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Synchronization: TheKey to EffectiveCommunication inAnimal CollectivesIain D. Couzin 1,2,*

From the rapidly expanding spiralwaves exhibited by colonies ofgiant honeybees to the ripples oflight that cross a turning school offish, synchrony proves essential tothe lives of group-living organisms.Here I consider what we knowabout the mechanisms and adap-tive value of synchronizationamong animals, as well as outlin-ing open questions that, ifanswered, could advance ourunderstanding of the functionalcomplexity of animal collectives.

In the early 1990s, two ant researchers,Nigel Franks in the UK [1] and Blaine Colein the USA [2], made a near-simultaneousobservation during their studies of small,cavity-dwelling ants. While the individualsin these colonies (which number only a fewtensof toa fewhundred individuals) tendedto spend approximately 70% of their timeinactive, sometimes a large proportion ofthe colony was seen to be active simulta-neously. Employing early-developed com-puter vision tools, these researchersdiscovered that colony activity levels fol-lowed a clear rhythm, increasing anddecreasing with a periodicity of approxi-mately 20 min [1,2]. Reminiscent of thewaves of neural activity exhibited in thebrain, the question arose of whether, andif so how, synchronization may enhancethe efficiency and/or the collective compu-tational capabilities of colonies.

Mechanistically, it was demonstrated thatlocal interactions among the ants are suf-ficient to explain the emergence of this

oscillatory activity [3]. Inactive ants, onspontaneously ‘waking’ and becomingactive, move within the nest, contactingand (probabilistically) activating otherswho are inactive. They in turn, if activated,may activate other ants and so on. Thiseffect, combined with a propensity forants to become inactive (especially thosewho have a low frequency of contacts)and the natural relatively restricted motionof ants to individual-specific ‘spatial fidel-ity zones’ in the nest, can account for thewaves of activity seen spreading out-wards from the nest center. However, akey mystery remains: we still do not havea decisive answer as to why these antsexhibit such clear rhythmical activity.

In South and Southeast Asia, colonies ofthe giant honeybee also exhibit synchro-nizedactivity, but here thewavesaremuchfasterandmorecomplex.These largebeesnestonasingle,opencomb,with thework-ers densely populating the comb surface.This open nest presents an opportunity forpredatory wasps. In response to suchthreats, the bees exhibit a remarkable col-lective response; they create ‘shimmering’waves,which formwhena typical subsetofindividuals (which tends to initiate waves)rapidly raise and lower their abdomens,inducing those near them to do similarly(in ‘Mexicanwave’ fashion).Unlike theants,butmuch likeneuronsandother ‘excitable’cells [4], individual bees appear to exhibit a‘refractory’ period whereby once an indi-vidual has raised and lowered its abdomenthere is a short period in which it appearsinsensitive to its neighbors. The resultingwaves are rapid and highly visible andpropagate across the colony surface asa series of expanding rings or spirals in afraction of a second (Figure 1A), startlingand repelling preying wasps [5].

Such collective properties are not unique;patterns of synchronous activity havebeen found in almost every animal groupstudied, from the simplest multicellular

animals (Placozoa) to humans. Synchronyplays a role (over a wide range of time-scales) in almost every aspect of groupbehavior. For example, to maintain thebenefits of group living, organisms onthe move must synchronize their deci-sions about when, and where, to moveto find food and appropriate habitats andto avoid threats (Figure 1B,C). The speedat which waves of turning can propagateacross bird flocks (e.g., in the dramatic‘murmuration’ of starlings; see Figure I inBox 1 [6]) led to the belief in the early 20thcentury that synchrony must be facilitatedby telepathy, or the ability of the brain todetect directly synchronized muscleactivity in others. While much remains amystery, in recent years advanced imag-ing techniques that allow automatedtracking of individuals within groups, bothin the laboratory and in the wild [7], arebeginning to reveal some key principles.

For example, studies of the propagationof behavior in fish, birds, and humanshave demonstrated that despite the vastdifferences among these organisms,there is a fundamental commonality inthe mechanism by which behaviorspreads. Unlike the spreading of disease(where a single source can be sufficientfor transmission and spreading occurs viaindependent exposures to infected indi-viduals), for behavior to spread individualsrequire reinforcement from multiple indi-viduals. Reinforcement tends to dependnot on the absolute number of other indi-viduals exhibiting a behavior but on thefraction of perceived individuals exhibitinga certain behavior [8,9]. In addition, thereis evidence from both fish and humansthat the influence individuals have alsodepends on the structure of social net-works. In Facebook, for example, rein-forcement (the probability of joining orengaging with others on the social net-work) tends to be stronger if the reinforc-ing individuals are perceived as belongingto different social cliques (and thus may

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provide more independent, less-corre-lated information) [10]. In schooling fish,analysis of the evolved network of socialinfluence reveals that it is structured toreduce the probability that individualsobtain correlated (redundant) informationfrom others [11]. In both scenarios thiscan benefit individuals since the utility ofobtaining information frommultiple others(the ‘wisdom of crowds’) is eroded if thatinformation is correlated.

Explaining the remarkable speed atwhich information propagates in someanimal groups, such as starlings and sil-verside fish, however, remains a keychallenge. One proposal is that theimportance of collective informationtransfer (e.g., regarding predators) is soprofound that such systems have

evolved to be in a special ‘critical’ regime(Box 1; [6]). Another, not mutually exclu-sive, hypothesis is that, much like whenhumans play the ‘mirror game’ in whichparticipants are instructed to mirror themotion of others [12], individuals mayreduce the time delays of communica-tion, and thus enhance synchrony withothers, by interacting not with the currentstate of the system (e.g., the currentpositions and velocities of others) butrather with a projected ‘future state’(the projected future positions and/orvelocities of neighbors). Biologically sucha mechanism is plausible for nonhumananimals; archer fish, for example, havebeen shown to be able to predict com-plex 3D target motion. It would be fasci-nating to determine whether organismsin groups are similarly responding to a

projected future state of the world, and ifso whether this enhances the speed ofbehavioral synchronization.

The study of animal groups offers greatpotential to reveal more clearly the rolethat synchronization plays in informationprocessing. Many groups are highly ame-nable to manipulative experimentation:they can be taken apart and put backtogether again to reveal how, and why,the components influence one another.Considerable scope exists to explorenew questions, such as the interplaybetween behavioral and physiologicalsynchronization, about which we knowvery little. We must strive to develop suchan integrative perspective, as doing sowillcontribute enormously to our understand-ing of collective phenomena.

(A)

(C)

0 ms 150 ms

300 ms 450 ms

600 ms 750 ms

(B)

Figure 1. Examples of Synchronized Behavior in Animal Collectives. (A) An image sequence, with intervals of 150 ms, showing the propagation of a spiral wavein a colony of giant honeybees (Apis dorsata) (filmed by Gerald Kastberger) [5]. (B) A flock of bronzewing pigeons (Phaps histrionica) taking flight together in innerAustralia. Photograph courtesy of Damien Farine. (C) Visualization of the trajectories of 150 schooling golden shiner fish (Notemigonus crysoleucas) demonstrating thesynchrony of their movements (by Vivek Sridhar and Matt Grobis).

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AcknowledgmentsThe author gratefully acknowledges support from the

NSF (IOS-1355061), the ONR (N00014-09-1-1074

and N00014-14-1-0635), the ARO (W911NG-11-1-

0385 and W911NF14-1-0431), and the Max Planck

Society.

1Department of Collective Behaviour, Max Planck Institute

for Ornithology, Konstanz, Germany2Department of Biology, University of Konstanz,

Konstanz, Germany

*Correspondence: icouzin@orn.mpg.de (I.D. Couzin).

https://doi.org/10.1016/j.tics.2018.08.001

References

1. Franks, N.R. et al. (1990) Synchronization of thebehaviour within nests of the ant Leptothorax acervorum(Fabricius) – I. Discovering the phenomenon and itsrelation to the level of starvation. Bull. Math. Biol. 52,597–612

2. Cole, B.J. (1991) Short-term activity cycles in ants: gener-ation of periodicity by worker interaction. Am. Nat. 137,244–259

3. Miramontes, O. et al. (1993) Collective behaviour of ran-dom-activated mobile cellular automata. Physica D 63,145–160

4. Strogatz, S. (2004) Sync: The Emerging Science of Spon-taneous Order, Penguin

5. Kastberger, G. et al. (2008) Social waves in giant honey-bees repel hornets. PLoS One 3, e3141

6. Bialek, W. et al. (2014) Social interactions dominate speedcontrol in poising natural flocks near criticality. Proc. Natl.Acad. Sci. U. S. A. 111, 7212–7217

7. Dell, A.I. et al. (2014) Automated image-based trackingand its application in ecology. Trends Ecol. Evol. 29, 417–428

8. Centola, D. (2010) The spread of behavior in an onlinesocial network experiment. Science 329, 1194–1197

9. Rosenthal, S.B. et al. (2015) Revealing the hidden net-works of interaction in mobile animal groups allows pre-diction of complex behavioral contagion. Proc. Natl. Acad.Sci. U. S. A. 112, 4690–4695

10. Ugander, J. et al. (2012) Structural diversity in social con-tagion. Proc. Natl. Acad. Sci. U. S. A. 109, 5962–5966

11. Strandburg-Peshkin, A. et al. (2013) Visual sensory net-works and effective information transfer in animal groups.Curr. Biol. 23, R709–R711

12. Noy, L. et al. (2011) The mirror game as a paradigm forstudying the dynamics of two people improvising motiontogether. Proc. Natl. Acad. Sci. U. S. A. 108, 20947–20952

Special Issue: Time in the Brain

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Stuck in the Present?Constraints onChildren’s EpisodicProspectionSimona Ghetti1,2,* andChristine Coughlin3

The examination of children’sability to simulate their future hasgained increased attention, andrecent discoveries highlight limita-tions in this ability that extendinto adolescence. We propose anaccount for this protracted devel-opmental trajectory, which encom-passes consideration of retrievalflexibility across timescales andself-knowledge. We also identifyavenues for future research.

We spend considerable time imaginingwhat our future might bring, savoring adesired turn of events or dreading theopposite. The mental simulation of afuture event sometimes includes so much

Box 1. Synchrony and ‘Criticality’ in Animal Groups

The extremely high speed at which behavioral change (e.g., turning) or density propagates across flocks ofbirds (Figure 1B and Figure I) or schools of fish (Figure 1C), along with certain statistical properties observedin such groups (e.g., the presence of long-range correlations in individuals’ velocity despite the presence ofhighly local interactions), has caused some researchers to speculate that such animal groups, as haspreviously been suggested for the brain, are ‘poised near criticality’ [6]. Taken from statistical mechanics, thetheory of critical phenomena demonstrates that certain generic properties appear in a collective system, be itof physical particles, neurons, or birds, when local interactions are tuned in a certain way. Near the criticalpoint, remarkable properties emerge spontaneously, such as individuals’ behavior becoming correlatedirrespective of the distance between them (which is mathematically equivalent to information being able topropagate almost without loss over the entire structure). Biological systems may benefit from being close toa critical state in a variety of contexts since they must often satisfy two, seemingly opposing survivalconditions: to respond quickly to changing environmental conditions, such as the appearance of a predator,and to remain robust and organized in the face of noise (e.g., in the case of fish or birds, eddies or gusts ofwind, respectively). While still controversial, criticality provides a fascinating, plausible, and increasinglytestable hypothesis for effective information processing in large collectives.

Figure I. A ‘Murmuration’ of Starlings (Sternus vulgaris). Here the flock is being attacked by aperegrine falcon (Falco peregrinus) just to the left of the center of the flock. Photograph courtesy of theCOBBS Laboratory, Institute for Complex Systems, National Research Council, Rome, Italy.

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