The Drosophila Mushroom Body: From Architecture to Algorithm in a Learning Circuit.

By M. Modi, Yichun Shuai, Glenn C. Turner. 2020

178 citations recorded by the source corpus.

The Drosophila brain contains a relatively simple circuit for forming Pavlovian associations, yet it achieves many operations common across memory systems. Recent advances have established a clear framework for Drosophila learning and revealed the following key operations: a) pattern separation, whereby dense combinatorial representations of odors are preprocessed to generate highly specific, nonoverlapping odor patterns used for learning; b) convergence, in which sensory information is funneled to a small set of output neurons that guide behavioral actions; c) plasticity, where changing the mapping of sensory input to behavioral output requires a strong reinforcement signal, which is also modulated by internal state and environmental context; and d) modularization, in which a memory consists of multiple parallel traces, which are distinct in stability and flexibility and exist in anatomically well-defined modules within the network. Cross-module interactions allow for higher-order effects where past experience influences future learning. Many of these operations have parallels with processes of memory formation and action selection in more complex brains. Expected final online publication date for the Annual Review of Neuroscience, Volume 43 is July 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Subfields: Cellular and Molecular Neuroscience, Genetics, Sensory Systems, 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. Bohan Zhao
4. Yi Zhong
5. Jun Zhu
6. Xingxing Zhang
7. Hang Su
8. Mingtian Zhang

In Genetics

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

In Sensory Systems

1. Yi Zhong
2. Qian Li

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): “, a memory center in fruit fly) coordinating multiple parallel compartments to process the sequentially-arrived experiences [23], [24], [78], [310], [421], corresponding to the use of modular networks in continual learning [15], [353], [430].”
• Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (2023, positive): “The outputs of these compartments are carried by distinct MB output neurons and integrated in a weighted-sum fashion to guide adaptive behaviours 22,34,35 .”
• CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One (2022, neutral): “In contrast, the robust biological learning system applies multiple compartments (i.e, sub-networks) to process sequential experiences in parallel, and integrates their outputs in a weighted-sum fashion to guide adaptive behaviors [3,9,32].”
• Differential conditioning produces merged long-term memory in Drosophila (2021, background): “In Drosophila, associative olfactory memories are mainly encoded by the of DANs (Aso and Rubin, 2016; Claridge-Chang et al., 2009; Cognigni et al., 2018) through alterations of the connections between KCs and MBONs (Aso et al., 2014a; Aso et al., 2014b; Cognigni et al., 2018; Dubnau and Chiang, 2013”