Dominic Gonschorek

The paper introduces a large-scale dataset of functional responses from mouse ganglion cells, enabling population-level analyses and computational modeling of retinal computation.

Eyewire II provides a large-scale EM dataset of the mouse retina, enabling cell type annotation and connectivity mapping through human-in-the-loop deep learning and functional alignment.

The paper shows that On-Off coding in vertebrate vision is a latent, dynamically regulated computation rather than a fixed circuit feature, revealed through zebrafish and mouse studies.

The paper maps retinal ganglion cell functional diversity in the mouse superior colliculus and identifies specialized response types for collision detection.

openretina provides a standardized framework for training and evaluating retinal models across datasets and species, revealing unexplained variance in current models.

The paper examines how photoreceptor degeneration in rd10 mice affects the resilience of different functional retinal ganglion cell (RGC) types, finding differential vulnerability among response types.

This study investigates how nitric oxide modulates contrast suppression in specific mouse retinal ganglion cell types using two-photon imaging and MEA recordings.

This study improves neural system identification by incorporating efficient coding principles, showing better performance and biologically plausible filters in retinal neuron response prediction.

This paper develops a domain adaptation framework to remove inter-experimental variability in systems neuroscience data while preserving biological signals, showing improved performance over existing methods.