Proximal Subclavian artery injury managed without performing median sternotomy: A case report.

UNLABELLED: Characterizing how diverse neuronal cell types transform synaptic inputs into somatic output is central to understanding how cellular diversity supports neural computation. The cerebellum provides a powerful system for addressing this question, because its major neuron types occupy distinct circuit positions and exhibit pronounced differences in dendritic morphology and active membrane properties. Yet whether these cellular differences correspond to distinct rules of synaptic integration remains unclear. Although detailed biophysical models reveal mechanistic determinants of dendritic processing and simplified functional models approximate neuronal input-output transformations, a compact and interpretable description that enables systematic comparison across cell types remains lacking. Here, we develop a reconstruction-based framework that quantifies synaptic integration in terms of the effective order of nonlinear interactions required to reproduce somatic responses. Applying this approach to a biophysically detailed Purkinje neuron model, we show that paired excitatory inputs give rise to higher-order nonlinear interactions that cannot be captured by linear or quadratic integration alone. Extending this analysis across multiple biophysical models representing major cerebellar neuron types, we uncover a hierarchical organization of synaptic integration complexity, in which distinct cell types are characterized by different effective integration orders. This hierarchy is robust to variability in synaptic location and is strongly associated with intrinsic cellular properties, including dendritic morphology and the diversity of active ion channels. In contrast, under the conditions examined here, input motifs involving inhibition are consistently well described by second-order interactions across cerebellar neuron types. Together, our results establish synaptic integration complexity as a key quantitative dimension that captures the hierarchical organization of effective integration rules across cerebellar neuron types.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s11571-026-10556-7.