Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila
Data files
Apr 23, 2025 version files 360.69 KB
Abstract
In Drosophila melanogaster, olfactory encoding in the mushroom body (MB) involves thousands of Kenyon cells (KCs) processing inputs from hundreds of projection neurons (PNs). Recent data challenge the notion of random PN-to-KC connectivity, revealing preferential connections between food-related PNs and specific KCs. Our study further uncovers a broader picture—an L-shaped hybrid network, supported by spatial patterning: food-related PNs diverge across KC classes, while pheromone-sensitive PNs converge on γ KCs. α/β KCs specialize in food odors, while γ KCs integrate diverse inputs. Such spatial arrangement extends further to the antennal lobe (AL) and lateral horn (LH), shaping a systematic olfactory landscape. Moreover, our functional validations align with computational predictions of KC odor encoding based on the hybrid connectivity, correlating PN-KC activity with behavioral preferences. Additionally, our simulations showcase the network’s augmented sensitivity and precise discrimination abilities, underscoring the computational benefits of this hybrid architecture in olfactory processing.
Description of the data and file structure
Project Overview
We provide the code used to reproduce the results presented in our paper, "Hybrid Neural Networks in the Mushroom Body Drive Olfactory Preference in Drosophila."
Our code performs a range of connectomic analyses using data from the hemibrain and FAFB datasets, and further integrates functional data from the DoOR database and behavioral data from Knaden et al., 2012.
Due to licensing restrictions, the associated data is not included here. To access the data, please visit: https://doi.org/10.5281/zenodo.15263535.
References:
Behavioral Data:
- Knaden, M., Strutz, A., Ahsan, J., Sachse, S., & Hansson, B. S. (2012). Spatial representation of odorant valence in an insect brain. Cell reports, 1(4), 392-399.
Functional Data:
- Münch, D., & Galizia, C. G. (2016). DoOR 2.0-comprehensive mapping of Drosophila melanogaster odorant responses. Scientific reports, 6(1), 21841.
Connectomic Data:
- Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S. Y., Hayworth, K. J., ... & Plaza, S. M. (2020). A connectome and analysis of the adult Drosophila central brain. elife, 9, e57443.
- Zheng, Z., Li, F., Fisher, C., Ali, I. J., Sharifi, N., Calle-Schuler, S., ... & Bock, D. D. (2022). Structured sampling of olfactory input by the fly mushroom body. Current Biology, 32(15), 3334-3349.
- Zheng, Z., Lauritzen, J. S., Perlman, E., Robinson, C. G., Nichols, M., Milkie, D., ... & Bock, D. D. (2018). A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell, 174(3), 730-743.
Files and variables
File: The zip file contains the codes.
main.py
Executes the core analyses, including spatial distribution analysis, behavioral analysis, and simulations. Each figure is annotated within the corresponding function.
PN_to_KC_coding_simulation.py
Performs simulations of artificial odors for Figure 4.Analyze_result.py
Analyzes simulation results, focusing on acuity and dimensionality.simulation_process.py
Generates artificial odors used in the simulations.behavioral_analysis.py
Analyzes the correlation between connection preferences and behavioral biases.generate_connection.py
Explores different PN-to-KC wiring configurations.MGPN_analysis.py
Analyzes multi-glomerular PN-to-KC connections.read_DoOR.py
Preprocesses data from the DoOR (Database of Odorant Responses).extract_bouton_claw.py
Extracts PN boutons and KC claws from anatomical data.function_data_processing.py
Analyzes functional imaging results.analysis_tool.py
Contains utility functions for analyzing spatial distribution data.shuffling_20241117.ipynb
Jupyter notebook for functional predictions and connection preference analysis.
Access information
Brain Research Center, National Tsing Hua University, Taiwan