General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain
Fig 5
Example of how eligibility traces allow the G-DHL rule to capture temporal interactions between events separated by a time gap.
Left: Two neural signals exhibiting an event each, and the related traces. The trace signals mi,t at time step t were numerically computed by applying a leaky accumulator process to the initial signals ui,t as follows: mi,t = mi,t−1 + (Δt/τ) ⋅ (−mi,t−1 + ui,t−1), with Δt = 0.001 and τ = 1. Right: the connection weight resulting from the application of the G-DHL rule component to the initial signals or to their memory traces.