High Accuracy Decoding of Dynamical Motion from a Large Retinal Population
Fig 2
High-accuracy reconstruction of the bar’s trajectory.
A: Schematic of the linear decoding method, here for 4 cells. A temporal filter is associated with each cell. Each time the cell spikes, its filter is added to the ongoing reconstruction at the time of the spike. The filters are optimized on part of the data to have the lowest reconstruction error and then tested on the rest of the data. B: Top: prediction of the bar’s position (black) from the activity of 123 cells in the salamander retina versus the real trajectory (red). Bottom: prediction of the bar’s position (black) from the activity of 178 cells in the guinea pig retina versus the real trajectory (red). C, D: Decoding performance plotted against the number of cells in the salamander (C) and guinea pig (D). Gray points correspond to random subsets of cells, black to the average performance. E: Histogram of the average decoding performance across all experiments using either causal (black) or acausal (white) decoding filters; results shown for the entire recorded neural population in both the salamander (left) and guinea pig (right).