October 29, 2020

A parameter-free statistical test that improves the detection of neuronal responsiveness

Neurophysiological studies depend on a reliable quantification of whether and when a neuron responds to stimulation. Current methods to determine responsiveness require arbitrary parameter choices, such as binning size. These choices can change the results, which invites bad statistical practice and reduces the replicability. Moreover, many methods only detect mean-rate modulated cells. New recording techniques that yield increasingly large numbers of cells would benefit from a test for cell-inclusion that requires no manual curation. Here, we present the parameter-free ZETA-test, which outperforms t-tests and ANOVAs by including more cells at a similar false-positive rate. We show that our procedure works across brain regions and recording techniques, including calcium imaging and Neuropixels data. Furthermore, in illustration of the method, we show in mouse visual cortex that 1) visuomotor-mismatch and spatial location are encoded by different neuronal subpopulations; and 2) optogenetic stimulation of VIP cells leads to early inhibition and subsequent disinhibition.

 bioRxiv Subject Collection: Neuroscience

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