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Correlation-based Hebbian plasticity is thought to shape neuronalconnectivity during development and learning, whereas homeostatic plasticitywould stabilize network activity. Here we investigate another, new aspect ofthis dichotomy: Can Hebbian associative properties also emerge as a networkeffect from a plasticity rule based on homeostatic principles on the neuronallevel? To address this question, we simulated a recurrent network of leakyintegrate-and-fire neurons, in which excitatory connections are subject to astructural plasticity rule based on firing rate homeostasis. We show that asubgroup of neurons develop stronger within-group connectivity as a consequenceof receiving stronger external stimulation. In an experimentallywell-documented scenario we show that feature specific connectivity, similar towhat has been observed in rodent visual cortex, can emerge from such aplasticity rule. The experience-dependent structural changes triggered bystimulation are long-lasting and decay only slowly when the neurons are exposedagain to unspecific external inputs.

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks