs between individual and collective cognition and the evolution of brain size and division of labor in eusocial insects such as ants.

We consider the External Clock Synchronization problem in dynamic sensor networks. Initially, sensors obtain inaccurate estimations of an external time reference and subsequently collaborate in order to synchronize their internal clocks with the external time. For simplicity, we adopt the drift-free assumption, where internal clocks are assumed to tick at the same pace. Hence, the problem is reduced to an estimation problem, in which the sensors need to estimate the initial external time. In this context of distributed estimation, this work is further relevant to the problem of collective approximation of environmental values by biological groups. Unlike most works on clock synchronization that assume static networks, this paper focuses on an extreme case of highly dynamic networks. We do however impose a restriction on the dynamicity of the network. Specifically, we assume a non-adaptive scheduler adversary that dictates an arbitrary, yet independent, meeting pattern. Such meeting patterns fit, for example, with short-time scenarios in highly dynamic settings, where each sensor interacts with only few other arbitrary sensors. We propose an extremely simple clock synchronization (or an estimation) algorithm that is based on weighted averages, and prove that its performance on any given independent meeting pattern is highly competitive with that of the best possible algorithm, which operates without any resource or computational restrictions, and further knows the whole meeting pattern in advance. In particular, when all distributions involved are Gaussian, the performances of our scheme coincide with the optimal performances. Our proofs rely on an extensive use of the concept of Fisher information. We use the Cramér-Rao bound and our definition of a Fisher Channel Capacity to quantify information flows and to obtain lower bounds on collective performance. This opens the door for further rigorous quantifications of information flows within collaborative sensors.

Eusocial societies and ants, in particular, maintain tight nutritional regulation at both individual and collective levels. The mechanisms that underlie this control are far from trivial since, in these distributed systems, information about the global supply and demand is not available to any single individual. Here we present a novel technique for non-intervening frequent measurement of the food load of all individuals in an ant colony, including during trophallactic events in which food is transferred by mouth-to-mouth feeding. Ants are imaged using a dual camera setup that produces both barcode-based identification and fluorescence measurement of labeled food. This system provides detailed measurements that enable one to quantitatively study the adaptive food distribution network. To demonstrate the capabilities of our method, we present sample observations that were unattainable using previous techniques, and could provide insight into the mechanisms underlying food exchange.

To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. Here, we theoretically study a general model of information sharing within animal groups. We take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. We present a simple and natural algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. We motivate this model by novel and existing empirical evidences for confidence sharing in animal groups. We rigorously show that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. We also show that this algorithm is minimal, in the sense that further reduction in communication may significantly reduce performances. Our proofs rely on the Cramér-Rao bound and on our definition of a Fisher Channel Capacity. We use these concepts to quantify information flows within the group which are then used to obtain lower bounds on collective performance. The abstract nature of our model makes it rigorously solvable and its conclusions highly general. Indeed, our results suggest confidence sharing as a central notion in the context of animal communication.

Collective decisions in animal groups emerge from the actions of individuals who are unlikely to have global information. Comparative assessment of options can be valuable in decision-making. Ant colonies are excellent collective decision-makers, for example when selecting a new nest-site. Here, we test the dependency of this cooperative process on comparisons conducted by individual ants. We presented ant colonies with a choice between new nests: one good and one poor. Using individually radio-tagged ants and an automated system of doors, we manipulated individual-level access to information: ants visiting the good nest were barred from visiting the poor one and vice versa. Thus, no ant could individually compare the available options. Despite this, colonies still emigrated quickly and accurately when comparisons were prevented. Individual-level rules facilitated this behavioural robustness: ants allowed to experience only the poor nest subsequently searched more. Intriguingly, some ants appeared particularly discriminating across emigrations under both treatments, suggesting they had stable, high nest acceptance thresholds. Overall, our results show how a colony of ants, as a cognitive entity, can compare two options that are not both accessible by any individual ant. Our findings illustrate a collective decision process that is robust to differences in individual access to information.

We study how desert ants, Cataglyphis niger, a species that lacks pheromonebased recruitment mechanisms, inform each other about the presence of food. Our results are based on automated tracking that allows us to collect a large database of ant trajectories and interactions. We find that interactions affect an ant's speed within the nest. Fast ants tend to slow down, whereas slow ones increase their speed when encountering a faster ant. Faster ants tend to exit the nest more frequently than slower ones. So, if an ant gains enough speed through encounters with others, then she tends to leave the nest and look for food. On the other hand, we find that the probability for her to leave the nest depends only on her speed, but not on whether she had recently interacted with a recruiter that has found the food. This suggests a recruitment system in which ants communicate their state by very simple interactions. Based on this assumption, we estimate the information-theoretical channel capacity of the ants' pairwise interactions. We find that the response to the speed of an interacting nest-mate is very noisy. The question is then how random interactions with ants within the nest can be distinguished from those interactions with a recruiter who has found food. Our measurements and model suggest that this distinction does not depend on reliable communication but on behavioural differences between ants that have found the food and those that have not. Recruiters retain high speeds throughout the experiment, regardless of the ants they interact with; non-recruiters communicate with a limited number of nestmates and adjust their speed following these interactions. These simple rules lead to the formation of a bistable switch on the level of the group that allows the distinction between recruitment and random noise in the nest. A consequence of the mechanism we propose is a negative effect of ant density on exit rates and recruitment success. This is, indeed, confirmed by our measurements.

Social behaviour has a key role in animal survival across species, ranging from insects to primates and humans. However, the biological mechanisms driving natural interactions between multiple animals, over long-term periods, are poorly studied and remain elusive. Rigorous and objective quantification of behavioural parameters within a group poses a major challenge as it requires simultaneous monitoring of the positions of several individuals and comprehensive consideration of many complex factors. Automatic tracking and phenotyping of interacting animals could thus overcome the limitations of manual tracking methods. Here we report a broadly applicable system that automatically tracks the locations of multiple, uniquely identified animals, such as mice, within a semi-natural setting. The system combines video and radio frequency identified tracking data to obtain detailed behavioural profiles of both individuals and groups. We demonstrate the usefulness of these data in characterizing individual phenotypes, interactions between pairs and the collective social organization of groups.

Social groups are structured by the decisions of their members. Social insects typically divide labour: some decide to stay in the nest while others forage for the colony. Two sources of information individuals may use when deciding whether to forage are their own experience of recent task performance and their own physiology, e.g. fat reserves (corpulence). The former is primarily personal information; the latter may give an indication of the food reserves of the whole colony. These factors are hard to separate because typically leaner individuals are also more experienced foragers. We designed an experiment to determine whether foraging specialisation is physiological or experience based (or both). We invented a system of automatic doors controlled by radio-tag information to manipulate task access and decouple these two sources of information. Our results show that when information from corpulence and recent experience conflict, ants behave only in accordance with their corpulence. However, among ants physiologically inclined to forage (less corpulent ants), recent experience of success positively influenced their propensity to forage again. Hence, foraging is organised via long-term physiological differences among individuals resulting in a relatively stable response threshold distribution, with fine-tuning provided by short-term learning processes. Through these simple rules, colonies can organise their foraging effort both robustly and flexibly.

Functional logical microcircuits are an essential building block of computation in the brain. However, single neuronal connections are unreliable, and it is unclear how neuronal ensembles can be constructed to achieve high response fidelity. Here, we show that reliable, mesoscale logical devices can be created in vitro by geometrical design of neural cultures. We control the connections and activity by assembling living neural networks on quasi-one-dimensional configurations. The linear geometry yields reliable transmission lines. Incorporating thin lines creates 'threshold' devices and logical 'AND gates'. Breaking the symmetry of transmission makes neuronal 'diodes'. All of these function with error rates well below that of a single connection. The von Neumann model of redundancy and error correction accounts well for all of the devices, giving a quantitative estimate for the reliability of a neuronal connection and of threshold devices. These neuronal devices may contribute to the implementation of computation in vitro and, ultimately, to its understanding in vivo.

Improvements in technique in conjunction with an evolution of the theoretical and conceptual approach to neuronal networks provide a new perspective on living neurons in culture. Organization and connectivity are being measured quantitatively along with other physical quantities such as information, and are being related to function. In this review we first discuss some of these advances, which enable elucidation of structural aspects. We then discuss two recent experimental models that yield some conceptual simplicity. A one-dimensional network enables precise quantitative comparison to analytic models, for example of propagation and information transport. A two-dimensional percolating network gives quantitative information on connectivity of cultured neurons. The physical quantities that emerge as essential characteristics of the network in vitro are propagation speeds, synaptic transmission, information creation and capacity. Potential application to neuronal devices is discussed.

Spontaneous activity is typical of in vitro neural networks, often in the form of large population bursts. The origins of this activity are attributed to intrinsically bursting neurons and to noisy backgrounds as well as to recurrent network connections. Spontaneous activity is often observed to emanate from localized sources or initiation zones, propagating from there to excite large populations of neurons. In this study, we use unidimensional cultures to overcome experimental difficulties in identifying initiation zones in vivo and in dissociated two-dimensional cultures. We found that spontaneous activity in these cultures is initiated exclusively in localized zones that are characterized by high neuronal density but also by recurrent and inhibitory network connections. We demonstrate that initiation zones compete in driving network activity in a winner-takes-most scenario.

The ability of synchronous population activity in layered networks to transmit a rate code is a focus of recent debate. We investigate these issues using a patterned unidimensional hippocampal culture. The network exhibits population bursts that travel its full length, with the advantage that signals propagate along a clearly defined path. The amplitudes of activity are measured using calcium imaging, a good approximate of population rate code, and the distortion of the signal as it travels is analyzed. We demonstrate that propagation along the line is precisely described by information theory as a chain of Gaussian communication channels. The balance of excitatory and inhibitory synapses is crucial for this transmission. However, amplitude information carried along this layered neuronal structure fails within 3 mm, ∼10 mean axon lengths, and is limited by noise in the synaptic transmission. We conclude that rate codes cannot be reliably transmitted through long layered networks.

Dissociated neurons were cultured on lines of various lengths covered with adhesive material to obtain an experimental model system of linear signal transmission. The neuronal connectivity in the linear culture is characterized, and it is demonstrated that local spiking activity is relayed by synaptic transmission along the line of neurons to develop into a large-scale population burst. Formally, this can be treated as a one-dimensional information channel. Directional propagation of both spontaneous and stimulated bursts along the line, imaged with the calcium indicator Fluo-4, revealed the existence of two different propagation velocities. Initially, a small number of neighboring neurons fire, leading to a slow, small and presumably asynchronous wave of activity. The signal then spontaneously develops to encompass much larger and further populations, and is characterized by fast propagation of high-amplitude activity, which is presumed to be synchronous. These results are well described by an existing theoretical framework for propagation based on an integrate-and-fire model.

A double pipette system for local, controlled drug infusion is presented. Two concentric pipettes can be manipulated separately and pressurized independently by a designated double holder. The inner pipette is loaded with the desirable solution (drug), and functions as a source, while the outer one is a sink. This gives a flow of the solution between the two pipettes that protrudes only a small distance into the surrounding fluid and does not diffuse away. Time resolution of the infusion is highly controllable, and oscillatory flow can be generated. Three implementations of the double pipette system are demonstrated. We show that local application of neurotransmitters in neuronal networks is an efficient way of stimulating activity in the network. We then present a wet micro lithography technique using topical application of proteins onto the substrate. Finally, we show that we can localize a given drug on a small targeted part of a cell.