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.
@article{arxiv.1806.04793,
title = {A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System},
author = {Fabian David Tschopp and Michael B. Reiser and Srinivas C. Turaga},
journal= {arXiv preprint arXiv:1806.04793},
year = {2018}
}
Comments
Work in progress. Final paper with results from an updated model with new connectome data will be coming soon