Actions have consequences; positive consequences or rewards make it more likely that a behavior will be repeated, while negative consequences or punishments can stop a behavior occurring again. Neuroscientists commonly refer to such rewards and punishments as ‘reinforcement’.

Fruit flies that are given a reward of sugar when they experience an odor will move towards the odor in later tests. However, in 2011, research revealed that if the flies were given at least the same amount of sugar in the tests as they were rewarded with during the earlier training, the flies stopped moving towards the odor. This suggests that fruit flies can recall how strong a reward was in the past and compare this remembered strength to the current reward on offer; fruit flies will only continue searching if they expect to gain a larger reward by doing so.

Insects were commonly thought to only learn the amount or ‘value’ of reinforcement, but not recall what kind or ‘quality’ of reward (or punishment) they had experienced. Now Schleyer et al.—including some of the researchers involved in the 2011 work—challenge and extend this notion and show that fruit fly larvae can remember both the value and quality of rewards and punishments.

Fruit fly larvae were trained to expect a reward of sugar when exposed to one odor and nothing when exposed to a different odor. Consistent with the previous results, the larvae moved towards the first odor in the tests where no additional reward was provided. Moreover, the larvae did not move towards the odor in later tests if an equal or greater amount of sugar was provided during the testing stage.

Schleyer et al. then took larvae that had been trained to expect a sugar reward and gave them a different, but equally valuable, reward during the testing stage—in this case, the reward was an amino acid called aspartic acid. These experiments revealed that most of the larvae continued to move towards the sugar-associated odor in search of the sugar reward. This indicates that the larvae were able to remember the quality of the reward, namely that it was sugar rather than aspartic acid.

Schleyer et al. performed similar experiments, and observed similar results, when using two different punishments: bitter-tasting quinine and high concentrations of salt. These findings show that experiencing an odor along with taste reinforcement could set up a memory specific to the quality of reinforcement in fruit fly larvae. Given the numerical simplicity of a larva's brain—which contains only 10,000 neurons—it is likely that other animals can also recall both the value and quality of a reward or punishment. However, understanding how such specificity comes about should be easier in the larva's simple brain.

What are the fundamental capacities of insect brains? To date, little use has been made of insect memory experiments to reveal these capacities, in particular regarding reinforcement. For example, after experiencing an odor with a sugar reward, fruit flies (Drosophila melanogaster) approach that odor in a later test. All the known circuitry (Heisenberg, 2003; Perisse et al., 2013) of such learned search behavior suggests that this is because the odor has acquired positive value; that is, that the flies are expecting to find ‘something good’ in its vicinity (Heisenberg, 2003; Gerber and Hendel, 2006; Schleyer et al., 2011; Perisse et al., 2013). Likewise, Drosophila can associate an odor with an electric-shock punishment. This supports their learned escape in a later test because they may expect ‘something bad’ with the odor. In other words, the only feature of reinforcement processing that insects are granted is value. We show that larval Drosophila are in a defined sense richer than this in their mnemonic capacity: they also recall of what particular quality that good or bad experience was. Given the numerical simplicity of the larval brain, this is suggested to be a more basic property of brains than hitherto assumed.

To address this question, we exploit an established assay for Pavlovian conditioning (Gerber et al., 2013) that allows reinforcers of various strength and quality to be used. In this Petri dish assay, larvae are placed onto a tasteless agarose substrate. This substrate is supplemented with a fructose sugar reward—if odorant A is presented. Odorant B is presented without the reward (A+/B). For a companion group of larvae, contingencies are reversed (A/B+). In a binary choice test, the larvae then systematically approach the previously rewarded odorant (Figure 1—figure supplement 1). This behavior, quantified as a positive associative performance index (PI) (Figure 1A), can best be grasped as a memory-based search for reward: if the test is performed in the presence of fructose, the learned approach is abolished (Figure 1A) (Gerber and Hendel, 2006; Schleyer et al., 2011) (olfactory behavior per se is not affected: see below). This is adaptive as learned search behavior is indeed obsolete in the presence of a sought-for item. We have previously shown (Schleyer et al., 2011) that regardless of the absolute concentration of fructose, such an abolishment is seen if the fructose concentration in the test substrate is equal to or higher than that used in training. This means that learned behavior is based on a relative assessment: the larvae recall how strong the training reward was and compare this remembered strength to the current testing situation. Only if that comparison promises a gain (remembered strength > current strength) do they search for what they can thus expect to gain at the odor source. We note that widely applied formal learning models of the Rescorla-Wagner type (Rescorla and Wagner, 1972) propose that memory acquisition will only occur if something new and unexpected happens, specifically if the experienced reward is stronger than predicted on the basis of memory (current strength > remembered strength). Thus, the same two pieces of information are compared during memory acquisition on the one hand and the expression of learned search behavior on the other hand—yet in a ‘swapped’ way. This can inform the animals respectively about what is new or what there is to be gained. Here, we ask whether these processes are integrated across different qualities of reward into one common scale of appetitive value or whether separate systems exist to confer mnemonic specificity for the ‘quality’ of reward.

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We introduce aspartic acid, a proteinogenic amino acid, as a novel quality of reward (Figure 1—figure supplement 2). Concentrations of aspartic acid and fructose are chosen such that their reward value is equal (Figure 1A). This allows us to study the larvae under test conditions that are of equal reward value but that either match or do not match the ‘quality’ of reward that has been employed during training. If the larvae merely searched for a reward of remembered value, learned search behavior should cease in both cases—because current strength matches remembered strength in both cases. If reward quality were the sole determinant for learned search, in contrast, learned search should be abolished only when test and training substrate match in quality, but should remain intact when quality does not match. If the larvae searched for a reward specified both by its value and by its quality, scores in the mismatch case should be partially abolished: in that case, the reward's value is as sought yet its quality is not. We find that learned search is fully abolished when the training and test reward match in both value and quality but remains partially intact (by 68%) if there is a mismatch in reward quality between training and test (Figure 1A,B). We conclude that after odor-fructose training the larvae approach the odor both in search of something ‘good’ (value) and in search of what is specifically fructose (quality of reward). Likewise, after odor-aspartic acid training, they search both for something ‘good’ and for aspartic acid. In other words, if during the test the larvae, for example, have sugar anyway but remember where aspartic acid can be found, they will still go for aspartic acid in addition.

Regarding the aversive domain, pairing an odor with quinine as punishment leads to aversive memory (Gerber and Hendel, 2006; Schleyer et al., 2011; El-Keredy et al., 2012; Apostolopoulou et al., 2014). In this case, learned behavior can best be understood as an informed escape that is warranted in the presence but not in the absence of quinine. Accordingly, in the presence but not in the absence of quinine one observes that the larvae run away from the previously punished odor (Gerber and Hendel, 2006; Schleyer et al., 2011; El-Keredy et al., 2012; Apostolopoulou et al., 2014) (see also Niewalda et al., 2008; Schnaitmann et al., 2010; Eschbach et al., 2011; Russell et al., 2011 for reports using other aversive reinforcers and/or adult flies). Notably and in accordance with our earlier results in the appetitive domain (Schleyer et al., 2011), such learned escape does not merely depend on the concentration of quinine in the test; rather, learned escape lessens as the quinine concentration in the test is reduced relative to that in training (Figure 2—figure supplement 1).

Given that high-concentration salt can also serve as punishment (Gerber and Hendel, 2006; Niewalda et al., 2008; Russell et al., 2011), we ask whether an odor-quinine memory is specific in prompting escape from quinine—but not from salt. For concentrations of quinine and salt that are of equal value as punishment, this is indeed the case (Figure 2A) (Figure 2—figure Supplement 2); likewise, odor-salt memories are specific in prompting learned escape from salt but not from quinine (for a summary see Figure 2B). Such specificity shows that larvae have a memory specific to the quality of punishment, a memory that can specifically be applied in the appropriate situation. We stress that the present results do not provide proof of the absence of aversive ‘common currency’ value processing. Indeed, in cases of unequal punishment value, larvae may use this information (Eschbach et al., 2011).

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Taken together, within both the appetitive and aversive domain, experiencing an odor with a taste reinforcement can establish an associative olfactory memory that is specific to the quality (fructose, aspartic acid, quinine, high-concentration salt) of taste reinforcement.

The experimental twist to reveal such quality-of-reinforcement memory is accomplished by flagrantly breaking the first rule of associative memory research: namely never, ever, to test for learned behavior in the presence of the reinforcer. We would like to stress that innate olfactory behavior per se is not affected by the presence of any of the tastant reinforcers (Hendel et al., 2005; Schleyer et al., 2011) (Figure 3). Also, the mere presence of any given tastant reinforcer during the memory test is not a critical determinant for whether learned behavior is observed: learned behavior can be observed (or not) in the presence of any of the tastant reinforcers in this study—what matters is how closely it matches the one used during training in quality and/or in value (Figures 1B and 2B).

Figure 3

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The mushroom bodies, a third-order ‘cortical’ (Tomer et al., 2010) brain region in insects, are canonically proposed to feature distinct regions harboring appetitive and aversive olfactory memory traces, respectively (Heisenberg, 2003; Perisse et al., 2013; see also Schleyer et al., 2011) (Figure 4A,B). Only recently has the possibility of different neuronal substrates underlying different qualities of reinforcement come to be considered. These studies have so far not yielded a double dissociation between different dopaminergic mushroom body input neurons for different qualities of reinforcement:

• For the aversive domain Galili et al. (2014) suggested that the set of dopaminergic mushroom body input neurons responsible for heat-punishment in adult Drosophila is nested within that for electric-shock punishment. Similarly, electric-shock punishment and punishment with the insect repellent DEET appear to be signaled towards the mushroom body by largely if not completely overlapping sets of dopamine neurons (Das et al., 2014).

• For the appetitive domain, a set of dopaminergic mushroom body input neurons (included in the 0104-Gal4 strain) that was previously found to be required for sugar-learning in adult Drosophila (Burke et al., 2012) turned out to be dispensable for water-reward learning (Lin et al., 2014). Whether in turn dopaminergic mushroom body input neurons included in the R48B04 strain, which were discovered by Lin et al. (2014) to be required for water-reward learning, are dispensable for sugar-learning remains to be tested.

Figure 4

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Thus, the nuanced memory of at least two qualities of appetitive and two qualities of aversive taste reinforcers as shown in the present study is unexpected. Appropriate to such nuanced memories, the mushroom bodies show a fairly complex substructure, even in larval Drosophila. At least 10 mushroom body regions are recognized, defined by the tiled innervation of input and output neurons (Pauls et al., 2010b). Our behavioral data suggest that at least five such tiles of the mushroom body would be required to accommodate learned search for fructose or aspartic acid, learned escape from quinine or from high-concentration salt and in addition a less specific appetitive value-memory (Figure 4C). Clearly the things worth remembering for a larva include many more than these five (Niewalda et al., 2008; Pauls et al., 2010a; Eschbach et al., 2011; Khurana et al., 2012; Rohwedder et al., 2012; Diegelmann et al., 2013). Likewise, the behavioral repertoire of larvae may be considerably greater than thought (Vogelstein et al., 2014). Using our current approach, it will now be possible to systematically determine the limits of specificity in the processing of reinforcement quality. This may reveal signals of intermediate specificity to inform the animals about, for example, edibility, caloric value, proteinogenic value, suitability for pupariation, toxicity, or even acutely and situationally modulated matters of concern (Simpson et al., 2015).

We note that the distinction between fructose and aspartic acid memory implies that the sensory neurons mediating the rewarding effects of these stimuli cannot be completely overlapping and that the sensory neurons mediating the punishing effects of quinine and high-concentration salt likewise cannot (for reviews of the taste system in Drosophila, see Cobb et al., 2009; Gerber et al., 2009). For identifying these neurons, it is significant that they may be distinct from those mediating innate choice behavior (Apostolopoulou et al., 2014; König et al., 2014).

In the vertebrate literature, the processing of reward by value has been regarded as a matter of sophistication because an integrated, higher-order value signal can be generated from sensorially distinct qualities of reward (e.g., Lak et al., 2014). On the other hand, reward expectations can apparently also be processed in a quality-specific manner (e.g., Dickinson and Balleine, 1994; Watanabe, 1996). In terms of the minimally required number of cells, the processing by reinforcer quality is more demanding than value-only processing (Figure 4B,C). The fact that even the humble, 10,000-neuron brain of a larva operates with both reward value and quality may suggest that they both represent fundamentally important, indispensable aspects of reward processing.

1. 1. Apostolopoulou AA
   2. Mazija L
   3. Wust A
   4. Thum AS

   (2014) The neuronal and molecular basis of quinine-dependent bitter taste signaling in Drosophila larvae

   Frontiers in Behavioral Neuroscience 8:6.

   https://doi.org/10.3389/fnbeh.2014.00006

   • Google Scholar
2. 1. Aso Y
   2. Sitaraman D
   3. Ichinose T
   4. Kaun K
   5. Vogt K
   6. Belliart-Guérin G
   7. Plaçais PY
   8. Robie A
   9. Yamagata N
   10. Schnaitmann C
   11. Rowell WJ
   12. Johnston RM
   13. Ngo TT
   14. Chen N
   15. Korff W
   16. Nitabach MN
   17. Heberlein U
   18. Preat T
   19. Branson KM
   20. Tanimoto H
   21. Rubin GM

   (2014) Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

   eLife 4:.

   https://doi.org/10.7554/eLife.04580

   • Google Scholar
3. 1. Burke CJ
   2. Huetteroth W
   3. Owald D
   4. Perisse E
   5. Krashes MJ
   6. Das G
   7. Gohl D
   8. Silies M
   9. Certel S
   10. Waddell S

   (2012) Layered reward signalling through octopamine and dopamine in Drosophila

   Nature 492:433–437.

   https://doi.org/10.1038/nature11614

   • Google Scholar
4. 1. Cobb M
   2. Scott K
   3. Pankratz M

   (2009)

   Gustation in Drosophila melanogaster

   SEB Experimental Biology Series 63:1–38.

   • Google Scholar
5. 1. Das G
   2. Klappenbach M
   3. Vrontou E
   4. Perisse E
   5. Clark CM
   6. Burke CJ
   7. Waddell S

   (2014) Drosophila learn opposing components of a compound food stimulus

   Current Biology 24:1723–1730.

   https://doi.org/10.1016/j.cub.2014.05.078

   • Google Scholar
6. 1. Dickinson A
   2. Balleine B

   (1994) Motivational control of goal-directed action

   Animal Learning & Behavior 22:1–18.

   https://doi.org/10.3758/BF03199951

   • Google Scholar
7. 1. Diegelmann S
   2. Klagges B
   3. Michels B
   4. Schleyer M
   5. Gerber B

   (2013) Maggot learning and synapsin function

   The Journal of Experimental Biology 216:939–951.

   https://doi.org/10.1242/jeb.076208

   • Google Scholar
8. 1. El-Keredy A
   2. Schleyer M
   3. König C
   4. Ekim A
   5. Gerber B

   (2012) Behavioural analyses of quinine processing in choice, feeding and learning of larval Drosophila

   PLOS ONE 7:e40525.

   https://doi.org/10.1371/journal.pone.0040525

   • Google Scholar
9. 1. Eschbach C
   2. Cano C
   3. Haberkern H
   4. Schraut K
   5. Guan C
   6. Triphan T
   7. Gerber B

   (2011) Associative learning between odorants and mechanosensory punishment in larval Drosophila

   The Journal of Experimental Biology 214:3897–3905.

   https://doi.org/10.1242/jeb.060533

   • Google Scholar
10. 1. Galili DS
    2. Dylla KV
    3. Ludke A
    4. Friedrich AB
    5. Yamagata N
    6. Wong JY
    7. Ho CH
    8. Szyszka P
    9. Tanimoto H

    (2014) Converging circuits mediate temperature and shock aversive olfactory conditioning in Drosophila

    Current Biology 24:1712–1722.

    https://doi.org/10.1016/j.cub.2014.06.062

    • Google Scholar
11. 1. Gerber B
    2. Biernacki R
    3. Thum J

    (2013) Odor-taste learning assays in Drosophila larvae

    Cold Spring Harbor Protocols 2013:.

    https://doi.org/10.1101/pdb.prot071639

    • Google Scholar
12. 1. Gerber B
    2. Hendel T

    (2006) Outcome expectations drive learned behaviour in larval Drosophila

    Proceedings Biological Sciences/ the Royal Society 273:2965–2968.

    https://doi.org/10.1098/rspb.2006.3673

    • Google Scholar
13. 1. Gerber B
    2. Stocker RF
    3. Tanimura T
    4. Thum AS

    (2009) Smelling, tasting, learning: Drosophila as a study case

    Results and Problems in Cell Differentiation 47:139–185.

    https://doi.org/10.1007/400_2008_9

    • Google Scholar
14. 1. Heisenberg M

    (2003) Mushroom body memoir: from maps to models

    Nature Reviews Neuroscience 4:266–275.

    https://doi.org/10.1038/nrn1074

    • Google Scholar
15. 1. Hendel T
    2. Michels B
    3. Neuser K
    4. Schipanski A
    5. Kaun K
    6. Sokolowski MB
    7. Marohn F
    8. Michel R
    9. Heisenberg M
    10. Gerber B

    (2005) The carrot, not the stick: appetitive rather than aversive gustatory stimuli support associative olfactory learning in individually assayed Drosophila larvae

    Journal of Comparative Physiology A, Neuroethology, Sensory, Neural, and Behavioral Physiology 191:265–279.

    https://doi.org/10.1007/s00359-004-0574-8

    • Google Scholar
16. 1. Khurana S
    2. Robinson BG
    3. Wang Z
    4. Shropshire WC
    5. Zhong AC
    6. Garcia LE
    7. Corpuz J
    8. Chow J
    9. Hatch MM
    10. Precise EF
    11. Cady A
    12. Godinez RM
    13. Pulpanyawong T
    14. Nguyen AT
    15. Li WK
    16. Seiter M
    17. Jahanian K
    18. Sun JC
    19. Shah R
    20. Rajani S
    21. Chen WY
    22. Ray S
    23. Ryazanova NV
    24. Wakou D
    25. Prabhu RK
    26. Atkinson NS

    (2012) Olfactory conditioning in the third instar larvae of Drosophila melanogaster using heat shock reinforcement

    Behavior Genetics 42:151–161.

    https://doi.org/10.1007/s10519-011-9487-9

    • Google Scholar
17. 1. König C
    2. Schleyer M
    3. Leibiger J
    4. El-Keredy A
    5. Gerber B

    (2014) Bitter-sweet processing in larval Drosophila

    Chemical Senses 39:489–505.

    https://doi.org/10.1093/chemse/bju016

    • Google Scholar
18. 1. Lak A
    2. Stauffer WR
    3. Schultz W

    (2014) Dopamine prediction error responses integrate subjective value from different reward dimensions

    Proceedings of the National Academy of Sciences of USA 111:2343–2348.

    https://doi.org/10.1073/pnas.1321596111

    • Google Scholar
19. 1. Lin S
    2. Owald D
    3. Chandra V
    4. Talbot C
    5. Huetteroth W
    6. Waddell S

    (2014) Neural correlates of water reward in thirsty Drosophila

    Nature Neuroscience 17:1536–1542.

    https://doi.org/10.1038/nn.3827

    • Google Scholar
20. 1. Menzel R

    (2014) The insect mushroom body, an experience-dependent recoding device

    Journal of Physiology, Paris 108:84–95.

    https://doi.org/10.1016/j.jphysparis.2014.07.004

    • Google Scholar
21. 1. Niewalda T
    2. Singhal N
    3. Fiala A
    4. Saumweber T
    5. Wegener S
    6. Gerber B

    (2008) Salt processing in larval Drosophila: choice, feeding, and learning shift from appetitive to aversive in a concentration-dependent way

    Chemical Senses 33:685–692.

    https://doi.org/10.1093/chemse/bjn037

    • Google Scholar
22. 1. Pauls D
    2. Pfitzenmaier JE
    3. Krebs-Wheaton R
    4. Selcho M
    5. Stocker RF
    6. Thum AS

    (2010a) Electric shock-induced associative olfactory learning in Drosophila larvae

    Chemical Senses 35:335–346.

    https://doi.org/10.1093/chemse/bjq023

    • Google Scholar
23. 1. Pauls D
    2. Selcho M
    3. Gendre N
    4. Stocker RF
    5. Thum AS

    (2010b) Drosophila larvae establish appetitive olfactory memories via mushroom body neurons of embryonic origin

    The Journal of Neuroscience 30:10655–10666.

    https://doi.org/10.1523/JNEUROSCI.1281-10.2010

    • Google Scholar
24. 1. Perisse E
    2. Burke C
    3. Huetteroth W
    4. Waddell S

    (2013) Shocking revelations and saccharin sweetness in the study of Drosophila olfactory memory

    Current Biology 23:R752–R763.

    https://doi.org/10.1016/j.cub.2013.07.060

    • Google Scholar
25. Book

    1. Rescorla RA
    2. Wagner AR

    (1972)

    A theory of pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement

    In: Black AH, Prokasy WF, editors. Classical Conditioning II: Current Research and Theory. New York: Appleton-Century-Crofts. pp. 64–99.

    • Google Scholar
26. 1. Rohwedder A
    2. Pfitzenmaier JE
    3. Ramsperger N
    4. Apostolopoulou AA
    5. Widmann A
    6. Thum AS

    (2012) Nutritional value-dependent and nutritional value-independent effects on Drosophila melanogaster larval behavior

    Chemical Senses 37:711–721.

    https://doi.org/10.1093/chemse/bjs055

    • Google Scholar
27. 1. Russell C
    2. Wessnitzer J
    3. Young JM
    4. Armstrong JD
    5. Webb B

    (2011) Dietary salt levels affect salt preference and learning in larval Drosophila

    PLOS ONE 6:e20100.

    https://doi.org/10.1371/journal.pone.0020100

    • Google Scholar
28. Book

    1. Schleyer M
    2. Diegelmann S
    3. Michels B
    4. Saumweber T
    5. Gerber B

    (2013)

    'Decision-making' in larval Drosophila

    In: Menzel R, Benjamin P, editors. Invertebrate learning and memory. München: Elsevier. pp. 41–55.

    • Google Scholar
29. 1. Schleyer M
    2. Saumweber T
    3. Nahrendorf W
    4. Fischer B
    5. von Alpen D
    6. Pauls D
    7. Thum A
    8. Gerber B

    (2011) A behavior-based circuit model of how outcome expectations organize learned behavior in larval Drosophila

    Learning & Memory 18:639–653.

    https://doi.org/10.1101/lm.2163411

    • Google Scholar
30. 1. Schnaitmann C
    2. Vogt K
    3. Triphan T
    4. Tanimoto H

    (2010) Appetitive and aversive visual learning in freely moving Drosophila

    Frontiers in Behavioral Neuroscience 4:10.

    https://doi.org/10.3389/fnbeh.2010.00010

    • Google Scholar
31. 1. Simpson SJ
    2. Clissold FJ
    3. Lihoreau M
    4. Ponton F
    5. Wilder SM
    6. Raubenheimer D

    (2015) Recent advances in the integrative nutrition of arthropods

    Annual Review of Entomology 60:293–311.

    https://doi.org/10.1146/annurev-ento-010814-020917

    • Google Scholar
32. 1. Tomer R
    2. Denes AS
    3. Tessmar-Raible K
    4. Arendt D

    (2010) Profiling by image registration reveals common origin of annelid mushroom bodies and vertebrate pallium

    Cell 142:800–809.

    https://doi.org/10.1016/j.cell.2010.07.043

    • Google Scholar
33. 1. Vogelstein JT
    2. Park Y
    3. Ohyama T
    4. Kerr RA
    5. Truman JW
    6. Priebe CE
    7. Zlatic M

    (2014) Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning

    Science 344:386–392.

    https://doi.org/10.1126/science.1250298

    • Google Scholar
34. 1. Watanabe M

    (1996) Reward expectancy in primate prefrontal neurons

    Nature 382:629–632.

    https://doi.org/10.1038/382629a0

    • Google Scholar

Author details

1. Michael Schleyer

   Department of Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   MS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   michael.schleyer@lin-magdeburg.de

   Competing interests

   The authors declare that no competing interests exist.

2. Daisuke Miura

   Department of Biology, Kyushu University, Fukuoka, Japan

   Contribution

   DM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Teiichi Tanimura

   Department of Biology, Kyushu University, Fukuoka, Japan

   Contribution

   TT, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Bertram Gerber

   1. Department of Genetics of Learning and Memory, Leibniz Institute for Neurobiology, Magdeburg, Germany
   2. Institute for Biology and Behavior Genetics, Otto von Guericke University Magdeburg, Magdeburg, Germany
   3. Center of Behavioral Brain Sciences, Magdeburg, Germany

   Contribution

   BG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   bertram.gerber@lin-magdeburg.de

   Competing interests

   The authors declare that no competing interests exist.

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