DOI: 10.5061/dryad.jh9w0vtnc
title: Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila

description: 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: # Hybrid neural networks in the mushroom body drive olfactory preference
 in Drosophila ## 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](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