<p><b>A.</b> The generalization error <i>vs.</i> the location of the test targets, estimated from simulations as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003377#pcbi-1003377-g006" target="_blank">Figure 6</a>. Shaded area represents one around the averages. Tuning width: . <b>B.</b> The noiseless performance (see Eq(25)), averaged over all the tested targets () is plotted <i>vs.</i> the number of trained targets. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003377#s4" target="_blank">Materials and Methods</a> for details about how this quantity was estimated. Blue: . Green: . Black: . Dashed gray: zero Parameters: .</p
<p>After training, the model was tested on data that differ from its training distribution. (A) Disc...
<p>The left chart is for binary and the right for multi-class classification. The X axis in both ref...
A) Sample normalized training and validation costs during towards the end of an optimization. The tr...
<p><b>A.</b> Average total number of target presentations required to learn the entire task <i>vs.</...
<p>Two snapshots of the activation patterns in the model during the memory period are shown, taken f...
Each point indicates a model performance estimate in the training set (X axis) and its gap from the ...
Model performance estimate and generalization gap according to the sample size and the level of task...
<p><b>A</b>) Baseline (black) and generalization (red) of the rotation across multiple directions (±...
<p><b>A.</b> An example of the variations of the error (blue) and the noiseless error (red) with the...
<p><b>A</b>) Baseline and generalization of the mean (±SEM) of a perturbation for a typical subject ...
<p>The bars provide group-averaged Pearson correlation coefficients, while individual participants a...
Model performance estimate and generalization gap according to the level of task difficulty in some ...
<p>The generalization error decreases as the number of trees in the ensemble prediction increases.</...
<p>Unexposed directions data represent the average across the 3 directions that were not exposed dur...
<p>Each row shows examples of probability densities (black lines) for a different sample (green and ...
<p>After training, the model was tested on data that differ from its training distribution. (A) Disc...
<p>The left chart is for binary and the right for multi-class classification. The X axis in both ref...
A) Sample normalized training and validation costs during towards the end of an optimization. The tr...
<p><b>A.</b> Average total number of target presentations required to learn the entire task <i>vs.</...
<p>Two snapshots of the activation patterns in the model during the memory period are shown, taken f...
Each point indicates a model performance estimate in the training set (X axis) and its gap from the ...
Model performance estimate and generalization gap according to the sample size and the level of task...
<p><b>A</b>) Baseline (black) and generalization (red) of the rotation across multiple directions (±...
<p><b>A.</b> An example of the variations of the error (blue) and the noiseless error (red) with the...
<p><b>A</b>) Baseline and generalization of the mean (±SEM) of a perturbation for a typical subject ...
<p>The bars provide group-averaged Pearson correlation coefficients, while individual participants a...
Model performance estimate and generalization gap according to the level of task difficulty in some ...
<p>The generalization error decreases as the number of trees in the ensemble prediction increases.</...
<p>Unexposed directions data represent the average across the 3 directions that were not exposed dur...
<p>Each row shows examples of probability densities (black lines) for a different sample (green and ...
<p>After training, the model was tested on data that differ from its training distribution. (A) Disc...
<p>The left chart is for binary and the right for multi-class classification. The X axis in both ref...
A) Sample normalized training and validation costs during towards the end of an optimization. The tr...