Thomas Euler

This paper introduces a flexible spatial visual stimulator with arbitrary spectrum control for use in vision research, demonstrating its application in mouse and zebrafish models.

The paper develops a Bayesian statistical pipeline for analyzing two-photon imaging data using Gaussian Process Regression to infer neural activity and guide experimental design.

The paper investigates how visual information from retinal ganglion cells (RGCs) is represented in the mouse dorsolateral geniculate nucleus (dLGN), finding that dLGN neurons receive input from diverse RGC types with limited functional convergence.

The paper benchmarks spike rate inference algorithms for two-photon calcium imaging data and finds that top-performing algorithms show high correlation despite diverse approaches.

This study shows mouse horizontal cells process cone input locally, with dendritic Ca2+ signals reflecting cone opsin activation and chromatic preference, supporting local feedback shaping cone output.

The paper shows that balanced excitatory and inhibitory inputs in bipolar cells decorrelate visual feature representation in the mammalian inner retina.

The paper examines connectivity patterns between bipolar cells and photoreceptors in the mouse retina, revealing deviations from classical synaptic organization.

The paper benchmarks spike rate inference algorithms using calcium imaging data and introduces a supervised learning method that outperforms existing techniques across diverse datasets.