Signatures of Hebbian plasticity in the nanoscale morphodynamics of cortical spines
The size, shape, and nanoscale organization of synaptic spines are predictive of physiological synaptic strength and thus key to connectome-based neural circuit models. In the intact brain, spine size and shape undergo continuous morphological remodeling. While these nanoscale morphodynamics are not well understood, it is clear that morphological changes that strongly impact synaptic strength…
The size, shape, and nanoscale organization of synaptic spines play a crucial role in determining the physiological strength of synapses, which is vital for creating neural circuit models based on connectomics. Within a functioning brain, these spines undergo continuous morphological alterations. Although the mechanisms behind these nanoscale morphological changes are not thoroughly understood, it is evident that significant modifications to the spine head and neck promote coordinated remodeling.
This remodeling can be initiated by spike-timing-dependent synaptic plasticity, which influences synaptic strength.
Researchers have now observed unmistakable evidence of such coordinated spine remodeling in live long-term nanoscopy of cortical spines. By utilizing data-driven generative models of synaptic spine morphodynamics, they have derived insights into the processes at play. Although these coordinated remodeling events constitute the smallest part of ongoing morphodynamic fluctuations, they are responsible for considerable changes in instantaneous synaptic strength.
Even though these fluctuations occur, the synaptic strength still exhibits a stable long-term component that arises from spine movements exploring only a limited portion of accessible spine morphospace.
The findings emphasize that nanoscale morphodynamic modifications in spines contribute substantial variability in the instantaneous synaptic strength within cortical circuits. Consequently, connectome-based neural circuit models should incorporate features related to spine organization beyond just the head size. To enhance the accuracy of these models, researchers should incorporate data-driven generative models of spine morphodynamics.
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