Title: A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System,
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.