Behavior of the simulated spiking network for the natural image patch dataset.

a–d: As Fig 6, with addition of c, which shows the Pearson correlation coefficient between the feedback weights Q and the feedforward weights W as they become symmetric over time. Because of the sparsity constraint on learning, the network cannot learn a perfect representation, so final autoencoder loss is still quite large (b), but the network nevertheless captures many salient features when attempting reconstruction (d). e: Reconstruction attempts for the same input stimuli as in (d), when the scaling factors Φj were uniformly multiplied by 1.5 after training.