Receptive field estimation in large visual neuron assemblies using a super-resolution approach

Computing the spike-triggered average (STA) is a simple method to estimate the sensory neurons' linear receptive fields (RFs). For random, uncorrelated stimuli the STA provides an unbiased RF estimate, but in practice, white noise is not a feasible stimulus as it usually evokes only weak responses. Therefore, for a visual stimulus, it is often used images of randomly modulated blocks of pixels. This solution naturally limits the resolution at which an RF can be obtained. Here we show that this limitation can be overcome by using a simple super-resolution technique. We define a novel type of stimulus, the Shifted White Noise (SWN), by introducing random spatial shifts in the usual stimulus in order to increase the resolution of the measurements. In simulated data we show that the average error using the SWN was 1.7 times smaller than when using the classical stimulus, with successful mapping of 2.3 times more neurons, covering a broader range of RF sizes. Moreover, successful RF mapping was achieved with short recordings of about one minute of activity, more than 10 times more efficient than the classical white noise stimulus. In recordings from mouse retinal ganglion cells with large scale microelectrode arrays, we could map 18 times more RFs covering a broader range of sizes. In summary, here we show that randomly shifting the usual white noise stimulus significantly improves RFs estimation, and requires only short recordings. It is straight forward to extend this method into the time dimension and adapt it to other sensory modalities.