Visual Function Profiles via Multi-Path Aggregation Reveal Neuron-Level Responses in the Drosophila Brain

TL;DR

This paper introduces a Multi-Path Aggregation framework that combines connectome data with visual features to accurately predict neuron responses in Drosophila, advancing understanding of neural computation and behavior.

Contribution

The study presents a novel neural network-based method that integrates connectome topology with visual input to predict neuron responses, outperforming existing models.

Findings

Achieved a Pearson correlation of 0.84 for ON/OFF responses.

Successfully predicted neuron properties like luminance and direction preferences.

Enabled navigation and obstacle avoidance in simulations using neuron response data.

Abstract

Accurately predicting individual neurons' responses and spatial functional properties in complex visual tasks remains a key challenge in understanding neural computation. Existing whole-brain connectome models of Drosophila often rely on parameter assumptions or deep learning approaches, yet remain limited in their ability to reliably predict dynamic neuronal responses. We introduce a Multi-Path Aggregation (MPA) framework, based on neural network steady-state theory, to build a whole-brain Visual Function Profiles (VFP) of Drosophila neurons and predict their responses under diverse visual tasks. Unlike conventional methods relying on redundant parameters, MPA combines visual input features with the whole-brain connectome topology. It uses adjacency matrix powers and finite-path optimization to efficiently predict neuronal function, including ON/OFF polarity, direction selectivity, and responses to complex visual stimuli. Our model achieves a Pearson correlation of 0.84+/-0.12 for ON/OFF responses, outperforming existing methods (0.33+/-0.59), and accurately captures neuron functional properties, including luminance and direction preferences, while allowing single-neuron or population-level blockade simulations. Replacing CNN modules with VFP-derived Lobula Columnar(LC) population responses in a Drosophila simulation enables successful navigation and obstacle avoidance, demonstrating the model's effectiveness in guiding embodied behavior. This study establishes a "connectome-functional profile-behavior" framework, offering a whole-brain quantitative tool to study Drosophila visual computation and a neuron-level guide for brain-inspired intelligence.