The Impact of Structural Changes on Learning Capacity in the Fly Olfactory Neural Circuit

2025-09-17

AI Summary

This study investigates how structural plasticity of the mushroom body (MB) regulates odor categorization during olfactory learning in *Drosophila*. Focusing on KC→MBON synaptic connectivity, we combine genetic perturbations—including ablation of mature Kenyon cells (KCs), synaptic pruning, and circuit rewiring—with biologically inspired neural network modeling to systematically assess how structural alterations impact MB output neuron (MBON) classification performance. We find that: (1) KC developmental maturity determines functional specificity—targeted ablation of mature KCs significantly impairs MBON odor discrimination; (2) the number of KC inputs received by an MBON is negatively correlated with its classification error rate; and (3) structural specificity—not merely connection density—of KC–MBON synapses is essential for efficient neural computation. This work establishes, for the first time at the circuit level, a causal role for developmentally regulated structural plasticity in perceptual learning, offering a novel paradigm for probing structure–function relationships in neural circuits.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Structured Learning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The Drosophila mushroom body (MB) is known to be involved in olfactory learning and memory; the synaptic plasticity of the Kenyon cell (KC) to mushroom body output neuron (MBON) synapses plays a key role in the learning process. Previous research has focused on projection neuron (PN) to Kenyon cell (KC) connectivity within the MB; we examine how perturbations to the mushroom body circuit structure and changes in connectivity, specifically within the KC to mushroom body output neuron (MBON) neural circuit, affect the MBONs' ability to distinguish between odor classes. We constructed a neural network that incorporates the connectivity between PNs, KCs, and MBONs. To train our model, we generated ten artificial input classes, which represent the projection neuron activity in response to different odors. We collected data on the number of KC-to-MBON connections, MBON error rates, and KC-to-MBON synaptic weights, among other metrics. We observed that MBONs with very few presynaptic KCs consistently performed worse than others in the odor classification task. The developmental types of KCs also played a significant role in each MBON's output. We performed random and targeted KC ablation and observed that ablating developmentally mature KCs had a greater negative impact on MBONs' learning capacity than ablating immature KCs. Random and targeted pruning of KC-MBON synaptic connections yielded results largely consistent with the ablation experiments. To further explore the various types of KCs, we also performed rewiring experiments in the PN to KC circuit. Our study furthers our understanding of olfactory neuroplasticity and provides important clues to understanding learning and memory in general. Understanding how the olfactory circuits process and learn can also have potential applications in artificial intelligence and treatments for neurodegenerative diseases.

Problem

• Examining how structural changes in KC-MBON neural circuits affect odor classification
• Investigating how KC developmental maturity impacts olfactory learning capacity
• Exploring how synaptic pruning and rewiring affect neural circuit learning performance

Innovation

• Constructed neural network modeling PN-KC-MBON connectivity
• Performed KC ablation and synaptic pruning experiments
• Explored KC developmental types through rewiring experiments