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Receptive Field Inference with Localized Priors

Figure 8

Comparison of 3D receptive field estimates for retinal data.

(Data from Chichilnisky lab, [55]). Top row: Maximum likelihood and ALDsf estimates for an OFF retinal ganglion cell (RGC) receptive field, stimulated using 1 minute of binary spatiotemporal white noise. Left column shows a schematic of the pixel 25 time bin receptive field, containing 2500 total coefficients. Each time bin was 8.33 ms, corresponding to a frame rate of 120 Hz. Colored lines indicate specific pixels whose timecourses shown at right, and spatial time-slices, depicted as images at right (taken at the 4th and 8th time bins, indicated by green and purple arrows, respectively). The ML and ALDsf estimates with 1 minute of training data are shown alongside the ML estimate computed from 20 minutes of data. Pixel time-courses were rescaled to be unit vectors, so that differences in temporal profiles (i.e., spacetime non-separability of filter) can be observed. Bottom row: Similar plots for an ON RGC, with spatial profiles shown for the 5th and 8th time bins. In both cases, the ALDsf accurately recovered the shape and timecourse of the RF, while the ML estimate was often indistinguishable from noise. We examined RF estimates from 3 ON and 3 OFF cells, and found that, with 1 minute of training data, the average mean-squared-error between each estimate and a reference estimate (the ML estimate computed with 20 minutes of data) was 18 times larger for ML and 6.6 times larger for ridge regression than for ALDsf.

doi: https://doi.org/10.1371/journal.pcbi.1002219.g008