Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways

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The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “Such a stabilizing weight dependence is advantageous because it can lead to more plausible unimodal weight distributions as observed empirically [60,62].”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence (2010, methodology): “In the same way as in our previous analysis using additive STDP (Gilson et al. 2009a,d), it is necessary to express these spike-time correlation structures in terms of the input-to-input correlation structure Ĉ in order to evaluate the weight evolution.”
Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity (2012, background): “Theoretical studies have also explored the development of neuronal response properties [28,29,36,39,42].”