General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain

doi:10.1371/journal.pcbi.1006227

General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain

Fig 6

Learning kernels of the four G-DHL differential components for a pair of pre-/post-synaptic spikes.

The three columns of graphs refer respectively to: ; τ₁ = τ₂; . The four rows of graphs refer to the G-DHL different components: ‘p’ indicates the positive part of the eligibility-trace derivative and ‘n’ indicates its negative part. Small gray circles indicate maximum synaptic changes.

doi: https://doi.org/10.1371/journal.pcbi.1006227.g006