Inability to activate Rac1-dependent forgetting contributes to behavioral inflexibility in mutants of multiple autism-risk genes

By Tao Dong, Jing He, Shiqin Wang, Lianzhang Wang, Yuqi Cheng, Yi Zhong. 2016

57 citations recorded by the source corpus.

Subfields: Aging, Cognitive Neuroscience, Molecular Biology, 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 Aging

1. Liyuan Wang
2. Qian Li
3. Yi Zhong

In Cognitive Neuroscience

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

In Molecular Biology

1. Qian Li
2. Xuchen Zhang
3. Liyuan Wang
4. Jun Zhu
5. Yi Zhong
6. Hang Su
7. Paul W. Frankland

In Neuroscience

1. Liyuan Wang
2. Jun Zhu
3. Qian Li
4. Yi Zhong
5. Hang Su
6. Paul W. Frankland
7. Xingxing Zhang
8. Blake Aaron Richards

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “Biological neural networks are capable of flexibly modulating synaptic plasticity in response to dynamic inputs, including (1) stabilization of previously-learned synaptic changes to overcome subsequent interference [154], [471], [495], which motivates the strategies of weight regularization to sele”
• Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (2023, positive): “For biological neural networks, active forgetting is able to remove outdated information and provide flexibility for adapting to a new memory 19,27,28 .”
• Genetic dissection of mutual interference between two consecutive learning tasks in Drosophila (2022, background): “Retro-I can be observed regardless of whether the time interval between two tasks is short or long (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).”
• AFEC: Active Forgetting of Negative Transfer in Continual Learning (2021, positive): “By contrast, biological neural networks can effectively learn a new experience on the basis of re-membering the old experiences, even if they conflict with each other [18, 5].”
• Modelling learning and memory in Drosophila to understand Intellectual Disabilities. (2020, unknown): “In parallel with the MB structural defects, Fmr1 mutants exhibit cognitive behavioral dysfunctions such as short and long term memory impairments (McBride et al., 2005; Bolduc et al., 2008; Kanellopoulos et al., 2012; Gatto et al., 2014; Dong et al., 2016).”
• The Persistence and Transience of Memory. (2017, background): “Consistent with this idea, recent studies provide evidence that forgetting is necessary for flexible behavior in dynamic environments (Dong et al., 2016; Epp et al., 2016; Shuai et al., 2010).”