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

Scientific Reports
Júlia V Gallinaro, Stefan Rotter

Abstract

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.

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Citations

Apr 8, 2020·Frontiers in Cellular Neuroscience·Christos Galanis, Andreas Vlachos
Aug 8, 2019·Scientific Reports·Fereshteh LagziStefan Rotter
Aug 28, 2020·Frontiers in Computational Neuroscience·Thomas Limbacher, Robert Legenstein
Jun 2, 2021·PLoS Computational Biology·Ankur SinhaVolker Steuber
Nov 12, 2021·PLoS Computational Biology·Júlia V Gallinaro, Claudia Clopath

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