Biology subjects

Eaves, D. G.

Publications and source records attributed to Eaves, D. G..

2 recordsLinked to original sources

bioRxiv2026

Distinct processing of visual motion by transcriptomic cell types in the superior colliculus

How the brain organizes visual processing across distinct neuron types is a fundamental yet unresolved question. Here we study how visual motion is processed by transcriptomically defined cell types in the superior colliculus (SC). We generated or identified transgenic mouse lines to target three transcriptomic types of neurons in the superficial SC, the Cbln4+, Vip+, and Grp+ neurons. We find that each of these cell types encodes distinct ranges of stimulus direction, speed, size, and spatial and temporal features. In response to ecologically relevant visual flows, the three cell types occupy partitional spaces in the encoding manifold. The three cell types also show unique morphology and connectivity, including their retinal and intracollicular input and projection patterns. Together, these results reveal a genetic logic for visual motion processing in the SC, where transcriptomic cell types are likely the basic computational units with their specific feature selectivity, morphology, and input-output connectivity.

neuroscience↗

bioRxiv2026

Robust learning-driven structural and functional plasticity of spines in the mature mouse cortex

Spines in the adult cortex are thought to be highly stable, and that their capacity for modest remodeling supports learning. Using a visual association task and a multilevel imaging approach in adult mice, we found a robust learning-driven increase in the complexity of spine nanostructure, as well as a rapid and persistent increase in spine formation during task acquisition that were accompanied by an overall reduction in spine size of layer 2/3 neurons in the primary visual cortex (V1). Trained animals further had an increased fraction of spines tuned to the task-relevant orientations, and the discriminability of spine responses in naive mice was predictive of their subsequent performance. Our results demonstrate that learning drives an increase in spine preferences for task-relevant information and point to reconfiguration of spine nanostructure and spine inputs as the structural drivers of these changes.

neuroscience↗