Frontiers in Computational Neuroscience Computational Neuroscience Structural Plasticity Controlled by Calcium Based Correlation Detection

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Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, background): “Third, anatomical experiments have shown that the number of real synapses per connected neuron pair is relatively constant in cortical areas [59] which indicates active regulation, for example, based on spike correlations [132,133].”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “, 2001) are further examples of the general theoretical framework of Equation (1) and so are some models of structural plasticity (Helias et al., 2008; Deger et al., 2012, 2018; Fauth et al., 2015).”
Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex (2012, methodology): “[27] is sensitive to the spike-timing of the pre- and the postsynaptic cell and describes structural plasticity in biophysical terms of protein kinetics in response to synaptic input.”
A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent “Heterosynaptic” LTD in the Dentate Gyrus In Vivo (2015, unknown): “Thus, we speculate that the level of Ca-independent CaMKII might be related to the fast dynamics of Ap and Ad (see also [33, 46]).”