Estimating neuronal firing density: A quantitative analysis of firing rate map algorithms
Fig 4
Adaptive smoothing, convolution method.
See Methods: Adaptive smoothing for more detail. Schematic showing the steps involved with creating a firing rate map using the adaptive smoothing approach accelerated through convolution. Position and spike data are binned separately into unsmoothed dwell and spike maps respectively. For this example, 50 mm bins are used. Convolution with circular unity-gain kernels of varying sizes is used to sum the total spikes and position samples at a set of discretized radii. For each bin, the smallest radius of kernel at which the adaptive equation can be satisfied is then found. The value of the firing rate map in that bin is equal to the number of spikes divided by the number of position samples within the kernel multiplied by the sampling interval of the position data. The result is a map which is functionally identical to one generated using the pixelwise approach (Fig 3) but in a fraction of the time (S2 Fig).