A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System

TL;DR

This study constructs a connectome-based convolutional network model of the Drosophila visual system, demonstrating that connectome knowledge can predict neuronal functional properties and aid in understanding circuit functions.

Contribution

The paper introduces a connectome-informed neural network model that predicts neuronal functions and reveals properties like orientation and direction selectivity from structure alone.

Findings

Connectome-based networks discover known neuronal selectivities.

Randomly initialized networks do not show these properties.

Connectome knowledge enables in silico functional predictions.

Abstract

What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.