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Updated: Mar 19, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
Published on: May 18, 2020
Homeostatic Plasticity Achieved by Incorporation of Random Fluctuations and Soft-Bounded Hebbian Plasticity in
Takashi Matsubara1, Kuniaki Uehara1
1Computational Intelligence, Fundamentals of Computational Science, Department of Computational Science, Graduate School of System informatics, Kobe University Kobe-shi, Japan.
Abstract:
Homeostatic plasticity is considered to maintain activity in neuronal circuits within a functional range. In the absence of homeostatic plasticity neuronal activity is prone to be destabilized because Hebbian plasticity mechanisms induce positive feedback change. Several studies on homeostatic plasticity assumed the existence of a process for monitoring neuronal activity on a time scale of hours and adjusting synaptic efficacy by scaling up and down. However, the underlying mechanism still remains unclear. Excitatory synaptic efficacy is associated with the size of the dendritic spine, and dendritic spine size fluctuates even after neuronal activity is silenced. These fluctuations could be a non-Hebbian form of synaptic plasticity that serves such a homeostatic function. This study proposed and analyzed a synaptic plasticity model incorporating random fluctuations and soft-bounded Hebbian plasticity at excitatory synapses, and found that the proposed model can prevent excessive changes in neuronal activity by scaling synaptic efficacy up and down. Soft-bounded Hebbian plasticity suppresses strong synapses, thereby scaling synapses down and preventing runaway excitation. Random fluctuations diffuse synaptic efficacy, thereby scaling synapses up and preventing neurons from falling silent. The proposed model acts as a form of homeostatic plasticity, regardless of neuronal activity monitoring.
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