A plausible mechanism for Drosophila larvaintermittent behavior.Panagiotis Sakagiannis1,*, Miguel Aguilera2, and Martin Paul Nawrot1

1Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Germany; 2Department of Informatics. University of Sussex. Brighton, UK

This manuscript was compiled on September 19, 2020

The behavior of many living organisms is not continuous. Rather,activity emerges in bouts that are separated by epochs of rest, aphenomenon known as intermittent behavior. Although intermit-tency is ubiquitous across phyla, empirical studies are scarce andthe underlying neural mechanisms remain unknown. Here we re-produce empirical evidence of intermittency during Drosophila larvafree exploration. Our findings are in line with previously reportedpower-law distributed rest-bout durations while we report log-normaldistributed activity-bout durations. We show that a stochastic net-work model can transition between power-law and non-power-lawdistributed states and we suggest a plausible neural mechanism forthe alternating rest and activity in the larva. Finally, we discuss pos-sible implementations in behavioral simulations extending spatialLevy-walk or coupled-oscillator models with temporal intermittency.

larva crawling | Levy-walks | neuronal avalanches

The search for statistical regularities in animal movementis a predominant focus of motion ecology. Random walks

form a broad range of models that assume discrete stepsof displacement obeying defined statistical rules and acutereorientations. A Levy walk is a random walk where the dis-placement lengths and the respective displacement durationsare drawn from a heavy-tailed, most often a power-law distri-bution. When considered in a 2D space reorientation anglesare drawn from a uniform distribution. This initial basicLevy walk has been extended to encompass distinct behavioralmodes bearing different go/turn parameters, thus termed com-posite Levy walk. Levy walks have been extensively studiedin the context of optimal foraging theory. A Levy walk with apower-law exponent between the limit of ballistic (α = 1) andbrownian motion (α = 3) yields higher search efficiency forforagers with an optimum around α = 2 when search targetsare patchily or scarcely distributed and detection of a targethalts displacement (truncated Levy walk) (1).

Nevertheless, the underlying assumption of non-intermittent flow of movement in Levy walk modelscomplicates the identification of the underlying generativemechanisms as they focus predominantly on reproducingthe observed spatial trajectories, neglecting the temporaldynamics of locomotory behavior. Therefore, Bartumeus(2009) stressing the need for a further extension coined theterm intermittent random walk, emphasizing the integrationof behavioral intermittency in the theoretical study of animalmovement (2). Here we aim to contribute to this goal bystudying the temporal patterns of intermittency duringDrosophila larva free exploration in experimental data andin a conceptual model, bearing in mind that power-law likephenomena can arise from a wide range of mechanisms,possibly involving processes of different timescales (1).While our study remains agnostic towards whether foragersreally perform Levy walks - a claim still disputed (1) - we

suggest that intrinsic motion intermittency should be takeninto account and the assumption of no pauses and acutereorientations should be dropped in favor of integrativemodels encompassing both activity and inactivity.

Drosophila larva is a suitable organism for the study ofanimal exploration patterns and the underlying neural mech-anisms. A rich repertoire of available genetic tools allowsacute activation, inhibition or even induced death of specificneural components. Crawling in 2D facilitates tracking ofunconstrained behavior. Also, fruit flies during this life stageare nearly exclusively concerned with foraging. Therefore afood/odor-deprived environment can be largely consideredstimulus-free, devoid of reorientation or pause sensory triggers,while target-detection on contact can be considered certain.Truncated spatial Levy-walk patterns of exploration with ex-ponents ranging from 1.5 to near-optimal 1.96 that hold overat least two orders of magnitude have been previously reportedfor the Drosophila larva. The turning-angle distribution, how-ever, was skewed in favor of small angles and a quasi-uniformdistribution was observed only for reorientation events ≥ 50◦

(3, 4). Moreover, it has been shown that these patterns arisefrom low-level neural circuitry even in the absence of sensoryinput or brain-lobe function and have therefore been termed‘null movement patterns’ (3, 4).

Previous empirical studies on larva intermittent locomotorybehavior have concluded that the distribution of durationsof rest bouts is power-law while that of activity bouts hasbeen reported to be exponential (5) or power-law (4). Geneticintervention has revealed that dopamine neuron activationaffects the activity/rest ratio via modulation of the power-lawexponent of the rest bouts, while the distribution of activitybouts remains unaffected. This observation hints towardsa neural mechanism that generates the alternating switchesbetween activity and rest where tonic modulatory input fromthe brain regulates the activity/rest balance according toenvironmental conditions and possibly homeostatic state.

Here we analyze intermittency in a large experimentaldataset and present a conceptual model that generates alter-nation between rest and activity, capturing the empirically ob-served power-law and non-power-law distributions. We discussa plausible neural mechanism for the alternation between restand activity and the regulation of the animal’s activity/restratio via modulation of the rest-bout power-law exponent bytop-down modulatory input. Our approach seeks to elaborateon the currently prevailing view that these patterns result

Conceptualization, P.S., M.A. and MP.N.; Methodology, P.S and M.A.; Writing – Original Draft, P.S.Writing – Review and Editing, P.S., M.A. and MP.N.

The authors do not declare any conflicts of interest.

*To whom correspondence should be addressed.e-mail : p.sakagiannis@uni-koeln.dewebpage : http://computational-systems-neuroscience.de/

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Table 1. Datasetdescription andempirical resultsfor rest/activitybout analyses.

from intrinsic neural noise (4).

Materials and Methods

Experimental dataset. We use a larva-tracking dataset avail-able at the DRYAD repository, previously used for spatialLevy-walk pattern detection (3). The dataset consists of upto one hour long recordings of freely moving larvae trackedas a single point (centroid) in 2D space. We consider threetemperature-sensitive shibirets fly mutants allowing for in-hibition of mushroom-body (MB247),brain-lobe/SOG (BL)or brain-lobe/SOG/somatosensory (BLsens) neurons and anrpr/hid mutant line inducing temperature-sensitive neuronaldeath of brain-lobe/SOG/somatosensory (BLsens) neurons.Each mutant expresses a different behavioral phenotype whenactivated by 32◦-33◦ C temperature. We compare phenotypicbehavior to control behavior in non-activated control groups.A reference control group has been formed consisting of allindividuals of the four 32◦-33◦ C control groups (Tab. 1).

For the present study recordings longer than 1024 secondshave been selected. Instances where larvae contacted thearena borders were excluded. The raw time series of x,y coor-dinates have been forward-backward filtered with a first-orderbutterworth low-pass filter of cutoff frequency 0.1Hz beforecomputing the velocity. The cutoff frequency was selectedas to preserve the plateaus of brief stationary periods whilesuppressing the signal oscillation due to peristaltic-stride cy-cles. Velocity values ≥ 2.5mm/sec have been discarded toaccount for observed jumps in single-larva trajectories thatare probably due to technical issues during tracking. Thisarbitrary threshold was selected as an upper limit for larvae oflength up to 5mm, crawling at a speed of up to 2 strides/secwith a scaled displacement per stride of up to 0.25.

Bout annotation. In order to designate periods of rest and ac-tivity we need to define a suitable threshold Vθ in the velocitydistribution as in (5). We used the density estimation algo-rithm to locate the first minimum Vθ = 0.085mm/sec in thevelocity histogram of the reference control group. A rest boutis then defined as a period during which velocity does not ex-ceed Vθ. Rest bouts necessarily alternate with periods termedactivity bouts. The bout annotation method is exemplifiedfor a single larva track in Fig. 1.

Bout distribution. To quantify the duration distribution of therest and activity bouts we used the maximum likelihood es-timation (MLE) method to fit a power-law, an exponential

and a log-normal distribution for each group as well as forthe reference control group. Given the tracking framerateof 2 Hz and the minimal tracking time of 1024 seconds, welimited our analysis to bouts of duration 21 to 210 seconds.The Kolmogorov-Smirnov distance DKS for each candidatedistribution was then computed over 64 logarithmic bins cov-ering this range. Findings are summarized in Tab. 2 for therest bouts and in Tab. 3 for the activity bouts.

Results

The results section is organized as follows. Initially wepresent a simple conceptual two-state model transitioningautonomously between power-law and non-power-law regimes.Next we attempt to reproduce existing findings (5) by study-ing intermittency during larva free exploration in a differentdataset (3). Finally we compare mutant and control larvaphenotypes in the context of intermittency.

Network model of binary units reproduces larval statistics ofintermittent behavior. Previous work on Drosophila larva in-termittent behavior reported that rest-bout durations arepower-law distributed while activity-bout durations are expo-nentially distributed (5). Our first contribution is to providea simple model displaying how this dual regime might emerge.

We define a kinetic Ising model with N = 1000 binaryneurons, with homogeneous all-to-all connectivity (Fig. 2A).Each neuron i is a stochastic variable with value si(t) at timet that can be either 1 or 0 (active or inactive). We assumethat this neuron population inhibits locomotory behavior, sothat when

∑isi(t) > 0 the larva is in the rest phase, and

otherwise the larva remains active .At time t+ 1, each neuron’s activation rate is proportional

to the sum of activities at time t, and will be activated with alinear probability function pi(t+ 1) = σ

N

∑jsj(t) + µ

N. Here,

σ is the propagation rate, which indicates that when a nodeis active at time t, it propagates its activation at time t + 1on average to σ other neurons. When one neuron is activated,this model behaves like a branching process (6), with σ asthe branching parameter. If σ < 1, activity tends to decreaserapidly until all units are inactive while, if σ > 1, activitytends to be amplified until saturation. At the critical point,σ = 1, activity is propagated in scale-free avalanches, in whichduration d of an avalanche once initiated follows a power-law distribution P (d) ∼ d−α (Fig. 2B, left), governed by acritical exponent (α = 2 at the N →∞ limit) describing howavalanches at many different scales are generated.

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A

B

C

DFig. 1. Bout annotation methodology. A:Individual larva trajectory. Spatial scaleand recording duration are noted. B: Ve-locity distribution for the single larva. Thethreshold obtained from the referencegroup, used for rest vs activity bout an-notation is denoted by the arrow. C: Theentire velocity time series of the larva.Rest and activity bouts are indicated bydifferent background colors. D: Magnifi-cation of the velocity time series.

When an avalanche is extinguished, the system returns toquiescence which is only broken by the initiation of a newavalanche. With a residual rate µ = 0.01 the system becomesactive by firing one unit and initiating a new avalanche. In thiscase the duration of quiescence bouts (the interval between twoconsecutive avalanches) follows an exponential distribution(Fig. 2B, right).

This simple conceptual model alternates autonomously be-tween avalanches of power-law distributed durations and qui-escence intervals of exponentially distributed durations. Thisalternation between power-law and non-power-law regimes canserve as a basic qualitative model of the transition betweenrest and activity bouts in the larva (cf. Discussion).

A si

σRest, if Σsi >0{Active, otherwise

B

100 101 102

10−5

10−4

10−3

10−2

10−1

100

rest

rest bouts

powerlaw MLE

100 101 102

activity

non-rest bouts

exponential MLE

0.0 0.2 0.4 0.6 0.8 1.0

duration, d(sec)

0.0

0.2

0.4

0.6

0.8

1.0

prob

abili

ty,Pd

Fig. 2. Probability distribution of the duration d of rest and activity phases in abranching process model of σ = 1, simulated over 105 occurrences of each phase.Duration is measured as the number of updates until a phase is ended. Unit activationsi(t) propagates to neighbouring units creating self-limiting avalanches. In therest phase, when

∑isi(t) > 0, the system yields a power law distribution with

exponent α ≈ 2. In the activity phase, when∑

isi(t) = 0, one unit of the

system is activated with probability µ = 0.01, yielding an exponential distributionwith coefficient λ = 0.1.

Parameterization of larval intermittent behavior. We analyzedintermittent behavior during larval crawling in a stimulus-free environment (cf. Materials and Methods for datasetdescription). Each individual larva was video-tracked in space

(Fig. 1A). From the time series of spatial coordinates we com-puted the instantaneous velocity and determined a thresholdvalue (Fig. 1B) that separates plateaus of continued activity(activity bouts) from epochs of inactivity (rest bouts, Fig. 1C-D) following the analyses suggested in (5).

We start out with the analysis of experimental controlgroups that were not subjected to genetic intervention. Asa first step we computed the number of occurrences of restand activity bouts and the activity ratio, which quantifies theaccumulated activity time as fraction of the total time (Tab. 1).For the reference control group we obtain an activity ratio of0.83 albeit with a fairly large variance across individuals.

For the duration distribution of rest bouts we find that it isbest approximated by a power-law distribution in all six controlgroups (Tab. 2) confirming previous results on independentdatasets (4, 5). The empirical duration distribution of rest-bouts across the reference control group is depicted in Fig. 3A(red dots). Again, the power law provides the best distributionfit. The exponent α of the power law ranges from 1.514 to1.938 with α = 1.598 for the reference control group.

When analyzing the durations of activity bouts we foundthat these are best approximated by a log-normal distributionin all groups (Tab. 3). This result is surprising as previouswork suggested the mode of an exponential distribution (5).For the reference control group Fig. 3B compares the empiricalduration distribution of activity bouts with the fits of the threedistribution functions.

100 101 102 103

10 5

10 4

10 3

10 2

10 1

100

restpowerlawexponentiallognormal

100 101 102 103

activitypowerlawexponentiallognormal

duration, d(sec)

prob

abilit

y , P

d

Fig. 3. Probability density of rest and activity bout durations for the reference controlgroup. Dots describe the probability density over logarithmic bins. Lines are the bestfitting power-law, exponential and log-normal distributions. The thick line denotes thedistribution having the minimum Kolmogorov-Smirnov distance DKS (Tab. 2 - 3).

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Table 2. Distri-bution parameterfits of empiricalrest bout duration.The relevant pa-rameters for thebest fitting distri-bution are indi-cated in bold text.

Table 3. Distribu-tion parameter fitsof empirical activ-ity bout duration.The relevant pa-rameters for thebest fitting distri-bution are indi-cated in bold text.

Modification of rest and activity bout durations in mutantflies. Behavioral phenotypes in genetic mutants can help iden-tify brain neuropiles in the nervous system of Drosophila larvathat are involved in the generation of intermittent behavior,or that have an effect on its modulation. To this end we ana-lyzed 4 experimental groups where genetic intervention wascontrolled by temperature either via the temperature-sensitiveshibire protocol or via temperature-induced neuronal death(rpr/hid genotype). Each group is compared to a non-activatedcontrol group as shown in Fig. 4 and described in Tab. 1.

Interestingly, genetic intervention can have a large effecton the activity ratio. When inactivating sensory neurons andto a lesser extend the mushroom body the activity ratio isdecreased (cf. BLsens > rpr/hid, MB247 > shits and BLsens> shits in Tab. 1). Inspection of the empirical duration distri-bution of rest bouts in Fig. 4 (first and third columns) showsthat while the power-law fit is superior for all control groups,the log-normal fit approximates best the respective mutantdistribution in 3 out of 4 cases (cf. MB247 > shits, BL > shits

and BLsens > shits in Tab. 2. This might hint impairment ofthe power-law generating processes due to neural dysfunction.In the fourth case of BLsens > rpr/hid the power-law is pre-served but shifted to higher values. Regarding activity, theempirical distributions indicate that overall the activity epochsare severely shortened in time for both the BLsens > rpr/hidand the BLsens > shits mutants in comparison to the respec-tive control groups (second and fourth columns) hinting earlytermination of activity bouts by the intermittency mechanism

Discussion

As most neuroscientific research focuses either on static net-work connectivity or on neural activation/inhibition - behaviorcorrelations, an integrative account of how temporal behavioralstatistical patterns arise from unperturbed neural dynamics isstill lacking. In this context, we hope to contribute to scientificdiscovery in a dual way. Firstly by extending existing mecha-nistic hypothesis for larva intermittent behavior and secondlyby promoting the integration of intermittency in functionalmodels of larval behavior. In what follows we elaborate onthese goals and finally describe certain limitations of our study.

Self-limiting inhibitory waves might underlie intermittentcrawling and its modulation. The neural mechanisms underly-ing intermittency in larva behavior remain partly unknown.Displacement runs are intrinsically discretized, comprised ofrepetitive, stereotypical peristaltic strides. These stem fromsegmental central pattern generator circuits (CPG) locatedin the ventral nerve chord, involving both excitatory andinhibitory premotor neurons and oscillating independentlyof sensory feedback (7). A ‘visceral pistoning’ mechanisminvolving head and tail-segment synchronous contraction un-derlies stride initiation (8). Speed is mainly controlled viastride frequency (8).Crawling is intermittently stopped dur-ing both stimulus-free exploratory behavior and chemotaxis,giving rise to non-stereotypical stationary bouts during whichreorientation might occur. During the former they are intrin-sically generated without need for sensory feedback or braininput (3), while during the latter an olfactory-driven sensori-

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10 5

10 4

10 3

10 2

10 1

100

rest

BLsens > rpr/hidcontrol

BLsens > rpr/hid

activity

BLsens > rpr/hidcontrol

BLsens > rpr/hid

100 101 102 103

10 5

10 4

10 3

10 2

10 1

100

MB247/ +MB247 > shits

100 101 102 103

MB247/ +MB247 > shits

duration, d(sec)

prob

abilit

y, P

d

10 5

10 4

10 3

10 2

10 1

100

rest

22oshits/ +33oshits/ +22oBL/ +33oBL/ +

activity

22oshits/ +33oshits/ +22oBL/ +33oBL/ +

100 101 102 103

10 5

10 4

10 3

10 2

10 1

100

BL > shits

BLsens > shits

100 101 102 103

BL > shits

BLsens > shits

duration, d(sec)

prob

abilit

y, P

d

Fig. 4. Probability density of rest andactivity bout durations for control andactivated mutant genotypes. In the firsttwo diagram pairs mutants are plottedagainst their single respective controls.In the fourth pair the rest two mutantsare plotted with their 4 control groupsshown in the third diagram pair. Dotsdescribe the probability density over log-arithmic bins. Lines indicate the dis-tribution with the lowest Kolmogorov-Smirnov distance DKS among thebest fitting power-law, exponential andlog-normal distributions for each group(Tab. 2 - 3).

motor pathway facilitates cessation of runs when navigatingdown-gradient. Specifically, inhibition of a posterior-segmentpremotor network by a sub-esophageal zone descending neurondeterministically terminates runs allowing easier reorientation(9).

It is reasonable to assume that this intermittent crawlinginhibition is underlying both free exploration and chemotaxis,potentially in the form of transient inhibitory bursts. A neu-ral network controlling the CPG through generation of self-limiting inhibitory waves is well suited for such a role. In thesimplest case, during stimulus-free exploration, the durationsof the generated inhibitory waves should follow a power-lawdistribution, behaviorally observed as rest bouts. In contrast,non-power-law distributed quiescent periods of the networkwould disinhibit locomotion allowing the CPG to generaterepetitive peristaltic strides resulting in behaviorally observedruns.

The model we presented (cf. 3.1) alternates autonomouslybetween avalanches of power-law distributed durations andquiescence intervals of exponentially distributed durationswithout need for external input. Therefore it can serve asa theoretical basis for the development of both generativemodels that reproduce the intermittent behavior of individ-ual larvae and of the above mechanistic hypothesis for theinitiation and cessation of peristaltic locomotion in the larvathrough disinhibition and inhibition of the crawling CPG re-spectively. To uncover the underlying neural mechanism andconfirm/reject our hypothesis, inhibitory input to the crawlingCPG should be sought, measured neurophysiologically andcorrelated to behaviorally observed stride and stride-free boutsduring stimulus-free exploration.

Intermittent behavior in the Drosophila larva is subject totwo modes of modulation, neither of which affects the distribu-tion of the activity bouts. Firstly, high ambient temperatureand daylight raise the activity ratio over long timescales byraising the number of activity bouts (5). This is achievedby lowering the probability of the extremely long rest bouts,without affecting the power-law exponent of the distribution,which coincides with fewer sleep events (> 5 minutes) ob-served during the day. This modulation is long-lasting andcould result from a different constant tonic activation of thesystem. Secondly, dopamine neuron activation raises the ac-tivity ratio acutely by modulation of the power-law exponentupwards (5) skewing locomotion towards the brownian limit.

This modulation could be transient in the context of salientphasic stimulation by the environment. As mentioned above,during chemotaxis larvae perform more and sharpest reorien-tations, terminating runs when navigating down-gradient. Ahypothesis integrating both experimental findings could bethen that this behavior stems from transient olfactory-drivendopaminergically-modulated inhibition of the crawling CPG.Our conceptual model can be extended to address the aboveclaims by adding tonic and/or phasic input.

Intermittency can extend functional models of larva locomo-tion. Traditional random walk models fail to capture the tem-poral dynamics of animal exploration (1). Even when time istaken into account in terms of movement speed, reorientationsare assumed to occur acutely. Integrating intermittency canaddress this limitation allowing for more accurate functionalmodels of autonomous behaving agents. Such virtual agentscan then be used in simulations of behavioral experimentspromoting neuroscientifically informed hypothesis that ad-vance over current knowledge and generate predictions thatcan stimulate further empirical work (10).

It is widely assumed that Drosophila larva exploration canbe descibed as a random walk of discrete non-overlapping runsand reorientations/head-casts (3) or alternatively that it isgenerated by the concurrent combined activity of a crawlerand a turner module generating repetitive oscillatory forwardperistaltic strides and lateral bending motions respectivelyand possibly involving energy transfer between the two me-chanical modes (11–13). Both models can easily be upgradedby adding crawling intermittency which might or might not beindependent of the lateral bending mechanism. In the discrete-mode case, intermittency can simply control the duration andtransitions between runs and head-casts or introduce a thirdmode of immobile pauses resulting in a temporally unfoldingrandom walk. In the overlapping-mode case the two modulesare complemented by a controlling intermittency module form-ing an interacting triplet. Depending on the crawler-turnerinteraction and the effect of intermittency on the turner mod-ule, multiple locomotory patterns emerge including straightruns, curved runs, stationary head-casts and immobile pauses.This simple extension would allow temporal fitting of genera-tive models to experimental observations in addition to theprimarily pursued spatial-trajectory fitting, facilitating theuse of calibrated virtual larvae in simulations of behavioral

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experiments.

Limitations. A limitation of our study is that due to the single-spinepoint tracking, it is impossible to determine whethermicro-movements occur during the designated inactivity peri-ods, an issue also unclear in (5). It follows that in both ouranalysed dataset and in (5), immobile pauses, feeding motionsand stationary head casts are indistinguishable. Therefore,what we define as rest bouts should be considered as periodslacking at least peristaltic strides but not any locomotory activ-ity. Our relatively low velocity threshold Vθ = 0.085mm/secthough allows stricter detection of rest bouts as it is evidentfrom the higher activity ratio (higher than 0.7 in most controlgroups in comparison to lower than 0.25 in (5)). To tacklethis, trackings of higher spatial resolution with more spine-points tracked per larva are needed, despite the computationalchallenge of the essentially long recording duration.

Also, our results show that an exponential distribution ofactivity bouts (5) might not always be the case, as we detectedlognormal long-tails in all cases. Still, the exponential-power-law duality in our model illustrates switching between indepen-dent and coupled modes of neural activity. Substituting theexponential regime by other long-tailed distribution such aslog-normal might require assuming more complex interactionsbetween the switching regimes and will be pursued in thefuture so that generative models of the data can be fit.

ACKNOWLEDGMENTS. This project was supported by the Re-search Training Group ‘Neural Circuit Analysis’ (DFG-RTG 1960,grant no. 233886668) and the Research Unit ‘Structure, Plasticityand Behavioral Function of the Drosophila mushroom body’(DFG-FOR 2705, grant no. 403329959), funded by the German ResearchFoundation. M.A. was funded by the UPV/EHU post-doctoral train-ing program ESPDOC17/17 and H2020 Marie Skłodowska-Curiegrant 892715, and supported in part from the Basque Government(IT1228-19). We also thank Dr. Jimena Berni for providing anupdated version of the larva-tracking dataset, on our request.

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