Dimokratis Karamanlis

LightSuite enables scalable, multimodal registration and cell detection across the central nervous system, improving precision and reproducibility in large animal studies.

The paper develops a spatial contrast model to capture nonlinear spatial integration in primate retinal ganglion cells using natural and artificial stimuli, showing improved performance over linear models.

This study examines how primate retinal ganglion cells adapt their spatial processing to natural versus artificial stimuli, finding that some cell types adjust receptive fields and surrounds, but efficient coding only partially explains these changes.

This study shows that natural stimuli with species-specific gaze patterns drive correlated spiking responses in retinal ganglion cells, violating efficient coding theory.

This paper introduces STR, a method combining super-resolution and tomography to identify receptive field subunits in retinal ganglion cells, validated in primate recordings.

The paper introduces an accelerated version of spike-triggered non-negative matrix factorization to identify coordinated subunit mosaics in primate ganglion cells.

This study compares predictive models for retinal ganglion cell responses, finding that CNNs outperform LN models, while LN models show better cross-stimulus generalization.

The paper investigates how primate retinal ganglion cells respond to saccade-like image shifts, revealing distinct response patterns and functional diversity among cell types.

The paper introduces a simple model that improves predictions of retinal ganglion cell responses to natural images by incorporating spatial variance in addition to average light intensity.

The paper shows that nonlinear spatial processing is important for retinal ganglion cell responses to natural stimuli, beyond linear receptive field models.