Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies
Fig 6
Computation of functional subunits and performance comparison with a subunit model.
A: Filters representing the relevant stimulus subspace computed via spike-triggered covariance (STC) for a sample ON parasol cell. Shown here are the top 8 of the 16 used for modeling. Scale bar: 100 μm. B: Inferred filters of the functional subunits for the sample cell. The optimal number of filters (here 8) was chosen during model training. The relative weight of each filter in the subunit model is displayed at the top-left of each panel as a percentage of the total sum across all filters. Scale bar: 100 μm. C: Eigenvalue spectrum from the STC analysis of the sample cell. Blue circles correspond to filters from A. D: Distribution of the obtained number of subunits for the four cell classes. Colors as in F. E: Firing rate of the sample cell (dark gray) overlaid with predictions from the SC model (blue) and the subunit model (pink) for a 2 s segment of the spatiotemporal white-noise stimulus. Pearson’s correlation coefficient between model prediction and cell response displayed next to model name in legend. F: Comparison of correlation coefficients for the SC model (y-axis, rSC) and the subunit model (x-axis, rsub) for each cell of the four cell classes under spatiotemporal white noise. The average correlation coefficients and
are shown next to the corresponding axes. G: Relative improvement in model performance (
) under white noise for each cell versus the number of spikes in the training segment of the stimulus (left) and the size of the cells’ spatial receptive fields (right). Colors as in F. In panels F and G, the sample ON parasol cell from A–E is highlighted with a black circle.