Extended Data Fig. 6: The bursting nonlinearity controls the learning rate.
From: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
a, Schematic of the network. Each hidden layer had 500 units. The recurrent weights (Z(1) and Z(2)) and the feedback alignment weights (Y(1) and Y(2)) are explicitly represented. b, Angle between the weight updates W(1) in the standard backpropagation algorithm and in burstprop for the MNIST digit recognition task. The angle is displayed for different values of the slope of the dendritic nonlinearity (β). Results are displayed as the mean +/- standard deviation over 10 realizations with randomly initialized weights.
