AUTHOR=Humble James , Hiratsuka Kazuhiro , Kasai Haruo , Toyoizumi Taro TITLE=Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder JOURNAL=Frontiers in Computational Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00038 DOI=10.3389/fncom.2019.00038 ISSN=1662-5188 ABSTRACT=

It is often assumed that Hebbian synaptic plasticity forms a cell assembly, a mutually interacting group of neurons that encodes memory. However, in recurrently connected networks with pure Hebbian plasticity, cell assemblies typically diverge or fade under ongoing changes of synaptic strength. Previously assumed mechanisms that stabilize cell assemblies do not robustly reproduce the experimentally reported unimodal and long-tailed distribution of synaptic strengths. Here, we show that augmenting Hebbian plasticity with experimentally observed intrinsic spine dynamics can stabilize cell assemblies and reproduce the distribution of synaptic strengths. Moreover, we posit that strong intrinsic spine dynamics impair learning performance. Our theory explains how excessively strong spine dynamics, experimentally observed in several animal models of autism spectrum disorder, impair learning associations in the brain.