Plot of test error across 20 epochs of training on MNIST of a one hidden layer network, with regular feedback weights (gray), sparse feedback weights that were amplified (red), and sparse feedback weights that were not amplified (blue). The network with amplified sparse feedback weights is the same as in Figure 8A and B, where feedback weights were multiplied by a factor of 5. While sparse feedback weights that were amplified led to improved training performance, sparse weights without amplification impaired the network’s learning ability. Right: Spreads (min – max) of the results of repeated weight tests () after 20 epochs for each of the networks. Percentages indicate means (two-tailed t-test, regular vs. sparse, amplified: , ; regular vs. sparse, not amplified: , ; sparse, amplified vs. sparse, not amplified: , , Bonferroni correction for multiple comparisons).