A connectome based hexagonal lattice convolutional network model of the Drosophila visual system.

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arXiv. 2018 Jun 12;:1806.04793

A connectome based hexagonal lattice convolutional network model of the Drosophila visual system. Reiser LabTuraga Lab

Tschopp FD, Reiser MB, Turaga SC

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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.

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A connectome based hexagonal lattice convolutional network model of the Drosophila visual system.

Abstract

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