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

By Jianjian Zhao, Xuchen Zhang, Bohan Zhao, Liyuan Wang, Wantong Hu, Yi Zhong, Qian Li. 2022

3 citations recorded by the source corpus.

Animals can continuously learn different tasks to adapt to changing environments and therefore have strategies to effectively cope with inter-task interference, including both proactive interference (Pro-I) and retroactive interference (Retro-I). Many biological mechanisms are known to contribute to learning, memory, and forgetting for a single task, however, mechanisms involved only when learning sequential different tasks are relatively poorly understood. Here, we dissect the respective molecular mechanisms of Pro-I and Retro-I between two consecutive associative learning tasks in Drosophila. Pro-I is more sensitive to inter-task interval (ITI) than Retro-I. They occur together at short ITI (<20 min), while only Retro-I remains significant at ITI beyond 20 min. Acutely overexpressing Corkscrew (CSW), an evolutionarily conserved protein tyrosine phosphatase SHP2, in mushroom body (MB) neurons reduces Pro-I, whereas acute knockdown of CSW exacerbates Pro-I. Such function of CSW is further found to rely on the γ subset of MB neurons and the downstream Raf/MAPK pathway. In contrast, manipulating CSW does not affect Retro-I as well as a single learning task. Interestingly, manipulation of Rac1, a molecule that regulates Retro-I, does not affect Pro-I. Thus, our findings suggest that learning different tasks consecutively triggers distinct molecular mechanisms to tune proactive and retroactive interference.

Subfields: Cellular and Molecular Neuroscience, Ecology, Immunology, Computational Biology & Bioinformatics, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Liyuan Wang
2. Qian Li
3. Yi Zhong
4. Jun Zhu
5. Xingxing Zhang
6. Hang Su
7. Mingtian Zhang
8. Xuchen Zhang

In Ecology

1. Liyuan Wang

In Immunology

1. Qian Li
2. Yi Zhong

In Neuroscience

1. Liyuan Wang
2. Jun Zhu
3. Qian Li
4. Yi Zhong
5. Hang Su
6. Xingxing Zhang

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “As biological learning is naturally on a continual basis [151], [234], [515], its underlying mechanisms provide an excellent reference for AI models.”
• Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (2023, background): “In particular, the γ subset of the Drosophila mushroom body ( γ MB) is a biological learning system that is essential for coping with different tasks in succession and enjoys relatively clear and in-depth understanding at both functional and anatomical levels [21–28] (Fig.”