Philipp Berens

The paper investigates neural circuits in the mouse retina and finds that color-opponent cells are predominantly located in the ventral retina, supporting color vision in the upper visual field.

The paper develops a biophysically constrained computational model of inner retinal circuits that performs well on new stimuli and matches connectomics data.

The paper evaluates different neuronal morphology representations for cell type discrimination using curated datasets of mouse neurons, finding 2D density maps and morphometric statistics to be the most effective.

This study shows how four types of mouse retinal ganglion cells use different dendritic integration rules, influenced by both morphology and ion channel distribution.

The paper identifies linear filters of retinal ganglion cells using a smooth electrical stimulus and an LNP model, showing diverse preferred stimuli in healthy and blind mouse retinas.

The paper develops a method to image the entire inner plexiform layer of the retina at once, revealing response speed gradients in bipolar cells and isolating experimental batch effects.

The paper characterizes cell types and circuit organization within neocortical radial clones using single-cell RNA-sequencing and electrophysiological recordings, finding that clones contain diverse cell types and exhibit specific connectivity patterns.

The paper describes a protocol to improve t-SNE visualizations in single-cell transcriptomics by using PCA initialization, high learning rate, and multi-scale kernels.

The paper maps spatio-temporal patterns of calpain activity in retinal degeneration models, linking Ca2+ dysregulation to primary rod death and apoptosis to secondary cone loss.

The paper develops an approximate Bayesian inference method for a mechanistic model of glutamate release at a ribbon synapse, showing it can estimate parameters from synthetic and experimental data and outperforms a standard GLM.

The paper shows UV-cones in larval zebrafish are tuned for UV-bright prey capture through regional boosts in detection efficiency and synaptic calcium baseline.

The paper identifies distinct cell types and circuit organization in layer 4 of primary sensory areas in adult mouse neocortex, challenging the canonical microcircuit model.

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.

This paper develops a CNN that detects saccades with human-level accuracy, outperforming existing methods and aiding neurophysiological research.

The paper shows that moving bars are processed faster than flashed bars in macaque V1, supporting the differential latency model of the flash lag illusion.

This study shows larval zebrafish use anisotropic retinal circuits to process color asymmetrically across visual space, matching natural scene statistics.

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 paper evaluates Bayesian uncertainty measures in deep learning for diabetic retinopathy detection, showing improved diagnostic performance through uncertainty-informed decision referral.

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.

DataJoint is an open-source toolbox for managing large scientific datasets using MATLAB or Python, enabling flexible data manipulation and processing.