Imitation Combined with a Characteristic Stimulus Duration Results in Robust Collective Decision-Making

For group-living animals, reaching consensus to stay cohesive is crucial for their fitness, particularly when collective motion starts and stops. Understanding the decision-making at individual and collective levels upon sudden disturbances is central in the study of collective animal behavior, and concerns the broader question of how information is distributed and evaluated in groups. Despite the relevance of the problem, well-controlled experimental studies that quantify the collective response of groups facing disruptive events are lacking. Here we study the behavior of small-sized groups of uninformed individuals subject to the departure and stop of a trained conspecific. We find that the groups reach an effective consensus: either all uninformed individuals follow the trained one (and collective motion occurs) or none does. Combining experiments and a simple mathematical model we show that the observed phenomena results from the interplay between simple mimetic rules and the characteristic duration of the stimulus, here, the time during which the trained individual is moving away. The proposed mechanism strongly depends on group size, as observed in the experiments, and even if group splitting can occur, the most likely outcome is always a coherent collective group response (consensus). The prevalence of a consensus is expected even if the groups of naives face conflicting information, e.g. if groups contain two subgroups of trained individuals, one trained to stay and one trained to leave. Our results indicate that collective decision-making and consensus in (small) animal groups are likely to be self-organized phenomena that do not involve concertation or even communication among the group members.     

Confidence Sharing: An Economic Strategy for Efficient Information Flows in Animal Groups

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.     

Information transfer in moving animal groups

Moving animal groups provide some of the most intriguing and difficult to characterise examples of collective behaviour. We review some recent (and not so recent) empirical research on the motion of animal groups, including fish, locusts and homing pigeons. An important concept which unifies our understanding of these groups is that of transfer of directional information. Individuals which change their direction of travel in response to the direction taken by their near neighbours can quickly transfer information about the presence of a predatory threat or food source. We show that such information transfer is optimised when the density of individuals in a group is close to that at which a phase transition occurs between random and ordered motion. Similarly, we show that even relatively small differences in information possessed by group members can lead to strong collective-level decisions for one of two options. By combining the use of self-propelled particle and social force models of collective motion with thinking about the evolution of flocking we aim to better understand how complexity arises within these groups.

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A review of fish swimming mechanics and behaviour in altered flows

Fishes suspended in water are subject to the complex nature of three-dimensional flows. Often, these flows are the result of abiotic and biotic sources that alter otherwise uniform flows, which then have the potential to perturb the swimming motions of fishes. The goal of this review is to highlight key studies that have contributed to a mechanistic and behavioural understanding of how perturbing flows affect fish. Most of our understanding of fish behaviour in turbulence comes from observations of natural conditions in the field and laboratory studies employing controlled perturbations, such as vortices generated in the wake behind simple geometric objects. Laboratory studies have employed motion analysis, flow visualization, electromyography, respirometry and sensory deprecation techniques to evaluate the mechanisms and physiological costs of swimming in altered flows. Studies show that flows which display chaotic and wide fluctuations in velocity can repel fishes, while flows that have a component of predictability can attract fishes. The ability to maintain stability in three-dimensional flows, either actively with powered movements or passively using the posture and intrinsic compliance of the body and fins, plays a large role in whether fish seek out or avoid turbulence. Fish in schools or current-swept habitats can benefit from altered flows using two distinct though not mutually exclusive mechanisms: flow refuging (exploiting regions of reduced flow relative to the earth frame of reference) and vortex capture (harnessing the energy of environmental vortices). Integrating how the physical environment affects organismal biomechanics with the more complex issue of behavioural choice requires consideration beyond simple body motions or metabolic costs. A fundamental link between these two ways of thinking about animal behaviour is how organisms sense and process information from the environment, which determines when locomotor behaviour is initiated and modulated. New data are presented here which show that behaviour changes in altered flows when either the lateral line or vision is blocked, showing that fish rely on multi-modal sensory inputs to negotiate complex flow environments. Integrating biomechanics and sensory biology to understand how fish swim in turbulent flow at the organismal level is necessary to better address population-level questions in the fields of fisheries management and ecology.     

Age-specific characteristics and coupling of cerebral arterial inflow and cerebrospinal fluid dynamics

The objective of this work is to quantify age-related differences in the characteristics and coupling of cerebral arterial inflow and cerebrospinal fluid (CSF) dynamics. To this end, 3T phase-contrast magnetic resonance imaging blood and CSF flow data of eleven young (24 ± 3 years) and eleven elderly subjects (70 ± 5 years) with a comparable sex-ratio were acquired. Flow waveforms and their frequency composition, transfer functions from blood to CSF flows and cross-correlations were analyzed. The magnitudes of the frequency components of CSF flow in the aqueduct differ significantly between the two age groups, as do the frequency components of the cervical spinal CSF and the arterial flows. The males' aqueductal CSF stroke volumes and average flow rates are significantly higher than those of the females. Transfer functions and cross-correlations between arterial blood and CSF flow reveal significant age-dependence of phase-shift between these, as do the waveforms of arterial blood, as well as cervical-spinal and aqueductal CSF flows. These findings accentuate the need for age- and sex-matched control groups for the evaluation of cerebral pathologies such as hydrocephalus.     

Measuring spectrally-resolved information transfer

Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy and entirely avoids filtering of the original signals. The approach thereby avoids well-known problems due to phase shifts or the ineffectiveness of filtering in the information theoretic setting. We also show that measuring frequency-resolved information transfer is a partial information decomposition problem that cannot be fully resolved to date and discuss the implications of this issue. Last, we evaluate the performance of our algorithm on simulated data and apply it to human magnetoencephalography (MEG) recordings and to local field potential recordings in the ferret. In human MEG we demonstrate top-down information flow in temporal cortex from very high frequencies (above 100Hz) to both similarly high frequencies and to frequencies around 20Hz, i.e. a complex spectral configuration of cortical information transmission that has not been described before. In the ferret we show that the prefrontal cortex sends information at low frequencies (4-8 Hz) to early visual cortex (V1), while V1 receives the information at high frequencies (> 125 Hz).     

School density affects the strength of collective avoidance responses in wild‐caught Atlantic herring Clupea harengus: a simulated predator encounter experiment

An experimental study in a semi‐controlled environment was conducted to examine whether school density in wild‐caught Atlantic herring Clupea harengus affects the strength of their collective escape behaviours. Using acoustics, the anti‐predator diving responses of C. harengus in two schools that differed in density were quantified by exposing them to a simulated threat. Due to logistical restrictions, the first fish was tested in a low‐density school condition (four trials; packing density = 1·5 fish m^?3; c. 6000 fish) followed by fish in a high‐density school condition (five trials; packing density = 16 fish m^?3; c. 60 000 fish). The C. harengus in a high‐density school exhibited stronger collective diving avoidance responses to the simulated predators than fish in the lower‐density school. The findings suggest that the density (and thus the internal organization) of a fish school affects the strength of collective anti‐predatory responses, and the extent to which information about predation risk is transferred through the C. harengus school. Therefore, the results challenge the common notion that information transfer within animal groups may not depend on group size and density.     

The principles of collective animal behaviour

In recent years, the concept of self-organization has been used to understand collective behaviour of animals. The central tenet of self-organization is that simple repeated interactions between individuals can produce complex adaptive patterns at the level of the group. Inspiration comes from patterns seen in physical systems, such as spiralling chemical waves, which arise without complexity at the level of the individual units of which the system is composed. The suggestion is that biological structures such as termite mounds, ant trail networks and even human crowds can be explained in terms of repeated interactions between the animals and their environment, without invoking individual complexity. Here, I review cases in which the self-organization approach has been successful in explaining collective behaviour of animal groups and societies. Ant pheromone trail networks, aggregation of cockroaches, the applause of opera audiences and the migration of fish schools have all been accurately described in terms of individuals following simple sets of rules. Unlike the simple units composing physical systems, however, animals are themselves complex entities, and other examples of collective behaviour, such as honey bee foraging with its myriad of dance signals and behavioural cues, cannot be fully understood in terms of simple individuals alone. I argue that the key to understanding collective behaviour lies in identifying the principles of the behavioural algorithms followed by individual animals and of how information flows between the animals. These principles, such as positive feedback, response thresholds and individual integrity, are repeatedly observed in very different animal societies. The future of collective behaviour research lies in classifying these principles, establishing the properties they produce at a group level and asking why they have evolved in so many different and distinct natural systems. Ultimately, this research could inform not only our understanding of animal societies, but also the principles by which we organize our own society.     

Flow preferences of individual blacknose dace (Rhinichthys atratulus); influence of swimming ability and environmental history

Fish from lotic environments generally have a variety of flow velocities available to them in their immediate environment. Other than prey availability or predator presence, little is known about what factors determine where in this mosaic of flows an individual fish will choose to locate. Since individuals of a species can have substantially different swimming abilities, and interspecific differences in flow velocity selection have been related to differential swimming abilities, one possibility is that an animal??s physical condition constrains the flow environments it chooses to occupy. Additionally, since the flow in an animal??s environment can contribute to swimming ability, there could also be environmental control over flow selection behavior. This study examined whether flow velocity selection by individual blacknose dace (Rhinichthys atratulus) is a repeatable trait in the laboratory, and whether it is a function of either the animal??s swimming ability or the magnitude of flow in their home stream reach. Blacknose dace from two populations, collected from each of two separate reaches with substantially different flows from within their home streams, exhibited significantly repeatable flow velocity selection over the course of 1?day in the laboratory. The flow velocity selected by the fish varied significantly among individual dace. Some of this variance was accounted for by fish from the slower stream reaches choosing significantly faster flows than did those from faster reaches. There were no significant differences in flow selection behavior between populations. There was also no relationship between sprinting ability and the flow velocity selected by a fish.     

The dynamics of coordinated group hunting and collective information transfer among schooling prey

Predator-prey interactions are vital to the stability of many ecosystems. Yet, few studies have considered how they are mediated due to substantial challenges in quantifying behavior over appropriate temporal and spatial scales. Here, we employ high-resolution sonar imaging to track the motion and interactions among predatory fish and their schooling prey in a natural environment. In particular, we address the relationship between predator attack behavior and the capacity for prey to respond both directly and through collective propagation of changes in velocity by group members. To do so, we investigated a large number of attacks and estimated per capita risk during attack and its relation to the size, shape, and internal structure of prey groups. Predators were found to frequently form coordinated hunting groups, with up to five individuals attacking in line formation. Attacks were associated with increased fragmentation and irregularities in the spatial structure of prey groups, features that inhibit collective information transfer among prey. Prey group fragmentation, likely facilitated by predator line formation, increased (estimated) per capita risk of prey, provided prey schools were maintained below a threshold size of approximately 2 m(2). Our results highlight the importance of collective behavior to the strategies employed by both predators and prey under conditions of considerable informational constraints.     

Collective animal behavior from Bayesian estimation and probability matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.     

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

Groups of organisms, from bacteria to fish schools to human societies, depend on their ability to make accurate decisions in an uncertain world. Most models of collective decision-making assume that groups reach a consensus during a decision-making bout, often through simple majority rule. In many natural and sociological systems, however, groups may fail to reach consensus, resulting in stalemates. Here, we build on opinion dynamics and collective wisdom models to examine how stalemates may affect the wisdom of crowds. For simple environments, where individuals have access to independent sources of information, we find that stalemates improve collective accuracy by selectively filtering out incorrect decisions (an effect we call stalemate filtering). In complex environments, where individuals have access to both shared and independent information, this effect is even more pronounced, restoring the wisdom of crowds in regions of parameter space where large groups perform poorly when making decisions using majority rule. We identify network properties that tune the system between consensus and accuracy, providing mechanisms by which animals, or evolution, could dynamically adjust the collective decision-making process in response to the reward structure of the possible outcomes. Overall, these results highlight the adaptive potential of stalemate filtering for improving the decision-making abilities of group-living animals.     

An information theoretic characterisation of auditory encoding

The entropy metric derived from information theory provides a means to quantify the amount of information transmitted in acoustic streams like speech or music. By systematically varying the entropy of pitch sequences, we sought brain areas where neural activity and energetic demands increase as a function of entropy. Such a relationship is predicted to occur in an efficient encoding mechanism that uses less computational resource when less information is present in the signal: we specifically tested the hypothesis that such a relationship is present in the planum temporale (PT). In two convergent functional MRI studies, we demonstrated this relationship in PT for encoding, while furthermore showing that a distributed fronto-parietal network for retrieval of acoustic information is independent of entropy. The results establish PT as an efficient neural engine that demands less computational resource to encode redundant signals than those with high information content.     

Imperfect detection and the determination of environmental flows for fish: challenges,implications and solutions

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Coping with flow: behavior, neurophysiology and modeling of the fish lateral line system

With the mechanosensory lateral line fish perceive water motions relative to their body surface and local pressure gradients. The lateral line plays an important role in many fish behaviors including the detection and localization of dipole sources and the tracking of prey fish. The sensory units of the lateral line are the neuromasts which are distributed across the surface of the animal. Water motions are received and transduced into neuronal signals by the neuromasts. These signals are conveyed by afferent nerve fibers to the fish brain and processed by lateral line neurons in parts of the brainstem, cerebellum, midbrain, and forebrain. In the cerebellum, midbrain, and forebrain, lateral line information is integrated with sensory information from other modalities. The present review introduces the peripheral morphology of the lateral line, and describes our understanding of lateral line physiology and behavior. It focuses on recent studies that have investigated: how fish behave in unsteady flow; what kind of sensory information is provided by flow; and how fish use and process this information. Finally, it reports new theoretical and biomimetic approaches to understand lateral line function.     

Analyzing fish movement as a persistent turning walker

The trajectories of Kuhlia mugil fish swimming freely in a tank are analyzed in order to develop a model of spontaneous fish movement. The data show that K. mugil displacement is best described by turning speed and its auto-correlation. The continuous-time process governing this new kind of displacement is modelled by a stochastic differential equation of Ornstein–Uhlenbeck family: the persistent turning walker. The associated diffusive dynamics are compared to the standard persistent random walker model and we show that the resulting diffusion coefficient scales non-linearly with linear swimming speed. In order to illustrate how interactions with other fish or the environment can be added to this spontaneous movement model we quantify the effect of tank walls on the turning speed and adequately reproduce the characteristics of the observed fish trajectories.     

Deciphering Interactions in Moving Animal Groups

Collective motion phenomena in large groups of social organisms have long fascinated the observer, especially in cases, such as bird flocks or fish schools, where large-scale highly coordinated actions emerge in the absence of obvious leaders. However, the mechanisms involved in this self-organized behavior are still poorly understood, because the individual-level interactions underlying them remain elusive. Here, we demonstrate the power of a bottom-up methodology to build models for animal group motion from data gathered at the individual scale. Using video tracks of fish shoal in a tank, we show how a careful, incremental analysis at the local scale allows for the determination of the stimulus/response function governing an individual''s moving decisions. We find in particular that both positional and orientational effects are present, act upon the fish turning speed, and depend on the swimming speed, yielding a novel schooling model whose parameters are all estimated from data. Our approach also leads to identify a density-dependent effect that results in a behavioral change for the largest groups considered. This suggests that, in confined environment, the behavioral state of fish and their reaction patterns change with group size. We debate the applicability, beyond the particular case studied here, of this novel framework for deciphering interactions in moving animal groups.     

The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows

The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users’ collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What’s more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other.     

Resolving Shifting Patterns of Muscle Energy Use in Swimming Fish

Muscle metabolism dominates the energy costs of locomotion. Although in vivo measures of muscle strain, activity and force can indicate mechanical function, similar muscle-level measures of energy use are challenging to obtain. Without this information locomotor systems are essentially a black box in terms of the distribution of metabolic energy. Although in situ measurements of muscle metabolism are not practical in multiple muscles, the rate of blood flow to skeletal muscle tissue can be used as a proxy for aerobic metabolism, allowing the cost of particular muscle functions to be estimated. Axial, undulatory swimming is one of the most common modes of vertebrate locomotion. In fish, segmented myotomal muscles are the primary power source, driving undulations of the body axis that transfer momentum to the water. Multiple fins and the associated fin muscles also contribute to thrust production, and stabilization and control of the swimming trajectory. We have used blood flow tracers in swimming rainbow trout (Oncorhynchus mykiss) to estimate the regional distribution of energy use across the myotomal and fin muscle groups to reveal the functional distribution of metabolic energy use within a swimming animal for the first time. Energy use by the myotomal muscle increased with speed to meet thrust requirements, particularly in posterior myotomes where muscle power outputs are greatest. At low speeds, there was high fin muscle energy use, consistent with active stability control. As speed increased, and fins were adducted, overall fin muscle energy use declined, except in the caudal fin muscles where active fin stiffening is required to maintain power transfer to the wake. The present data were obtained under steady-state conditions which rarely apply in natural, physical environments. This approach also has potential to reveal the mechanical factors that underlie changes in locomotor cost associated with movement through unsteady flow regimes.     

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks.