2017/06/30 by Júlia V Gallinaro, Júlia V. Gallinaro, Stefan Rotter · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · #Artificial intelligence #Artificial neural network #Associative property #Biology #Computer science #Hebbian theory #Homeostasis #Homeostatic plasticity #Mathematics #Metaplasticity #Neural dynamics and brain function #Neuroplasticity #Neuroscience #Neuroscience and Neuropharmacology Research #Photoreceptor and optogenetics research #Physics #Plasticity #Premovement neuronal activity #Structural plasticity #Synaptic plasticity #Synaptic scaling #Visual cortex #q-bio.NC
paper · pdf · doi:10.1038/s41598-018-22077-3
19 pages, 4 figures
openalex publication_date 2018/02/22 · arxiv created 2018/02/24 · arxiv updated 2018/03/02 · openalex created_date 2020/11/23 · openalex updated_date 2026/08/05
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.