Figure 2.
Sketch of the stimulus estimation process, consisting of training and test phases. In this example, three stimuli of different intensities (A) were applied four times: for each of the stimuli, three response traces were included in the corresponding stimulus response class S1, S2, or S3 of the training dataset (B, top row, vertical lines indicate spike times), the remaining trace was used as test data (C, top row). B, Training: the analyzed response feature, here spike count, was determined for each trace in the training dataset. These numbers were sorted, and divided into three commensurate quantile classes (Q1, Q2, Q3) according to their ranks. We determined how often each possible spike count was contained in each quantile class Qi. For each spike count value, the index i of the most probable quantile class Qi determined to which rank class Ri all responses showing this spike count were assigned. The rank class look table gives the definitions of (R1, R2, R3) and line 5 shows the rank classes for each of the responses shown in line 1. The rank class matrix (B, bottom left) shows how many of the traces contained in each stimulus response class (S1, S2, S3) were assigned to each of the three rank classes (R1, R2, R3). The rank class matrix is used to create a stimulus estimation table (B, bottom right), giving for each rank class the stimulus, which has most probably elicited the response (maximum likelihood). C, Test: each trace in the test dataset is assigned to one of the rank classes based on the rank-class look table constructed in the training. Applying the stimulus estimation table, this rank class determines the estimated stimulus for each test response trace. The comparison of estimated and presented stimuli for all response traces leads to the percentage of correct estimations.