Implementing feature binding through dendritic networks of a single neuron

A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma. Experimental and computational investigations have revealed various modes of integration operations that include linear, superlinear, and sublinear summation. Interestingly, different types of neurons exhibit diverse patterns of dendritic integration depending on the spatial distribution of dendrites. The functional implications of these specific integration modalities remain largely unexplored. In this study, we employ the Purkinje cell (PC) as a model system to investigate these complex questions. Our findings reveal that PCs generally exhibit sublinear summation across their expansive dendrites. Both spatial and temporal input dynamically modulates the degree of sublinearity. Strong sublinearity necessitates the synaptic distribution in PCs to be globally scattered sensitive, whereas weak sublinearity facilitates the generation of complex firing patterns in PCs. Using dendritic branches characterized by strong sublinearity as computational units, we demonstrate that a neuron can successfully address the feature binding problem. Taken together, these results offer a systematic perspective on the functional role of dendritic sublinearity, inspiring a broader understanding of dendritic integration in various neuronal types.