General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain
Fig 3
Examples of learning kernels generated by the G-DHL rule.
The signals involved events generated with a cosine function (as in Fig 1). In the examples, the G-DHL coefficients were set as follows (the rule names are arbitrary): Causal rule: σp,p = σp,n = σn,p = σn,n = ηp,s = ηn,s = 0, ηs,p = 1, ηs,n = −1. Anticausal rule: σp,p = σp,n = σn,p = σn,n = ηs,p = ηp,s = 0, ηs,n = 1, ηn,s = −1. Coincidence rule: ηs,p = ηs,n = ηp,s = ηn,s = 0, σp,p = σn,n = 1, σp,n = σn,p = −1. Flat-at-zero rule: σp,p = σn,n = ηs,p = ηs,n = ηp,s = ηn,s = 0, σp,n = −1, σn,p = 1.