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to translate Hopfield models into spiking networks where the intrinsic neuronal time scale would be clearly defined (Gerstner, 1991; Gerstner and van Hemmen, 1992). After my return to Munich, I wondered whether my spiking networks would be able to learn spatio-temporal spike patterns. In analogy to earlier work on sequence learning (Herz et al., 1991), I realized that this would only be possible if I used a Hebbian learn-ing rule which reflects the causality principle implicit in Hebb’s formulation: the timing must be such that synapses that contrib-ute to firing the postsynaptic neuron are maximally strengthened. Hence long-term potentiation (LTP) must be maximal if the spike arrives at the synapse 1 or 2 ms

From Hebb Rules to Spike-Timing-Dependent Plasticity: A Personal Account