convolutional layers followed by two fully connected layers on the CIFAR10 dataset. We used a similar network as ref. [@Srivastava_etal14]. The convolutional layers used a $5\times 5$ kernel, a stride of $1$, and had $96$, $128$, and $256$ feature maps going from the bottom upwards. Max-pooling with a pooling window of $3 \times 3$ and stride $2$ was applied after each convolutional layer. The two fully connected layers on top had $2,048$ neurons each. All layers were batch-normalized and dropout was applied after the input layer, after each max-pooling layer, and after each fully connected layer.

The 32$\times$32$\times$3 CIFAR10 color images were pre-processed using global contrast normalization followed by ZCA whitening. The training set of 50,000 images was used for training/validation and we report errors on the 10,000 images test set. Unlike the MNIST dataset, standard backpropagation significantly outperforms training using local errors as shown in Fig. \[fig:cifar10\_results\] and Table \[tab:cifar10\]. Performance of local error learning deteriorates slightly when using sign-concordant local feedback weights instead of symmetric local feedback weights. Performance does not improve for the local classifier in the second fully connected layer compared to the classifier in the first fully connected layer. We trained a variant of the 