Bias-free estimation of information content in temporally sparse neuronal activity
Fig 8
Bias-correction methods allow the estimation of spatial information content independently of the specific choice of spatial binning.
(A) Estimated SI (mean ± SEM) as a function of the spatial bin size for the naïve calculation (blue) and shuffle (black). (B) The naïve SI (mean ± SEM) as a function of the sample duration for different numbers of spatial bins. (C) Estimated SI (mean ± SEM) as a function of the number of spatial bins (log-scale) for the naïve calculation (blue), SSR (magenta), and BAE (green). While for the naïve calculation, the SI increases with the number of spatial bins, the SSR and BAE methods reach a stable estimation for a sufficient number of bins. (D) Estimated SI (mean) as a function of the bin size for the naïve calculation (blue), SSR (magenta), and BAE (green). The full-resolution SI (independent of bin size) was estimated using a linear extrapolation to bin size = 0 (indicated by the black arrow). Inset, the same analysis for different sample durations. Data were averaged across N = 9 mice. For each mouse, SI was averaged across the last four imaging sessions in each of the two environments when they were familiar.