Genetic dissection of mutual interference between two consecutive learning tasks in Drosophila

Continual learning is a natural ability of animals ranging from invertebrates to vertebrates but a great challenge for artificial intelligence (Fayek et al., 2020; Kudithipudi et al., 2022; Parisi et al., 2019; Wang et al., 2023). To achieve continual learning, the mutual interference between learning tasks, including proactive and retroactive interference, needs to be properly tuned. The interference from the previous task on the learning and memory of the current task is called Pro-I, while the interference from the current task on the memory of the following task is named Retro-I (Bouton, 1993; Miller, 2021; Wixted, 2004). Although, we have much understanding of the molecular mechanisms of learning and memory for a single learning task (Johansen et al., 2011; Kandel et al., 2014), the molecular mechanisms that modulate the interferences between different tasks remain unclear.

Drosophila is a well-studied and highly tractable genetic model organism for understanding molecular mechanisms underlying a single learning task (Davis, 2005; Noyes et al., 2021; Waddell and Quinn, 2001) as well as related human diseases (Mariano et al., 2020; Pandey and Nichols, 2011; van Alphen and van Swinderen, 2013). In recent years, Drosophila has also emerged as an excellent model for studying interference mechanisms between two associative learning tasks. Several molecules, including Rac1, Foraging, Scribble, SLC22A, Fmr1, SCAR, and Dia, have been reported to regulate Retro-I (Cervantes-Sandoval et al., 2016; Dong et al., 2016; Gai et al., 2016; Gao et al., 2019; Reaume et al., 2011; Shuai et al., 2010). Of these molecules, Scribble and SLC22A, have also been reported to regulate Pro-I (Cervantes-Sandoval et al., 2016; Gai et al., 2016). It indicates that Retro-I and Pro-I may have shared regulatory molecules. Previous studies reported that resistance to Pro-I and Retro-I is very different in patients with autism (Mottron et al., 1998), schizophrenia (Torres et al., 2001), and ADHD (Orban et al., 2022). It raises a possibility that Pro-I and Retro-I may have distinct molecular mechanisms. Of note, all reported molecules in regulating Retro-I or Pro-I also play important roles in the learning or memory of a single learning task (Cervantes-Sandoval et al., 2016; Dong et al., 2016; Gai et al., 2016; Gao et al., 2019; Reaume et al., 2011; Shuai et al., 2010). Whether there are molecules specifically responsible for modulating memory interference without affecting a single learning task remains to be determined.

Psychological studies have shown that Pro-I is closely related to content similarity, context similarity, and the time interval between the proactive task and the target task (Kliegl and Bäuml, 2021). In Drosophila, it has been known that an aversive associative learning task produces significant Pro-I on another similar task immediately following (Cervantes-Sandoval et al., 2016; Gai et al., 2016). However, the requirements for the generation of such Pro-I have not been systematically investigated, such as content similarity, context similarity, and the time interval between tasks.

Pro-I is affected by content similarity and context similarity between tasks

Consistent with previous studies (Cervantes-Sandoval et al., 2016; Gai et al., 2016), significant Pro-I was observed between two consecutive aversive tasks (Figure 1A; Associative group). This Pro-I phenomenon was still evident 1 hr later (Figure 1—figure supplement 1A) and was not affected by changing the order of the two tasks (Figure 1—figure supplement 1B). To reduce content similarity between tasks, we changed the proactive task into non-associative stimuli (Mo et al., 2022; Tully and Quinn, 1985; Zhang et al., 2018) or an appetitive learning task (Liu et al., 2012; Perisse et al., 2013; Pribbenow et al., 2022), which was less similar to the target task. With such changes, no significant Pro-I was observed (Figure 1A and B). Then, we changed the learning context of the two aversive tasks to reduce context similarity (Figure 1C). When both tasks were learned in the same context, the Pro-I was evident. In contrast, when the two tasks were learned in different contexts, the Pro-I was no longer observed. In addition, recent studies have found that a single aversive learning task can produce two opposing memory components: avoidance memory and approach memory (Jacob and Waddell, 2020; Naganos et al., 2022; Zhao et al., 2021). Here, we found that the Pro-I only affected avoidance memory (Figure 1—figure supplement 1C). Together, these data suggest that reducing inter-task similarity by changing learning content or context can release Pro-I in Drosophila, which is consistent with psychological studies (Kliegl and Bäuml, 2021).

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Pro-I and Retro-I differ in time interval sensitivity and regulatory molecules

We next tested the relationship between the Pro-I and ITI (Figure 2A–C). Flies showed significant Pro-I when the ITI was 15 min or less (0, 5, 10, and 15 min). If the ITI was 20 min or more (20, 30, and 60 min), no significant Pro-I was observed. In contrast, when the ITI was gradually increased from 0 to 60 min (0, 20, 30, and 60 min), the flies consistently exhibited significant Retro-I (Figure 2D and E). This result is consistent with previous studies on Retro-I (Cervantes-Sandoval et al., 2016; Dong et al., 2016; Gai et al., 2016; Gao et al., 2019; Reaume et al., 2011; Shuai et al., 2010).

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Given that Scribble and SLC22A regulate both Pro-I and Retro-I (Cervantes-Sandoval et al., 2016; Gai et al., 2016), other regulators of Retro-I may also affect Pro-I. Rac1 is required to mediate Retro-I when the ITI is 1.5 hr (Shuai et al., 2010). We next tested whether Rac1 also affects Retro-I with 0 min ITI and explored whether Rac1 plays a role in Pro-I (Figure 2F). The transgene dominant-negative Rac1 (Rac1-DN) or constitutively active Rac1 (Rac1-CA) was used to inhibit or increase Rac1 activity, respectively (Luo et al., 1994). Since acute manipulation of Rac1 activity in mushroom body (MB) neurons is sufficient to regulate memory performance (Gao et al., 2019; Shuai et al., 2010), Rac1 transgenes were expressed using MB-GS, a Gene-Switch (GS) tool capable of inducing transgene expression specifically in the MB only on administration of the drug RU486 (Mao et al., 2004). Suppressing (MB-GS/UAS-Rac1-DN, RU486+) or increasing (MB-GS/UAS-Rac1-CA, RU486+) Rac1 activity in MB neurons significantly mitigated or aggravated the Retro-I, respectively. However, the same manipulations did not affect the Pro-I, indicating that there is a different molecular mechanism underlying the Pro-I.

Pro-I, but not Retro-I, is bidirectionally regulated by CSW

Since the difference between Pro-I and Retro-I lies in the sensitivity to inter-task time intervals, we speculate that molecules that modulate ‘time interval’ effects may specifically regulate Pro-I. Given that CSW, an evolutionarily conserved protein tyrosine phosphatase SHP2, has been reported to regulate the ‘time interval’ effect in long-term memory (LTM) formation in Drosophila (Pagani et al., 2009), we tested the role of CSW in Pro-I. Compared to the parental control group (MB-GS/+, RU486+), acutely knocking down CSW in MB neurons using two independent RNAi lines (MB-GS/UAS-csw-RNAi-1 and MB-GS/UAS-csw-RNAi-2; RU486+) showed more severe Pro-I, which can be better reflected by the Pro-I+/Pro-I– ratio (Figure 3A). To further determine this result, we added a comparison with uninduced control groups (Figure 3B) or genetic control groups (Figure 3—figure supplement 1A and 1D) and obtained consistent results. When the ITI was 20 min, Pro-I was no longer observed in control flies but was still present in flies with acute knockdown of CSW (Figure 3C; Figure 3—figure supplement 1B). Conversely, acute overexpression of CSW in MB neurons (MB-GS/UAS-csw, RU486+) reduced Pro-I by using a newly constructed transgenic strain (Figure 3D and E; Figure 3—figure supplement 1C and E) and a previously reported strain (Botham et al., 2008; Figure 3—figure supplement 1F). Of note, bidirectional manipulation of CSW expression in MB neurons did not affect Retro-I (Figure 3F).

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The MB contains ~2000 intrinsic neurons called Kenyon cells (KCs), which are further divided into three major types: γ neurons (~675), α/β neurons (~990), and α’/β’neurons (~350) (Aso et al., 2014). We next sought to determine whether the modulatory effects of CSW on Pro-I could be narrowed to a certain type of MB neurons. 5-HT1B-Gal4 (Gao et al., 2019; Shyu et al., 2017; Yuan et al., 2005), VT30604-Gal4 (Wu et al., 2013), and C739-Gal4 (O’Dell et al., 1995) were used to drive transgene expression in γ neurons, α’/β’ neurons, and α/β neurons, respectively. Overexpressing CSW in γ neurons or α/β neurons, but not in α’/β’ neurons showed higher performance with Pro-I when compared with their respective genetic controls (Figure 3—figure supplement 1G). To rule out possible developmental effects, as there is no available GS tool for subtypes of MB neurons, we employed another inducible expression system called TARGET, which relies on a temperature shift to induce transgene expression (McGuire et al., 2003). Acute overexpression of CSW in γ neurons (Gal80^ts/+; 5-HT1B/UAS-csw, induced), but not in the α/β neurons (C739/+; Gal80^ts/UAS-csw, induced), significantly reduced the Pro-I compared with their respective controls (Figure 3G and H). In contrast, acute knockdown of CSW in γ neurons increased the Pro-I (Figure 3I). In uninduced flies, Pro-I was not affected (Figure 3—figure supplement 1H). These data suggest that CSW regulates Pro-I in MB γ neurons. In addition, the result in Figure 1C shows that no Pro-I occurred when the two tasks were learned in different contexts. In this case, knocking down the CSW remained ineffective (Figure 3—figure supplement 1I).

The Raf/MAPK pathway acts downstream of the CSW to regulate Pro-I

CSW is generally considered as a positive regulator of Ras/MAPK signaling (Perkins et al., 1996). And the regulation of CSW on the ‘time interval’ effect in LTM formation is also thought to be via the MAPK pathway (Pagani et al., 2009). So, we further explored whether CSW affects Pro-I through the MAPK pathway. Feeding U0126, a widely used inhibitor of the MAPK pathway (Thomas and Huganir, 2004), did not affect single-task learning, but significantly exacerbated Pro-I when learning two consecutive tasks (Figure 4A). Flies with acute genetic knockdown of MAPK in MB neurons (MB-GS/UAS-MAPK-RNAi, RU486+) also exhibited more severe Pro-I than uninduced and parental control flies (Figure 4B; Figure 4—figure supplement 1A).

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Consistent with these results, Raf kinase, a classical upstream regulator of MAPK (Thomas and Huganir, 2004), was found to bidirectionally regulate the Pro-I in MB neurons like CSW (Figure 4C and D). Acutely knocking down Raf significantly exacerbated the Pro-I, while acute overexpression of Raf-GOF, which encodes a constitutively active Raf kinase (Brand and Perrimon, 1994), significantly reduced Pro-I. When Raf-GOF and CSW-RNAi were co-expressed, Raf-GOF expression dominated the effect on Pro-I, indicating that Raf acts downstream of CSW (Figure 4E). Thus, our data support that CSW regulates Pro-I through Raf/MAPK pathway.

Our previous study found that the Raf/MAPK pathway is activated by learning to protect memory retention via non-muscle myosin Ⅱ Sqh after single-task learning (Zhang et al., 2018). Although the regulatory effect of CSW on Pro-I in two consecutive task learning was also through the Raf/MAPK pathway, manipulating CSW did not affect the learning and memory of a single task (Figure 4—figure supplement 1B and C), unlike Raf (Figure 4—figure supplement 1D; Zhang et al., 2018). Interestingly, as a downstream molecule of the Raf/MAPK pathway in regulating single-task memory (Zhang et al., 2018), Sqh did not participate in the Pro-I between the two tasks (Figure 4—figure supplement 1E). These results suggest that learning two different tasks consecutively may specifically recruit CSW to activate the Raf/MAPK pathway to reduce Pro-I through downstream molecules different from Sqh, a mechanism that differs from the memory protection mechanism triggered by a single learning task.

Our findings support a model for understanding molecular mechanisms of mutual interference between two successive learning tasks in Drosophila (Figure 4F). First, in learning two different associative tasks in succession, Pro-I and Retro-I occur simultaneously when the time distance between tasks is close (<20 min), while only Retro-I could be observed when the time distance was far (>20 min). Second, we identify a molecular pathway that specifically regulates Pro-I: the CSW/Raf/MAPK pathway. The upstream signaling molecule CSW of this pathway is not involved in single-task learning and memory. In learning two different tasks close in time, although Pro-I and Retro-I occurred simultaneously, CSW was only involved in regulating Pro-I. Third, Rac1 specifically regulates Retro-I without affecting Pro-I when two different tasks are temporally close. Consistently, the Rac1/SCAR/Dia pathway has been reported to regulate Retro-I when the two tasks are temporally distant (1.5 hr interval) (Gao et al., 2019; Shuai et al., 2010).

According to our data, Pro-I is no longer evident when two aversive olfactory learning tasks are separated by more than 20 min (Figure 2B and C). After 20 min, the memory performance of the proactive task partially declines and a consolidated memory component called anesthesia-resistant memory (ARM) is largely formed (Quinn and Dudai, 1976; Tully et al., 1994; Tully and Quinn, 1985; Zhang et al., 2016). It raises a possibility that the decay or consolidation of the proactive task may release Pro-I. Based on this possibility, the reason why the CSW/Raf/MAPK pathway can bidirectionally regulate Pro-I can be explained because of the ability to modulate the decay or consolidation of proactive task memory. However, it is not supported by the following experimental results. First, acute knockdown or overexpression of CSW in MB neurons did not affect the immediate and early memory performance of a single task (Figure 4—figure supplement 1B and C). Second, in the Retro-I paradigm, the proactive task in the Pro-I paradigm became the target task and was directly examined, however, its performance was not affected by manipulating CSW (Figure 3F). Third, manipulating Raf/MAPK pathway in MB neurons during the adult stage did not affect immediate memory and ARM performance after a single aversive learning task (Zhang et al., 2018).

Our current findings suggest that inter-task similarity from different dimensions contributes to Pro-I. First, the content similarity between tasks can influence Pro-I. Two similar aversive associative tasks could produce significant Pro-I. However, Pro-I was not observed after reducing the inter-task content similarity by replacing the proactive task with non-associative stimuli or an appetitive associative task (Figure 1A and B). Due to the similarity of content, both tasks are more likely to use the same neural circuit thus increasing the possibility of Pro-I. Second, the similarity of environmental contexts between tasks plays an important role in Pro-I. By using different colors of light, we changed the environmental context similarity of the two tasks during the learning stage without affecting the content similarity. Pro-I was significantly released when the similarity of environmental contexts between tasks was reduced (Figure 1C). Third, the similarity of time contexts between tasks also contributes to Pro-I. Time is also considered as a context (Bouton, 1993). When the ITI was increased to more than 20 min, the Pro-I is no longer significant (Figure 2A–C). This phenomenon can be explained as the 20 min ITI might make the time contexts of the two tasks significantly different, thus reducing the inter-task similarity. According to this explanation, the CSW/Raf/MAPK pathway can regulate Pro-I probably by affecting time context similarity between tasks. Aversive learning can induce MAPK activation that peaks at around 20 min, which can be modulated by CSW and Raf (Pagani et al., 2009; Zhang et al., 2018). Overexpression of CSW in MB neurons is reported to accelerate MAPK activation (Pagani et al., 2009) and can reduce Pro-I (Figure 3D, E and H). Knocking down Raf in MB neurons shortens the duration of MAPK activation (Zhang et al., 2018) and can exacerbate Pro-I (Figure 4C). With these findings, an interesting idea is that MAPK activation can help distinguish the time contexts of the two tasks. Thus, the CSW/Raf/MAPK pathway can bidirectionally regulate Pro-I by affecting the similarity of time contexts between tasks. These properties of Pro-I that we obtained using the Drosophila model are consistent with psychological studies (Kliegl and Bäuml, 2021).

Although Pro-I in the current work only occurs when the interval between two tasks is less than 20 min, the phenomenon of Pro-I varies across biological learning systems and tasks (Crossley et al., 2019; Epp et al., 2016; Jonides and Nee, 2006). Future works are required to determine whether orthologues of CSW also participate in other forms of Pro-I. Interestingly, the molecules regulating Pro-I and Retro-I in Drosophila are involved in different diseases with varying levels of intellectual disability. Rac1 and Fmr1, which regulate Retro-I in Drosophila, are high-risk genes for autism (Lord et al., 2020). Mutations in PTPN11, a human orthologue of csw, account for more than half of Noonan syndrome (Roberts et al., 2013). Therefore, further studies on the molecular mechanisms underlying Pro-I and Retro-I may also contribute to the understanding of the pathogenesis of autism and Noonan syndrome. In addition, studies of Pro-I and Retro-I from the synaptic and neural circuit levels may also inspire continual learning in artificial intelligence (Wang et al., 2021; Wang et al., 2022).

All data generated or analysed during this study are included in the manuscript and supporting file.

1. 1. Aso Y
   2. Hattori D
   3. Yu Y
   4. Johnston RM
   5. Iyer NA
   6. Ngo T-TB
   7. Dionne H
   8. Abbott LF
   9. Axel R
   10. Tanimoto H
   11. Rubin GM

   (2014) The neuronal architecture of the mushroom body provides a logic for associative learning

   eLife 3:e04577.

   https://doi.org/10.7554/eLife.04577

   • PubMed
   • Google Scholar
2. 1. Botham CM
   2. Wandler AM
   3. Guillemin K

   (2008) A transgenic Drosophila model demonstrates that the helicobacter pylori caga protein functions as A eukaryotic gab adaptor

   PLOS Pathogens 4:e1000064.

   https://doi.org/10.1371/journal.ppat.1000064

   • PubMed
   • Google Scholar
3. 1. Bouton ME

   (1993) Context, time, and memory retrieval in the interference paradigms of Pavlovian learning

   Psychological Bulletin 114:80–99.

   https://doi.org/10.1037/0033-2909.114.1.80

   • PubMed
   • Google Scholar
4. 1. Brand AH
   2. Perrimon N

   (1994) Raf acts downstream of the EGF receptor to determine dorsoventral polarity during Drosophila oogenesis

   Genes & Development 8:629–639.

   https://doi.org/10.1101/gad.8.5.629

   • PubMed
   • Google Scholar
5. 1. Cervantes-Sandoval I
   2. Chakraborty M
   3. MacMullen C
   4. Davis RL

   (2016) Scribble scaffolds a signalosome for active forgetting

   Neuron 90:1230–1242.

   https://doi.org/10.1016/j.neuron.2016.05.010

   • PubMed
   • Google Scholar
6. 1. Crossley M
   2. Lorenzetti FD
   3. Naskar S
   4. O’Shea M
   5. Kemenes G
   6. Benjamin PR
   7. Kemenes I

   (2019) Proactive and retroactive interference with associative memory consolidation in the snail Lymnaea is time and circuit dependent

   Communications Biology 2:242.

   https://doi.org/10.1038/s42003-019-0470-y

   • PubMed
   • Google Scholar
7. 1. Davis RL

   (2005) Olfactory memory formation in Drosophila: from molecular to systems neuroscience

   Annual Review of Neuroscience 28:275–302.

   https://doi.org/10.1146/annurev.neuro.28.061604.135651

   • PubMed
   • Google Scholar
8. 1. Dong T
   2. He J
   3. Wang S
   4. Wang L
   5. Cheng Y
   6. Zhong Y

   (2016) Inability to activate rac1-dependent forgetting contributes to behavioral inflexibility in mutants of multiple autism-risk genes

   PNAS 113:7644–7649.

   https://doi.org/10.1073/pnas.1602152113

   • PubMed
   • Google Scholar
9. 1. Epp JR
   2. Silva Mera R
   3. Köhler S
   4. Josselyn SA
   5. Frankland PW

   (2016) Neurogenesis-mediated forgetting minimizes proactive interference

   Nature Communications 7:10838.

   https://doi.org/10.1038/ncomms10838

   • PubMed
   • Google Scholar
10. 1. Fayek HM
    2. Cavedon L
    3. Wu HR

    (2020) Progressive learning: a deep learning framework for continual learning

    Neural Networks 128:345–357.

    https://doi.org/10.1016/j.neunet.2020.05.011

    • PubMed
    • Google Scholar
11. 1. Gai Y
    2. Liu Z
    3. Cervantes-Sandoval I
    4. Davis RL

    (2016) Drosophila SLC22A transporter is a memory suppressor gene that influences cholinergic neurotransmission to the mushroom bodies

    Neuron 90:581–595.

    https://doi.org/10.1016/j.neuron.2016.03.017

    • PubMed
    • Google Scholar
12. 1. Gao Y
    2. Shuai Y
    3. Zhang X
    4. Peng Y
    5. Wang L
    6. He J
    7. Zhong Y
    8. Li Q

    (2019) Genetic dissection of active forgetting in labile and consolidated memories in Drosophila

    PNAS 116:21191–21197.

    https://doi.org/10.1073/pnas.1903763116

    • PubMed
    • Google Scholar
13. 1. Jacob PF
    2. Waddell S

    (2020) Spaced training forms complementary long-term memories of opposite valence in Drosophila

    Neuron 106:977–991.

    https://doi.org/10.1016/j.neuron.2020.03.013

    • PubMed
    • Google Scholar
14. 1. Johansen JP
    2. Cain CK
    3. Ostroff LE
    4. LeDoux JE

    (2011) Molecular mechanisms of fear learning and memory

    Cell 147:509–524.

    https://doi.org/10.1016/j.cell.2011.10.009

    • PubMed
    • Google Scholar
15. 1. Jonides J
    2. Nee DE

    (2006) Brain mechanisms of proactive interference in working memory

    Neuroscience 139:181–193.

    https://doi.org/10.1016/j.neuroscience.2005.06.042

    • PubMed
    • Google Scholar
16. 1. Kandel ER
    2. Dudai Y
    3. Mayford MR

    (2014) The molecular and systems biology of memory

    Cell 157:163–186.

    https://doi.org/10.1016/j.cell.2014.03.001

    • PubMed
    • Google Scholar
17. 1. Kliegl O
    2. Bäuml K-HT

    (2021) Buildup and release from proactive interference-cognitive and neural mechanisms

    Neuroscience and Biobehavioral Reviews 120:264–278.

    https://doi.org/10.1016/j.neubiorev.2020.10.028

    • PubMed
    • Google Scholar
18. 1. Kudithipudi D
    2. Aguilar-Simon M
    3. Babb J
    4. Bazhenov M
    5. Blackiston D
    6. Bongard J
    7. Brna AP
    8. Chakravarthi Raja S
    9. Cheney N
    10. Clune J
    11. Daram A
    12. Fusi S
    13. Helfer P
    14. Kay L
    15. Ketz N
    16. Kira Z
    17. Kolouri S
    18. Krichmar JL
    19. Kriegman S
    20. Levin M
    21. Madireddy S
    22. Manicka S
    23. Marjaninejad A
    24. McNaughton B
    25. Miikkulainen R
    26. Navratilova Z
    27. Pandit T
    28. Parker A
    29. Pilly PK
    30. Risi S
    31. Sejnowski TJ
    32. Soltoggio A
    33. Soures N
    34. Tolias AS
    35. Urbina-Meléndez D
    36. Valero-Cuevas FJ
    37. van de Ven GM
    38. Vogelstein JT
    39. Wang F
    40. Weiss R
    41. Yanguas-Gil A
    42. Zou X
    43. Siegelmann H

    (2022) Biological underpinnings for lifelong learning machines

    Nature Machine Intelligence 4:196–210.

    https://doi.org/10.1038/s42256-022-00452-0

    • Google Scholar
19. 1. Liu C
    2. Plaçais P-Y
    3. Yamagata N
    4. Pfeiffer BD
    5. Aso Y
    6. Friedrich AB
    7. Siwanowicz I
    8. Rubin GM
    9. Preat T
    10. Tanimoto H

    (2012) A subset of dopamine neurons signals reward for odour memory in Drosophila

    Nature 488:512–516.

    https://doi.org/10.1038/nature11304

    • PubMed
    • Google Scholar
20. 1. Lord C
    2. Brugha TS
    3. Charman T
    4. Cusack J
    5. Dumas G
    6. Frazier T
    7. Jones EJH
    8. Jones RM
    9. Pickles A
    10. State MW
    11. Taylor JL
    12. Veenstra-VanderWeele J

    (2020) Autism spectrum disorder

    Nature Reviews. Disease Primers 6:5.

    https://doi.org/10.1038/s41572-019-0138-4

    • PubMed
    • Google Scholar
21. 1. Luo L
    2. Liao YJ
    3. Jan LY
    4. Jan YN

    (1994) Distinct morphogenetic functions of similar small gtpases: Drosophila drac1 is involved in axonal outgrowth and myoblast fusion

    Genes & Development 8:1787–1802.

    https://doi.org/10.1101/gad.8.15.1787

    • PubMed
    • Google Scholar
22. 1. Mao ZM
    2. Roman G
    3. Zong L
    4. Davis RL

    (2004) Pharmacogenetic rescue in time and space of the rutabaga memory impairment by using gene-switch

    PNAS 101:198–203.

    https://doi.org/10.1073/pnas.0306128101

    • PubMed
    • Google Scholar
23. 1. Mariano V
    2. Achsel T
    3. Bagni C
    4. Kanellopoulos AK

    (2020) Modelling learning and memory in Drosophila to understand intellectual disabilities

    Neuroscience 445:12–30.

    https://doi.org/10.1016/j.neuroscience.2020.07.034

    • PubMed
    • Google Scholar
24. 1. McGuire SE
    2. Le PT
    3. Osborn AJ
    4. Matsumoto K
    5. Davis RL

    (2003) Spatiotemporal rescue of memory dysfunction in Drosophila

    Science 302:1765–1768.

    https://doi.org/10.1126/science.1089035

    • PubMed
    • Google Scholar
25. 1. Miller RR

    (2021) Failures of memory and the fate of forgotten memories

    Neurobiology of Learning and Memory 181:107426.

    https://doi.org/10.1016/j.nlm.2021.107426

    • PubMed
    • Google Scholar
26. 1. Mo H
    2. Wang L
    3. Chen Y
    4. Zhang X
    5. Huang N
    6. Liu T
    7. Hu W
    8. Zhong Y
    9. Li Q

    (2022) Age-Related memory vulnerability to interfering stimuli is caused by gradual loss of MAPK-dependent protection in Drosophila

    Aging Cell 21:e13628.

    https://doi.org/10.1111/acel.13628

    • PubMed
    • Google Scholar
27. 1. Mottron L
    2. Belleville S
    3. Stip E
    4. Morasse K

    (1998) Atypical memory performance in an autistic savant

    Memory 6:593–607.

    https://doi.org/10.1080/741943372

    • PubMed
    • Google Scholar
28. 1. Naganos S
    2. Ueno K
    3. Horiuchi J
    4. Saitoe M

    (2022) Dopamine activity in projection neurons regulates short-lasting olfactory approach memory in Drosophila

    The European Journal of Neuroscience 56:4558–4571.

    https://doi.org/10.1111/ejn.15766

    • PubMed
    • Google Scholar
29. 1. Noyes NC
    2. Phan A
    3. Davis RL

    (2021) Memory suppressor genes: modulating acquisition, consolidation, and forgetting

    Neuron 109:3211–3227.

    https://doi.org/10.1016/j.neuron.2021.08.001

    • PubMed
    • Google Scholar
30. 1. O’Dell KM
    2. Armstrong JD
    3. Yang MY
    4. Kaiser K

    (1995) Functional dissection of the Drosophila mushroom bodies by selective feminization of genetically defined subcompartments

    Neuron 15:55–61.

    https://doi.org/10.1016/0896-6273(95)90064-0

    • PubMed
    • Google Scholar
31. 1. Orban SA
    2. Festini SB
    3. Yuen EK
    4. Friedman LM

    (2022) Verbal memory interference in attention-deficit hyperactivity disorder: a meta-analytic review

    Journal of Attention Disorders 26:1549–1562.

    https://doi.org/10.1177/10870547221085515

    • PubMed
    • Google Scholar
32. 1. Pagani MR
    2. Oishi K
    3. Gelb BD
    4. Zhong Y

    (2009) The phosphatase SHP2 regulates the spacing effect for long-term memory induction

    Cell 139:186–198.

    https://doi.org/10.1016/j.cell.2009.08.033

    • PubMed
    • Google Scholar
33. 1. Pandey UB
    2. Nichols CD

    (2011) Human disease models in Drosophila melanogaster and the role of the fly in therapeutic drug discovery

    Pharmacological Reviews 63:411–436.

    https://doi.org/10.1124/pr.110.003293

    • PubMed
    • Google Scholar
34. 1. Parisi GI
    2. Kemker R
    3. Part JL
    4. Kanan C
    5. Wermter S

    (2019) Continual lifelong learning with neural networks: a review

    Neural Networks 113:54–71.

    https://doi.org/10.1016/j.neunet.2019.01.012

    • PubMed
    • Google Scholar
35. 1. Perisse E
    2. Yin Y
    3. Lin AC
    4. Lin S
    5. Huetteroth W
    6. Waddell S

    (2013) Different kenyon cell populations drive learned approach and avoidance in Drosophila

    Neuron 79:945–956.

    https://doi.org/10.1016/j.neuron.2013.07.045

    • PubMed
    • Google Scholar
36. 1. Perkins LA
    2. Johnson MR
    3. Melnick MB
    4. Perrimon N

    (1996) The nonreceptor protein tyrosine phosphatase corkscrew functions in multiple receptor tyrosine kinase pathways in Drosophila

    Developmental Biology 180:63–81.

    https://doi.org/10.1006/dbio.1996.0285

    • PubMed
    • Google Scholar
37. 1. Pribbenow C
    2. Chen YC
    3. Heim MM
    4. Laber D
    5. Reubold S
    6. Reynolds E
    7. Balles I
    8. Fernández-D V Alquicira T
    9. Suárez-Grimalt R
    10. Scheunemann L
    11. Rauch C
    12. Matkovic T
    13. Rösner J
    14. Lichtner G
    15. Jagannathan SR
    16. Owald D

    (2022) Postsynaptic plasticity of cholinergic synapses underlies the induction and expression of appetitive and familiarity memories in Drosophila

    eLife 11:e80445.

    https://doi.org/10.7554/eLife.80445

    • PubMed
    • Google Scholar
38. 1. Quinn WG
    2. Dudai Y

    (1976) Memory phases in Drosophila

    Nature 262:576–577.

    https://doi.org/10.1038/262576a0

    • PubMed
    • Google Scholar
39. 1. Reaume CJ
    2. Sokolowski MB
    3. Mery F

    (2011) A natural genetic polymorphism affects retroactive interference in Drosophila melanogaster

    Proceedings. Biological Sciences 278:91–98.

    https://doi.org/10.1098/rspb.2010.1337

    • PubMed
    • Google Scholar
40. 1. Roberts AE
    2. Allanson JE
    3. Tartaglia M
    4. Gelb BD

    (2013) Noonan syndrome

    Lancet 381:333–342.

    https://doi.org/10.1016/S0140-6736(12)61023-X

    • PubMed
    • Google Scholar
41. 1. Shuai YC
    2. Lu BY
    3. Hu Y
    4. Wang LZ
    5. Sun K
    6. Zhong Y

    (2010) Forgetting is regulated through Rac activity in Drosophila

    Cell 140:579–589.

    https://doi.org/10.1016/j.cell.2009.12.044

    • PubMed
    • Google Scholar
42. 1. Shyu WH
    2. Chiu TH
    3. Chiang MH
    4. Cheng YC
    5. Tsai YL
    6. Fu TF
    7. Wu T
    8. Wu CL

    (2017) Neural circuits for long-term water-reward memory processing in thirsty Drosophila

    Nature Communications 8:15230.

    https://doi.org/10.1038/ncomms15230

    • PubMed
    • Google Scholar
43. 1. Thomas GM
    2. Huganir RL

    (2004) Mapk cascade signalling and synaptic plasticity

    Nature Reviews. Neuroscience 5:173–183.

    https://doi.org/10.1038/nrn1346

    • PubMed
    • Google Scholar
44. 1. Torres IJ
    2. Flashman LA
    3. O’Leary DS
    4. Andreasen NC

    (2001) Effects of retroactive and proactive interference on word list recall in schizophrenia

    Journal of the International Neuropsychological Society 7:481–490.

    https://doi.org/10.1017/s1355617701744049

    • PubMed
    • Google Scholar
45. 1. Tully T
    2. Quinn WG

    (1985) Classical conditioning and retention in normal and mutant Drosophila melanogaster

    Journal of Comparative Physiology. A, Sensory, Neural, and Behavioral Physiology 157:263–277.

    https://doi.org/10.1007/BF01350033

    • PubMed
    • Google Scholar
46. 1. Tully T
    2. Preat T
    3. Boynton SC
    4. Del Vecchio M

    (1994) Genetic dissection of consolidated memory in Drosophila

    Cell 79:35–47.

    https://doi.org/10.1016/0092-8674(94)90398-0

    • PubMed
    • Google Scholar
47. 1. van Alphen B
    2. van Swinderen B

    (2013) Drosophila strategies to study psychiatric disorders

    Brain Research Bulletin 92:1–11.

    https://doi.org/10.1016/j.brainresbull.2011.09.007

    • PubMed
    • Google Scholar
48. 1. Waddell S
    2. Quinn WG

    (2001) Flies, genes, and learning

    Annual Review of Neuroscience 24:1283–1309.

    https://doi.org/10.1146/annurev.neuro.24.1.1283

    • PubMed
    • Google Scholar
49. Conference

    1. Wang L
    2. Zhang M
    3. Jia Z
    4. Li Q
    5. Ma K
    6. Bao C
    7. Zhong Y

    (2021)

    AFEC: Active Forgetting of Negative Transfer in Continual Learning

    Advances in Neural Information Processing Systems 34 NeurIPS 2021.

    • Google Scholar
50. Conference

    1. Wang L
    2. Zhang X
    3. Li Q
    4. Zhu J
    5. Zhong Y

    (2022) CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One

    Computer Vision – ECCV 2022. pp. 254–271.

    https://doi.org/10.1007/978-3-031-19809-0

    • Google Scholar
51. Preprint

    1. Wang L
    2. Zhang X
    3. Su H
    4. Zhu J

    (2023) A comprehensive survey of continual learning: theory, method and application

    arXiv.

    https://arxiv.org/abs/2302.00487

    • Google Scholar
52. 1. Wixted JT

    (2004) The psychology and neuroscience of forgetting

    Annual Review of Psychology 55:235–269.

    https://doi.org/10.1146/annurev.psych.55.090902.141555

    • PubMed
    • Google Scholar
53. 1. Wu C-L
    2. Shih M-FM
    3. Lee P-T
    4. Chiang A-S

    (2013) An octopamine-mushroom body circuit modulates the formation of anesthesia-resistant memory in Drosophila

    Current Biology 23:2346–2354.

    https://doi.org/10.1016/j.cub.2013.09.056

    • PubMed
    • Google Scholar
54. 1. Yang Q
    2. Zhou J
    3. Wang L
    4. Hu W
    5. Zhong Y
    6. Li Q

    (2023) Spontaneous recovery of reward memory through active forgetting of extinction memory

    Current Biology 33:848.e3.

    https://doi.org/10.1016/j.cub.2023.01.022

    • PubMed
    • Google Scholar
55. 1. Yuan Q
    2. Lin F
    3. Zheng X
    4. Sehgal A

    (2005) Serotonin modulates circadian entrainment in Drosophila

    Neuron 47:115–127.

    https://doi.org/10.1016/j.neuron.2005.05.027

    • PubMed
    • Google Scholar
56. 1. Zhang X
    2. Li Q
    3. Wang L
    4. Liu ZJ
    5. Zhong Y

    (2016) Cdc42-Dependent forgetting regulates repetition effect in prolonging memory retention

    Cell Reports 16:817–825.

    https://doi.org/10.1016/j.celrep.2016.06.041

    • PubMed
    • Google Scholar
57. 1. Zhang X
    2. Li Q
    3. Wang L
    4. Liu ZJ
    5. Zhong Y

    (2018) Active protection: learning-activated raf/MAPK activity protects labile memory from rac1-independent forgetting

    Neuron 98:142–155.

    https://doi.org/10.1016/j.neuron.2018.02.025

    • PubMed
    • Google Scholar
58. 1. Zhao B
    2. Sun J
    3. Li Q
    4. Zhong Y

    (2021) Differential conditioning produces merged long-term memory in Drosophila

    eLife 10:e66499.

    https://doi.org/10.7554/eLife.66499

    • PubMed
    • Google Scholar

Author details

1. Jianjian Zhao

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Xuchen Zhang

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Present address

   Department of Molecular and Cellular Physiology and Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Bohan Zhao

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Present address

   Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, San Diego, United States

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9177-1278

4. Wantong Hu

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

5. Tongxin Diao

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Liyuan Wang

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

7. Yi Zhong

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Supervision, Funding acquisition

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7927-5976

8. Qian Li

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   liqian8@tsinghua.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7317-1570

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