General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain
Fig 2
Superposition of learning kernels of the G-DHL rule components.
The learning kernels considered correspond to different inter-event intervals, with events represented by a cosine function as in Fig 1. The kernels are indicated with pairs of letters referring respectively to the pre- and post-synaptic neuron, where ‘S’ refers to [ui], ‘P’ to , and ‘N’ to
. PS/SN kernels overlap, and so do SP/NS kernels.