Bias-free estimation of information content in temporally sparse neuronal activity
Fig 5
Validating the accuracy of the different bias-correction methods in estimating the true SI of individual simulated neurons.
(A) The naïve SI versus the true SI for simulated place cells with similar tuning properties to those observed in the hippocampal data. (B-C) The estimated SI of individual simulated neurons versus their true SI for the SR (B) and SSR (C) methods. (D) Distribution of the estimation errors of individual neurons for the naïve calculation (blue), SR (red), SSR (magenta), and the deviations between the naïve SI of each cell across different realizations (gray). (E-G) Same as in B-D but for AE (orange) and BAE (green). (H) Estimated SI of the same simulated neurons for the BAE method versus the SSR method. Inset, discrepancy between the estimated SI using SSR versus BAE. (I-J) Absolute estimation bias (mean ± SEM) as a function of the cells’ average firing rates (I) or number of active time bins (J) for SR (red), SSR (magenta), BAE (green) and AE (orange). Data were averaged across N = 9 simulations. (K-L) Differences in the absolute estimation bias between SR and SSR (K) and between AE and BAE (L) as a function of both the cells’ average firing rates and the sample duration. These results show that SSR and BAE yield a smaller bias than SR and AE, especially when the firing rates are low or the sample durations are short, indicating they are more robust. Data pooled from N = 9 simulations. Each simulation corresponds to behavioral data from a different mouse and consists of 100 simulated place cells.