Work

Convergent multi-modular architecturefor adaptive learning in Drosophila and artificial intelligence

Year: 2025

Type: article

Abstract: olfactory learning system, which exemplifies a hierarchical multi-modular architecture with specific connectivity patterns. Through an interdisciplinary comparison of anatomical and functional characteristics, we find that it reflects an elegant combination of two classical multi-modular methods in machine learning: ensemble learning (EL) and mixture-of-expert (MoE). This biological hierarchy incorporates the respective strengths of EL and MoE to improve adaptability and employ effective strategies to address their technical challenges, promoting generalization and alleviating interference on a continual basis. We further propose interdisciplinary research directions, such as developing bio-inspired machine learning models that reconcile EL and MoE, and conducting targeted biological experiments to dissect modular learning functions.

Source: iScience

DOI: 10.1016/j.isci.2025.113799

Authors: Liyuan Wang, Qian Li

Institutions: Beijing National Research Center for Information Science and Technology, Tsinghua University, Sun Yat-sen University

Language: English

Topic: Neurobiology and Insect Physiology Research

Subfield: Cellular and Molecular Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,240

Funders: National Natural Science Foundation of China, Sun Yat-sen University, Fundamental Research Funds for the Central Universities