The neural code is robust to neuronal drop-out

Multiple sources of noise can affect the encoding of information in the nervous system. We assessed the robustness of the code to neuronal drop-out. The classifier was trained with all sites (here n=256) as described in the Methods. Here, before testing the performance of the classifier, a proportion of sites (indicated in the x-axis) were removed from the classifier (simulating the process of neuronal death or axonal death). Classifier parameters: MUA, n=256 sites, [100;300) ms time interval, bin size = 50 ms.