Individual dopaminergic neurons induce unique, yet overlapping combinations of behavioural modulations, including safety learning, memory retrieval, and acute locomotion

Dopamine has many functions in the brain. Among them, two turned out to be highly conserved throughout the animal kingdom: to carry ‘teaching’ signals during associative learning and to initiate and control movement (Schultz, 2015; Kaun and Rothenfluh, 2017; Gershman and Uchida, 2019; Speranza et al., 2021; Ryczko and Dubuc, 2023). In mammals and humans, it is widely accepted that these two functions are brought about by different populations of dopaminergic neurons (DANs): effects regarding learning are thought to be elicited by the mesolimbic pathway, effects regarding movement by the nigro-striatal pathway (Speranza et al., 2021). Here, we study in the larva of the fruit fly Drosophila melanogaster whether in contrast the same individual central brain-DANs can induce both functions regarding memory and movement.

The fruit fly larva is an established model organism to study the neuronal and genetic mechanisms underlying associative learning (Widmann et al., 2018; Thum and Gerber, 2019). Due to the numerical simplicity of its brain and the available toolbox for transgene expression, the larva allows investigating these mechanisms at tractable levels of complexity. In D. melanogaster, distinct sets of DANs convey appetitive and aversive unconditioned stimuli (US), respectively (larvae [Widmann et al., 2018; Thum and Gerber, 2019]; adults [Kaun and Rothenfluh, 2017; Boto et al., 2020]) (a similar scenario may be emerging in vertebrates, too [Lammel et al., 2014; Schultz, 2015; Groessl et al., 2018; Menegas et al., 2018; de Jong et al., 2019; Kutlu et al., 2023]). Each of the DANs synapses onto a different compartment of the parallel fibres of Kenyon cells (KCs) that form a higher-order brain structure called mushroom body (larvae [Thum and Gerber, 2019]; adults [Modi et al., 2020]). Odours are combinatorially coded across the array of these KCs, which, in turn, give output to mushroom body output neurons (MBONs). The MBONs collect information across the KCs and send it towards efferent circuitry, promoting either approach or avoidance (larvae [Thum and Gerber, 2019]; adults [Modi et al., 2020]). Within the KCs, the coincidence of odour presentation and activity in DANs is detected and enables a memory trace to be formed. The respective memory engram is thought to come by synaptic depression between the KCs and the MBONs of the same mushroom body compartment, such that the net effect of the activity across the MBONs is tilted towards approach or avoidance, respectively (larvae [Eschbach et al., 2020; Eschbach et al., 2021]; adults [Cohn et al., 2015; Owald et al., 2015]). Electron microscope reconstructions further revealed a direct within-compartment connection from the DANs onto the MBONs (larvae [Eichler et al., 2017]; adults [Takemura et al., 2017]). Thus, each DAN has two main targets: the KCs and the MBONs of its own compartment. The general organisation of this DAN-KC-MBON matrix is likely shared with other insects (Menzel, 2001; Strausfeld et al., 2009; Schürmann, 2016; Kaun and Rothenfluh, 2017).

Three pairs of identified DANs of the dorso-lateral cluster 1 (DL1) have been shown to support aversive learning upon optogenetic activation during Pavlovian conditioning (Eschbach et al., 2020; Weiglein et al., 2021; Weber et al., 2023). In this study, we ask whether those same DANs have additional functions. Specifically, we ask for their role for safety learning, i.e., learning that a cue predicts the absence of a threat; memory retrieval, i.e., whether a memory will be retrieved and affect behaviour; and modulations of acute, innate locomotion. Little is known about how the dopaminergic system controls any of these functions – if at all. By systematically testing the functions of the same DANs across diverse behavioural tasks, we investigate the fundamental organisation of the dopaminergic system: do different individual neurons specialise in each function, or does a single neuron serve multiple functions? Given the highly conserved functionality of the dopaminergic system across the animal kingdom, this study has the potential to provide valuable insights into the dopaminergic organisation of behaviour in any brain.

We focus our study on the DANs that support aversive learning when optogenetically activated, called DAN-d1, DAN-f1, and DAN-g1. We investigated the transgenic expression pattern of the driver strains covering each DAN, and the effects of activating each DAN on learning, memory retrieval, and locomotion.

The used driver strains drive transgene expression in identified DANs

Previous studies had described the expression patterns of the used split driver strains in the central nervous system in detail (Selcho et al., 2009; Rohwedder et al., 2016; Eschbach et al., 2020; Weber et al., 2023). Here, we extended the view on the expression within the whole body. Towards this end, we applied a recent protocol for whole-body visualisations (Kobler et al., 2021). We confirmed the highly specific transgene expression in the brain of the four split-Gal4 driver strains without any detectable expression in the rest of the body (Figure 1A–D, Figure 1—figure supplements 1–4). TH-Gal4 is known to cover almost all DANs in the brain, with the notable exception of the neurons of the primary protocerebral anterior medial (pPAM) cluster (Selcho et al., 2009). We confirm this pattern of transgene expression and in addition found strong expression outside of the brain, in particular in the proventriculus and in hypodermal melanin-producing cells (Figure 1E, Figure 1—figure supplement 5, Figure 1—video 1). This pattern fits to the reported function of dopamine in the hardening and darkening of the insect cuticle (Neckameyer and White, 1993; Verlinden, 2018) and a recent report of TH-Gal4 expression in the adult proventriculus (Erhardt et al., 2023). This result means that any genetic manipulation using TH-Gal4 may affect cells outside of the central nervous system. Finally, we confirm that R58E02-Gal4 covers three of the four pPAM DANs that are missing in the TH-Gal4 expression pattern (Rohwedder et al., 2016), with no expression outside of the central nervous system (Figure 1F, Figure 1—figure supplement 6).

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Some individual DANs establish and retrieve associative memories

In larvae, for many kinds of aversive US, learned behaviour is only observed if the US is present at the moment of memory retrieval (Gerber and Hendel, 2006; Niewalda et al., 2008; Eschbach et al., 2011; El-Keredy et al., 2012; Toshima et al., 2019). For example, we trained groups of larvae with an odour as the conditioned stimulus (CS) and quinine as US and observed an aversive associative memory when we tested the animals in the presence of quinine, but not in its absence (Figure 2A). Learned aversive behaviour therefore can be viewed as an escape from the US, guided by the respective memory – an escape that is pointless in absence of anything to escape from (Schleyer et al., 2011; Schleyer et al., 2013; Schleyer et al., 2015a; Paisios et al., 2017).

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We then asked for the roles of the DL1-DANs for both establishing and retrieving associative memories by employing the same learning experiment, but instead of quinine we used optogenetic DAN activation by blue light as a virtual US. We also tested the innate odour preference of all genotypes, both in darkness and in blue light (Figure 2—figure supplement 1) and observed no evidence that the innate odour preference was modified by activating the respective DANs.

After training with DAN-d1 as virtual US, zero memory scores were observed when the animals were tested in darkness. When tested in blue light, however, the animals of the experimental genotype, but not the controls, showed aversive associative memory (Figure 2B, Figure 2—figure supplement 2A, B). Interestingly, a recent study observed no significant memory with DAN-d1 as virtual US in a similar kind of experiment (Weber et al., 2023). We wondered whether this difference could be attributed to the strength of DAN activation in different experimental setups. We therefore repeated the experiment with two different light intensities and observed an aversive memory only when using the low intensity that we use in all other learning experiments in this study, but not with a 10-fold higher intensity (Figure 2—figure supplement 2C). Moreover, when raising the larvae in food supplemented with all-trans-retinal, which had been shown to increase the effectiveness of our effector ChR2-XXL (Dawydow et al., 2014), no aversive memory could be detected (Figure 2—figure supplement 2D). Thus, it seems that a too strong activation of DAN-d1 prevents the establishment and/or retrieval of an aversive memory.

For DAN-f1, we likewise found an aversive memory that was retrieved only when DAN-f1 was activated also during the test (Figure 2C, Figure 2—figure supplement 2E). Our results indicate that each DAN-d1 and DAN-f1 each is sufficient to establish an aversive memory, which requires the respective DAN to be activated for retrieval.

A previous study used the same driver strains crossed to UAS-CsChrimson as the optogenetic effector in a similar learning experiment and observed learned behaviour also in darkness (Eschbach et al., 2020). Using the same effector and the same intensity of red light stimulation, we were able to replicate these results (Figure 2—figure supplement 3). Thus, the memories induced by either effector differed in the conditions of how they could be retrieved. These results call for caution against using different optogenetic effectors interchangeably.

Individual DANs are sufficient for establishing safety but not punishment memories

Animals can associate stimuli not only with the presence of a threat but also with its absence (Rescorla, 1967; Malaka, 1999). Such safety memory has been found in D. melanogaster but received little attention by the community until recently (larvae [Paisios et al., 2017; Schleyer et al., 2018; Schleyer et al., 2020; Sen et al., 2024]; adults [Jacob and Waddell, 2020; Zhao et al., 2021]). That means a US, such as bitter taste, can establish two different memories: punishment memory after paired training and safety memory after unpaired training (Schleyer et al., 2011; Schleyer et al., 2018; Figure 3A). Critically, our results so far demonstrate that each of the individual DANs was sufficient to establish an associative memory – but not whether this was a punishment memory, a safety memory, or both. Therefore, we next focussed on the animals’ odour preference after paired and unpaired training, respectively, compared to the odour preference of the pooled genetic controls as a baseline. A punishment memory would reveal itself by decreased odour preferences after paired training, a safety memory by increased preferences after unpaired training, compared to the baseline. Due to the rather small effect sizes and high variability of odour preferences, we pooled the data of all independent replications of the experiments (Figure 2, including Figure 2—figure supplements 2 and 3), as well as the genetic controls of each experiment for a post hoc analysis of the odour preference.

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For the case of DAN-d1, after paired training, there was no difference in odour preference between the genetic controls and the experimental genotype, nor between the two testing conditions (Figure 3B, left). After unpaired training, in contrast, the larvae of the experimental genotype exerted increased odour preferences when tested under blue light compared to all other groups (Figure 3B, right). This suggests that the activation of DAN-d1 established no punishment memory, but a safety memory. For DAN-f1, we found analogous results (Figure 3C). In the case of DAN-g1, after paired training, we found more negative odour preferences when tested in the presence than in the absence of light, but no difference between the controls and the experimental genotype (Figure 3D, left). This suggests a potential punishment memory by the light, but not by the activation of DAN-g1. After unpaired training, the animals of the experimental genotype showed higher odour preferences than the controls, both in darkness and in light (Figure 3D, right).

Taken together, we found that all three DANs induced only a safety memory after unpaired training, but no punishment memory after paired training (confirming similar results using CsChrimson; Eschbach et al., 2020). The safety memory established by DAN-d1 and DAN-f1 was retrieved only if the respective neuron was activated during the test, whereas the safety memory established by DAN-g1 was, at least partially, also retrieved without neuronal activation.

Activating the DANs covered by TH-Gal4 modulates the animals’ bending and velocity

Next, we investigated which effect activating the DL1-DANs would have on locomotion.

Using the novel IMBA (Individual Maggot Behaviour Analysis) software for behavioural analyses, we recently had found strong locomotor effects upon optogenetic activation of the DANs covered by TH-Gal4 (Thane et al., 2023). Within the current study, we repeated this experiment in total five times, in two different laboratories and with different parameters (Figure 4—figure supplement 1A). This gave us the opportunity to probe the behavioural effects upon DAN activation for their robustness across experimental repetitions. Groups of around eight experimentally naïve larvae of either the experimental genotype or the driver or effector controls were allowed to crawl freely on an agar-filled Petri dish in darkness. After 30 s, blue light was activated for 30 s, followed by darkness. We video-recorded the animals’ behaviour and analysed it afterwards with the IMBA software (Figure 4A).

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The IMBA software provides a rich variety of behavioural attributes for individual animals. We selected 17 attributes (for a list and descriptions, see Table 1) and first asked how these attributes relate to each other. Towards this end, we performed a Spearman’s correlation analysis of all 17 attributes based on the animals’ baseline behaviour in darkness. We found three clusters of inter-correlated attributes (Figure 4B). Cluster 1 consists of attributes measuring different aspects of directional changes, e.g., the animals’ bending, or the rate of lateral head movements, the so-called head casts (HC). All attributes of cluster 2 relate to the speed of the larvae’s peristaltic movement. Finally, cluster 3 consists of attributes dealing with the directional bias of the animals’ behaviour. For example, the bending bias measures to which extent an animal was bent more towards one side than another.

Table 1

Name	Unit	Description	Cluster
Bending	$\circ$	The bending angle (BA) is the angle between the larva’s tail vector and a vector from the middle of the larva to its head. Positive values indicate the larva bending to the left, negative values bending to the right. ‘Bending’ in this study refers to the absolute, i.e., direction-independent BA.	1
Bending bias		The absolute of the average of all BA values of a larva, divided by the average of all absolute BA values: $\text{abs}\left(\overline{BA}\right)/\left(\overline{abs\left(BA\right)}\right)$. The bending bias is constrained between 0 and 1, with 0 indicating a larva was bending equally to left and right, and 1 indicating a larva was only bending into one direction (no matter which direction).	3
HV angular speed	$\frac{\circ }{s}$	The head vector angular speed (HVAS) is the angular speed of the head vector (HV) through the head of the larva. Positive values indicate movement of the head to the right, negative values to the left. ‘HV angular speed’ in this study refers to the absolute, i.e., direction-independent HVAS.	1
HV angular speed bias		Calculated analogous to the bending bias. Constrained between 0 and 1, with 0 indicating a larva was moving its head equally fast to left and right, and 1 indicating a larva was moving its head only into one direction.	3
HV angular acceleration	$\frac{\circ }{s^2}$	The absolute angular acceleration of the head vector (HV).	1
TV angular speed	$\frac{\circ }{s}$	The tail vector angular speed (TVAS) is the angular speed of the tail vector (TV) through the rear half of the larva. Positive values indicate movement to the right, negative values to the left. ‘TV angular speed’ in this study refers to the absolute, i.e., direction-independent TVAS.	1
TV angular speed bias		Calculated analogous to the bending bias. Constrained between 0 and 1, with 0 indicating a larva was moving its tail equally fast to left and right, and 1 indicating a larva was moving its tail only into one direction.	3
TV angular acceleration	$\frac{\circ }{s^2}$	The absolute angular acceleration of the tail vector (TV).	1
HC rate	$\frac{HC}{s}$	The number of head casts (HC) per second. HCs were defined as events of an HVAS larger than 35 °/s or smaller than –35 °/s, respectively.	1
HC rate bias		The HC rate of HCs to the left minus the HC rate of HCs to the right, divided by the total HC rate. Constrained between 0 and 1, with 0 indicating a larva had equal HC rates to left and right, and 1 indicating a larva was making HCs in only one direction.	3
HC angle	$\circ$	The head cast angle (HCA) indicates how much the orientation of the animal changes upon each HC. It is defined as the difference between the BA at the end and the beginning of the HC. Positive values indicate that the larva changes its orientation to the left, negative values that it changes its orientation to the right. ‘HC angle’ in this study refers to the absolute, i.e., direction-independent HCA.	1
HC angle bias		Calculated analogous to the bending bias. Constrained between 0 and 1, with 0 indicating a larva was making equally large HCs to left and right, and 1 indicating a larva was making HCs in only one direction.	3
Distance travelled	$bl$	The distance of the complete path a larva travels during the observation period (measured in body lengths [bl]).	2
Midpoint speed	$\frac{bl}{s}$	The speed of the larva’s midpoint, irrespective of direction.	2
Midpoint acceleration	$\frac{bl}{s^2}$	The absolute acceleration of the larva’s midpoint.	2
Tail velocity	$\frac{bl}{s}$	The velocity of the larva’s tail point in a forward direction, i.e., in the direction of the tail vector.	2
Tail acceleration	$\frac{bl}{s^2}$	The absolute acceleration of the larva’s tail in the direction of the tail vector.	2

Then, we wondered how much each attribute would change upon light stimulation. Towards this end, we calculated a ∆-value of each attribute as an individual’s behaviour during the second half of the light stimulation minus its baseline behaviour during darkness (Figure 4A). We used only the second half of the stimulation to exclude any potential startle-like response to the sudden light. Then, we used a random forest classification algorithm to classify individual larvae between the genotypes based on the 17 ∆-attributes. Crucially, the random forest provides the relative importance of each attribute for the classification. In other words, it can be used to obtain an unbiased measure of how relevant each attribute is for discriminating the behaviour of different genotypes (Thane et al., 2023). We found consistently the attributes of clusters 1 and 2 to be much more important than those of cluster 3 (Figure 4C). Within each cluster, most attributes were on average similarly important, with strong variability both within and across data sets (Figure 4—figure supplement 1).

We concluded that there does not appear to be a single ‘most important’ behavioural attribute that accounts for the differences between the experimental genotype and the genetic controls. Rather, the differences are best described by clusters 1 and 2, representing the amount of bending and directional changes and the peristaltic speed, respectively. Within each cluster, the attributes were strongly correlated (Figure 4B) and varied in importance (Figure 4—figure supplement 1). Therefore, we decided to present the relative importance of each attribute cluster along with each experiment. Additionally, we present one representative attribute of each cluster 1 and cluster 2. We picked bending and tail velocity as intuitive behavioural descriptors that have been used previously (Thane et al., 2023).

DANs modulate locomotion independently of the light responses

In the previous experiments, animals of all genotypes displayed a very strong response to the light stimulation. We therefore wondered whether the observed effects upon DAN activation were due to a modulation of this light response or an independent modulation of the animals’ locomotion.

We compared the animals’ behavioural changes in three sets of experiments, activating the DANs with strong blue light, weak blue light, or red light, respectively (Figure 5). When using the about 10-fold lower light intensity, we nevertheless observed rather strong light responses in the controls (Figure 4—figure supplement 2) and robust differences in behaviour upon activation of either set of DANs (Figure 5—figure supplement 1).

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Red light reportedly triggers only very mild light responses (Xiang et al., 2010; Eschbach et al., 2021). We therefore next used the red-shifted effector CsChrimson (Liu et al., 2012) and raised larvae either with or without all-trans-retinal, which is necessary for the Chrimson channels to function (Klapoetke et al., 2014). When the larvae were raised with retinal, we observed similar effects as when using ChR2-XXL (Figure 5—figure supplement 2). The control without retinal, in contrast, displayed only very weak responses to the red light (Figure 5—figure supplement 3).

In order to compare the effects of DAN activation between the three experiments, we defined the behaviour of the pooled controls in each experiment as zero and displayed the difference of each experimental group from their respective controls. The behavioural effects of the DAN activations were strikingly similar in the three conditions, both in magnitude and in time course (Figure 5A and B). The only obvious difference was an extended reduction of the tail velocity beyond the activation of the TH-Gal4-DANs that could be seen only when using ChR2-XXL (Figure 5A). This ‘after-effect’ may be due to the reportedly slow closing times of the ChR2-XXL channel (Dawydow et al., 2014).

We wanted to further quantify the relation between the light responses elicited in each experiment and the behavioural effects of the DAN activation. Therefore, we used the mean behavioural changes upon light stimulation of the pooled controls of each experiment as an indicator for the effect size of the light response and the difference in behavioural changes between the experimental groups and the respective controls as an indicator for the effect size of the DAN activation. We found that the effect sizes of the DAN activation were all relatively similar and completely unrelated to the effect sizes of the light responses (Figure 5C).

In summary, we concluded that the observed modulations of bending and tail velocity upon DAN activation occurred independently of the animals’ responses to the light stimulation itself.

Modulations of locomotion do not determine olfactory behaviour

The experiments shown in Figure 5—figure supplement 1 used the same light intensity and genotypes as our previous olfactory experiments with TH-Gal4-DANs (Figure 2F, Figure 2—figure supplement 1F, Figure 3F). Considering the results of both kinds of experiments, two questions arise: how can larvae approach or avoid an odour while the TH-Gal4-DANs are activated (as seen e.g. in Figure 3F) if activating TH-Gal4-DANs completely stops forward locomotion (as seen in Figure 5—figure supplement 1)? And is it possible that the modulations upon DAN activation directly determine the odour preference? To address these questions, we analysed video-recordings of the test phases of the experiments shown in Figure 3F, Figure 2—figure supplement 1F. During innate olfactory preference (Figure 6A–C), we found increased bending and decreased tail velocity when the experimental genotype was tested in light rather than in darkness. Both effects were weaker than those during the locomotion experiments and declined further over time (Figure 6B and C). In other words, in this experiment the animals’ locomotion was only moderately altered, in a way that still allowed them to reach their goal (Figure 6A). A possible reason for the differing strengths of behavioural responses may be the different handling of the animals: in the olfactory experiments, the Petri dish was loaded with the larvae in dimmed room light and placed in the experimental setup, followed by an immediate start of the light stimulation and video-recording. In the locomotion experiments, the larvae were kept in darkness for at least 40 s before the light stimulation started. The sudden light stimulus after a period of darkness may trigger stronger modulations of locomotion.

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During the testing phase after paired training (Figure 6D–F) and unpaired training (Figure 6G–I), the locomotion effects were even weaker: the experimental genotype showed only a small peak in bending within the first approximately 10 s of the test (Figure 6E and H) and was not different from the other genotypes in terms of forward velocity (Figure 6F and I). It appears that the repeated exposure to both light and DAN activation during the training further reduced the modulations of locomotion.

Notably, although bending and velocity were very similar after paired and unpaired training in the experimental genotype, the animals’ odour preference was drastically different, with strong avoidance after paired training (Figure 6D) and approach after unpaired training (Figure 6G). This suggests that the preference is not directly dependent on locomotion modulations. Next, we addressed this question more directly. We asked whether the bending or tail velocity of each individual larva within the first 60 s would be correlated with its average preference (i.e. its position on the dish) during the following 60 s, which would be indicative of a causal relationship. However, no such correlation was found (Figure 6K and L), suggesting that the types of locomotion modulations we study here do not have a direct impact on odour preference. This is consistent with previous studies, which identified the timing and direction of reorientation events with respect to the odour as the major determinants of olfactory preference (Gomez-Marin et al., 2011; Schleyer et al., 2015b; Paisios et al., 2017; Thane et al., 2023).

Different subsets of DANs convey opposite acute valence when activated

Next, we asked how to interpret the behavioural effects of activating DANs. Stopping and turning around upon a sudden activation of TH-Gal4-DANs can be interpreted as a reaction to an aversive signal (Xiang et al., 2010; Eschbach et al., 2021) – on the other hand, slowing down and turning on the spot could also be a behavioural strategy to stay in a pleasant situation. Likewise, going straight and speeding up upon R58E02-Gal4-DAN activation can be interpreted as a ‘go on’ reaction to an appetitive signal or a strategy to escape. In both larvae and adult flies, the artificial activation of TH-Gal4-DANs and R58E02-Gal4-DANs induce aversive and appetitive memories, respectively (Liu et al., 2012; Yamagata et al., 2015; Rohwedder et al., 2016; Felsenberg et al., 2018; Saumweber et al., 2018; Schleyer et al., 2020; Lozada-Perdomo et al., 2025). However, studies in adults suggest that acute valence (approach/avoidance) and associative valence (appetitive/aversive US) are often not correlated (Rohrsen et al., 2021; Mohammad et al., 2024; Lozada-Perdomo et al., 2025). Therefore, to test for the valence of activation of either set of DANs in larvae, we performed choice experiments between a dark and an illuminated half of a Petri dish (Figure 7A). Larvae of all genotypes avoided the illuminated half as expected (Xiang et al., 2010; von Essen et al., 2011). Relative to the genetic controls, larvae with TH-Gal4-DANs activated showed more avoidance, and larvae with R58E02-Gal4-DANs showed more approach (Figure 7B and C, Figure 7—videos 1–2). With CsChrimson as effector and red instead of blue light, larvae had a positive preference for having their R58E02-Gal4-DANs activated (Figure 7—figure supplement 1A). We next analysed the animals’ behaviour near the dark-light border. When activating TH-Gal4-DANs, the animals’ bending was increased and their velocity decreased on the illuminated half compared to the controls (Figure 7D–F), similar to our previous experiments (Figure 4). When activating R58E02-Gal4-DANs, the animals’ bending was decreased and their velocity increased compared to the controls everywhere on the dish (Figure 7G–I, Figure 7—figure supplement 1B–D).

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Taken together, we concluded that activating TH-Gal4-DANs conveys an acute aversive signal and activating R58E02-Gal4-DANs conveys an acute appetitive signal. The animals’ reaction to the sudden activation of these DANs should therefore be interpreted as aversive and appetitive behaviour, respectively.

The modulations of bending and velocity are dopamine-dependent

Several DANs have been found to have additional neurotransmitters (Eichler et al., 2017; Aso et al., 2019). Therefore, we wondered whether the observed effects on locomotion were indeed dependent on dopamine signalling. Towards this end, we combined optogenetic activation by ChR2-XXL with TH-RNAi, which knocks down the expression of tyrosine hydroxylase, the rate-limiting enzyme of dopamine synthesis. Thus, when both effectors are expressed in a set of DANs, these DANs can get activated by light but have a reduced dopamine synthesis. We found that animals with the ple-dsRNA (TH-RNAi) construct have a higher baseline bending before stimulation than without, irrespective of the presence of TH-Gal4 (Figure 8A and B). In addition, the baseline tail velocity was increased in the animals expressing both effectors in the TH-Gal4-DANs (Figure 8A and C). These observations suggest that the TH knockdown was effective in modulating behaviour. Upon stimulation, we found an increased bending in both genotypes that expressed ChR2-XXL compared to the genetic controls. This increase was slightly smaller in animals with the TH-RNAi (Figure 8D, 3rd and 5th group from left). The change in tail velocity upon DAN activation was unaffected by the TH-RNAi (Figure 8E, 3rd and 5th group from left). The attributes of clusters 1 and 2 were both important to discriminate between the genotypes, suggesting that the TH-RNAi did also affect attributes related to forward locomotion to some extent (Figure 8F and G). Nevertheless, the effects of the TH-RNAi were overall rather weak. There are several possible reasons for this lack of effects, including a too weak knockdown of tyrosine hydroxylase by the ple-dsRNA construct used, or a developmental adaptation to the reduced dopamine levels. Therefore, we next sought to inhibit tyrosine hydroxylase by an independent and acute technique.

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Larvae were fed the tyrosine hydroxylase inhibitor 3IY for 4 hr before the experiment, following established protocols (Neckameyer, 1996; Thoener et al., 2021). 3IY-fed larvae showed different baseline behaviour compared to animals without 3IY, but overall, the differences were less pronounced than in the case of TH-RNAi (Figure 8H–K). Interestingly, the baseline tail velocity was decreased rather than increased compared to animals without 3IY feeding (Figure 8K). This means inhibiting the activity of tyrosine hydroxylase acutely or during the whole development had opposite effects on the baseline tail velocity. Upon DAN activation, 3IY-fed animals increased their bending less and reduced their tail velocity less than animals without 3IY, in both cases to levels comparable to the genetic controls (Figure 8L–O). These effects, both regarding the changes in bending and tail velocity, as well as the shifted baseline velocity, were replicated in two independent experiments (Figure 8—figure supplement 1).

Taken together, we concluded that the locomotor effects upon activation of the TH-Gal4-DANs were largely dopamine-dependent, although we note that this conclusion is based mostly on inhibiting the tyrosine hydroxylase acutely with 3IY.

Individual DANs are sufficient to increase bending and decrease velocity

Given the broad expression pattern of TH-Gal4, we wondered which cells were responsible for the observed modulations of bending and velocity. Therefore, we probed the DANs of the DL1 cluster individually with experiments that included two cycles of light stimulation (Figure 9—figure supplement 1). Overall, we found similar differences between the experimental genotype and the controls upon both stimulations, but more enhanced during the second stimulation.

For DAN-d1, we found that the experimental genotype displayed an increased bending and decreased tail velocity compared to the controls that remained trends for the first stimulation (Figure 9—figure supplement 1A–C) and were statistically significant for the second stimulation (Figure 9A–D). The effect on bending seemed to be somewhat weaker than for TH-Gal4 and expressed itself in a delayed peak rather than a general increase. The tail velocity, in contrast, was reduced to zero in both cases (compare Figures 4D–G–9A–D). However, using weaker light did not result in any significant differences between the experimental genotype and the controls, in contrast to the case of TH-Gal4 (compare Figure 5—figure supplement 1A–C, Figure 9—figure supplement 2A–C). Thus, activating DAN-d1 individually has similar effects as activating all neurons covered by TH-Gal4, but two stimulations of strong light were required to fully induce these effects.

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Next, we repeated the same experiments for DAN-f1 and DAN-g1. We found no effects on locomotion upon activating DAN-f1 (Figure 9E–G, Figure 9—figure supplement 1D–F, Figure 9—figure supplement 2D–F). For DAN-g1, we found both significantly increased bending and decreased velocity, again only for strong light and clearer during the second activation, just like for DAN-d1 (Figure 9H–L, Figure 9—figure supplement 1G–I, Figure 9—figure supplement 2G–I). Finally, we activated DAN-f1 and DAN-g1 simultaneously and found increased bending and decreased velocity comparable to the activation of DAN-g1 alone (Figure 9M–P, Figure 9—figure supplement 1K–M, Figure 9—figure supplement 2K–M).

Taken together, our experiments demonstrated that two of the three tested DANs, when activated individually, induced qualitatively similar effects on bending and velocity that also the combination of almost all DANs induced.

In this study, we found that the optogenetic activation of the same individual DANs can have multiple effects in establishing associative memories, retrieving memories and acutely modulating innate locomotion (Figure 10A). How can such diverse functions be integrated into the same neuronal circuit?

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Source data to all behavioural experiments are included in Source data 1. The IMBA software used for analysis (Thane et al., 2023) is publicly available at GitHub: https://github.com/mthane/IMBA (Thane, 2026).

1. Book

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   Data Clustering: Algorithms and Applications

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Author details

1. Naoko Toshima

   1. Leibniz Institute for Neurobiology, Magdeburg, Germany
   2. Hokkaido University, Institute for Academic Innovation, Sapporo, Japan

   Contribution

   Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9245-7494

2. Arman Behrad

   Leibniz Institute for Neurobiology, Magdeburg, Germany

   Present address

   Technical University Dresden, Computational Neuroscience, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Dresden, Germany

   Contribution

   Data curation, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Franziska Behnke

   Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   Data curation, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0000-4986-3964

4. Gauri Kaushik

   1. Hokkaido University, Institute for Academic Innovation, Sapporo, Japan
   2. Hokkaido University, Graduate School of Science, Sapporo, Japan

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Alice Weiglein

   Leibniz Institute for Neurobiology, Magdeburg, Germany

   Present address

   Otto-von Guericke University Magdeburg, Institute for Medical Data Science, Magdeburg, Germany

   Contribution

   Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

6. Martin Strauch

   RWTH Aachen University, Imaging and Computer Vision, Aachen, Germany

   Present address

   Institute of Pathology, University Clinic Aachen, RWTH Aachen University, Aachen, Germany

   Contribution

   Investigation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

7. Juliane Thoener

   Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   Investigation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8597-751X

8. Oliver Kobler

   Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   Investigation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7173-9919

9. Maia Lisandra M Wang

   1. Hokkaido University, Institute for Academic Innovation, Sapporo, Japan
   2. Hokkaido University, Graduate School of Life Science, Sapporo, Japan

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0006-8183-7639

10. Ayaka Fukushima

    1. Hokkaido University, Institute for Academic Innovation, Sapporo, Japan
    2. Hokkaido University, Graduate School of Life Science, Sapporo, Japan

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Markus Dörr

    Leibniz Institute for Neurobiology, Magdeburg, Germany

    Present address

    Technical University of Applied Sciences Würzburg-Schweinfurt, Würzburg, Germany

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Michael Schleyer

    1. Leibniz Institute for Neurobiology, Magdeburg, Germany
    2. Hokkaido University, Institute for Academic Innovation, Sapporo, Japan
    3. Hokkaido University, Graduate School of Life Science, Sapporo, Japan

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    m.schleyer@oia.hokudai.ac.jp

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8020-1483

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