Retinal ganglion cells and their post-synaptic "relay" neurons in the dLGN are well known to have center-surround opponent receptive fields, such that their responses selectively encode spatial differences in luminance. Nevertheless, spatially uniform temporally flickering white noise is a powerful visual stimulus for these neurons, evoking spikes at specific times in the white noise sequence with high reliability and precision. Consequently, if the same white noise stimulus is presented repeatedly and the responses are aligned to the stimulus and displayed as a raster plot, the neural response literally resembles a barcode. Different neurons fire different barcodes in response to the same stimulus, reflecting their distinct spatiotemporal integration filters and intrinsic nonlinearities. There are a few examples in the literature, however, of the identical barcode showing up in different neurons even in different animals or different species. This inspired the idea that neurons might be classified according to their barcode patterns in response to some standard white noise stimulus.

However, it is not known whether neurons in primary or higher visual cortical areas or in the superior colliculus will also be driven by spatially uniform white noise, or whether their responses will be barcode-like. Even in the dLGN, where barcodes have been observed before, it remains unknown whether dGN responses will be barcode-like in mice or in awake animals. Finally we have no idea how many such barcode classes there might be, how discrete or continuous the categories, or what fraction of neurons belong to an identifiable barcode class.

To answer all these questions, this Allen Instute OpenScope project is presenting many repeats of a white noise stimulus to awake mice while recording throughout the brain with Neuropixels electrodes, targeting as many visually responsive cortical and subcortical brain areas as possible. In addition to spatially uniform white noise, we are also presenting sinusoidal gratings whose contrast is modulated by the identical temporal white noise sequence.

The value of this public data set will extend beyond this analysis. White noise responses of neurons are valuable for many other kinds of anlaysis, including measuring temporal precision, trial-by-trial reliability, entropy, and mutual information of visual responses. Yet white noise visual stimuli are missing from the current open data ecosystem for the mouse visual system.