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

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 postsynaptic cell are matched to the statistics of convergent spike trains. •Feedforward signals in this network converge, diverge, and reconverge•Individual spike trains become progressively more informative at each layer•Whereas second-order neurons average out noise, third-order neurons detect coincidences•At each layer, postsynaptic neurons are well tuned to their presynaptic inputs Convergence and reconvergence are thought to be canonical circuit motifs. Jeanne and Wilson identify a three-layer feedforward network in Drosophila olfaction that computes progressively more informative single-neuron sensory representations by matching postsynaptic properties to the statistics of presynaptic inputs.