Spatial synaptic regularization stabilizes learning across biological and artificial neural networks

Authors

Zhu, H., Chen, Y., Zhao, P., Xiong, Z., Peng, H., Wu, F., Zhang, R.

Abstract

How the spatial organization of synapses contributes to stable learning remains a fundamental question in neuroscience. Using the H01 electron microscopy connectome of human temporal cortex, we found that dendritic spines clustered morphologically, whereas synaptic weights followed a center-elevated, surround-suppressed arrangement along dendrites. A regularized Hebbian model formalized this spatial signature, showing that strong synapses lower the probability that neighboring synapses reach high-weight states. Translating this principle into Spatial Synaptic Regularization (SSR) reduced forgetting and stabilized learning across diverse artificial networks and tasks, including continual visual learning, large language-model knowledge editing, and parameter-efficient adaptation of vision-language models, by preserving high-rank, low-overlap representations. These findings identify spatial synaptic organization as an unrecognized dimension for stabilizing learning and show that structural connectomics can yield actionable AI methods.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 01 Jul 2026.

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