Philipp Berens

A large-scale dataset of functional mouse ganglion cell layer responses

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

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs

This paper introduces a compound model for single-cell RNA-seq data without UMIs, using Pearson residuals to improve gene selection and embeddings in Smart-seq2 datasets.

Eyewire II - A connectomic resource for resolving cell types and circuits of the mouse retina

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 functional organization of retinal input to the mouse superior colliculus

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

Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

The paper introduces JO_SCPLOWAXLEYC_SCPLOW, a framework for differentiable simulation of biophysical neuron models, enabling efficient parameter optimization and task-driven training of large-scale models.

Combined statistical-mechanistic modeling links ionchannel genes to physiology of cortical neuron types

The paper develops a hybrid modeling approach to link ion channel gene expression to the electrophysiological properties of cortical neuron types using statistical and mechanistic models.

An unsupervised map of excitatory neurons' dendritic morphology in the mouse visual cortex

The paper uses graph-based machine learning to analyze dendritic morphology of excitatory neurons in mouse visual cortex, finding a continuous morphological landscape rather than discrete types.

A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations

The paper develops a biophysical model of the mouse outer retina to study contrast invariant coding, finding that nonlinearities in the retina enable robust classification despite contrast variations.

GABAergic amacrine cells balance biased chromatic information in the mouse retina

The paper identifies 25 functional GABAergic amacrine cell types in the mouse retina that balance chromatic information by reducing chromatic tuning and increasing receptive field diversity.

The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense

The paper challenges claims that t-SNE and UMAP embeddings of single-cell data are misleading, showing they can still provide biologically relevant information despite not preserving high-dimensional distances.

Aversion encoding and behavioral state modulation of lateral habenula neurons

The paper identifies electrophysiological diversity in lateral habenula neurons in mice and links firing patterns to aversive stimuli and behavioral states.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky

This study shows that mouse V1 has an asymmetric distribution of color-opponent neurons, particularly in areas encoding the sky, which may enhance detection of dark UV-objects in daylight.

A chromatic feature detector in the retina signals visual context changes

The paper identifies a retinal ganglion cell type that detects visual context changes by responding selectively to chromatic contrast transitions.

Visualizing single-cell data with the neighbor embedding spectrum

The paper explores a spectrum of visualization methods between t-SNE and UMAP for single-cell data, showing how varying a single parameter affects the representation of local vs. continuous structures.

Sustained ON alpha retinal ganglion cells in the temporal retina exhibit task-specific regional adaptions in dendritic signal integration

The paper investigates regional adaptations in dendritic signal processing of sustained ON alpha retinal ganglion cells in the mouse retina, finding differences in signal integration between nasal and temporal regions that may aid in prey detection.

The combination of stimulus-driven and modulatory inputs in visual thalamus depend on visual responsiveness and stimulus type

The paper shows that modulatory inputs in the dLGN affect neurons based on their visual responsiveness and stimulus type.

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models

The paper introduces probabilistic solvers to quantify numerical uncertainty in neuroscience models, showing they reveal spike-time jitter and spike count changes with minimal computational cost.

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling

The paper identifies two distinct synaptic sites on horizontal cells in the mouse retina that mediate local feedback to cones and global feedforward signaling to bipolar cells.

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

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.

A multimodal cell census and atlas of the mammalian primary motor cortex

The paper generates a multimodal cell census and atlas of the mammalian primary motor cortex, revealing a unified molecular genetic landscape and cross-species taxonomy of cell types.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

This paper compares normalization methods for single-cell RNA-seq data, showing that analytic Pearson residuals outperform other approaches in identifying variable genes and dimensionality reduction.

Estimating smooth and sparse neural receptive fields with a flexible spline basis

The paper introduces a spline-based method for estimating neural receptive fields, showing improved performance over non-spline methods in both simulated and experimental data.

Distinct Synaptic Transfer Functions in Same-Type Photoreceptors

The paper shows that UV-cones in larval zebrafish eyes have distinct synaptic transfer functions based on their location, using a combination of imaging and modeling techniques.

MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space

The paper introduces MorphVAE, a variational autoencoder for generating neural morphologies from 3D-walks, showing improved cell type discrimination and efficient morphology generation.

Center-surround interactions underlie bipolar cell motion sensing in the mouse retina

The paper investigates motion sensing in mouse retinal bipolar cells, finding that some possess motion-sensitive receptive fields and transmit direction-specific signals to downstream neurons.

Sparse reduced-rank regression for exploratory visualization of paired multivariate datasets

This paper introduces sparse reduced-rank regression for visualizing relationships between paired multivariate datasets, showing improved performance over existing methods in Patch-seq data.

Phenotypic variation within and across transcriptomic cell types in mouse motor cortex

The paper combines morpho-electric and transcriptomic data from mouse motor cortex neurons to show that transcriptomic types within families form continuous morpho-electric landscapes rather than discrete entities.

Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

This paper uses Bayesian inference to estimate parameters of retinal neuron models from imaging data and optimizes stimulus waveforms for retinal neuroprosthetics.

Ancestral circuits for vertebrate colour vision emerge at the first retinal synapse

The paper reveals how larval zebrafish retinal circuits process color by rotating spectral information into primary achromatic and chromatic axes at the first synapse, a strategy also used by fruit flies.

Object manifold geometry across the mouse cortical visual hierarchy

Recordings of thousands of mouse visual cortex neurons reveal that lateral areas LM, LI, and AL carry higher linearly decodable object information associated with reduced object manifold dimension and radius.