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Neuron. 2015 Dec 2;88(5):1014-1026. doi: 10.1016/j.neuron.2015.10.018. Epub 2015 Nov 12.

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

Author information

1
Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA.
2
Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA. Electronic address: rachel_wilson@hms.harvard.edu.

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 postsynaptic cell are matched to the statistics of convergent spike trains.

PMID:
26586183
PMCID:
PMC5488793
DOI:
10.1016/j.neuron.2015.10.018
[Indexed for MEDLINE]
Free PMC Article

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