Large-scale analysis of mouse and human dendritic spines reveals no evidence of local spatial clustering and weak morphological similarity between neighbouring synaptic contacts

Dendritic spines receive most excitatory synaptic inputs in the cortex, and their morphology and arrangement influence how neurons integrate signals. Whether spines form spatial clusters and whether neighbouring synaptic contacts resemble one another remain important questions for understanding synaptic organization and constructing computational representations of neurons. We investigated these questions using large-scale reconstructions of mouse (Minnie65) and human (H01) , with morphological analyses covering more than 600,000 spines. Analyses of spine placement in the mouse dataset did not support local spatial clustering. Instead, spines were less closely spaced within dendritic branches than expected under independent placement; models incorporating short-range suppression improved, but did not fully reproduce, the observed spacing distributions. Across both datasets, neighbouring spines showed only weak excess morphological similarity after accounting for differences between branches. In the mouse dataset, repeated contacts from the same presynaptic axon showed correlated morphology, and their measurements improved spine-volume prediction beyond geometry and partner distances, although limited partner availability restricted the population-wide benefit. We compared our findings with previous studies and examined possible reasons for discrepant conclusions, including differences in tissue, morphological measures and statistical assumptions. The weak morphological resemblance between neighbouring spines challenges the use of spatial proximity as a basis for aggregating morphological information in spine-level graph models. Consistent with this finding, a graph-based LeJEPA model showed limited performance, illustrating the difficulty of exploiting spatial neighbourhoods to learn representations of spine morphology.