Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.

ABSTRACT: One of the proposed canonical circuit motifs employed by the brain is a feedforward network where parallel signals converge, diverge, and reconverge. Here we investigate a network with this architecture in the Drosophila olfactory system. We focus on a glomerulus whose receptor neurons converge in an all-to-all manner onto six projection neurons that then reconverge onto higher-order neurons. We find that both convergence and reconvergence improve the ability of a decoder to detect a stimulus based on a single neuron's spike train. The first transformation implements averaging, and it improves peak detection accuracy but not speed; the second transformation implements coincidence detection, and it improves speed but not peak accuracy. In each case, the integration time and threshold of the

SUBMITTER: Jeanne JM 

PROVIDER: S-EPMC5488793 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature