Fabian H. Sinz

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.

Dual-feature selectivity enables bidirectional coding in visual cortical neurons

The paper identifies a dual-feature encoding strategy in visual cortical neurons where neurons respond to both enhancing and suppressing visual features, showing this strategy is conserved across species.

NEURD offers automated proofreading and feature extraction for connectomics

NEURD automates proofreading and feature extraction from connectomics data to improve accuracy and accessibility of neural morphology analyses.

Functional connectomics spanning multiple areas of mouse visual cortex

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

Functional connectomics reveals general wiring rule in mouse visual cortex

Functionally similar neurons in mouse visual cortex connect preferentially across layers and areas, including feedback, and feature tuning, not receptive-field position, predicts synapses beyond axon-dendrite proximity.

Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks

The paper investigates how macaque V4 neurons respond to natural images using features from CNNs trained on various tasks, finding V4 is best predicted by semantic features and shows task selectivity.

Deep learning-driven characterization of single cell tuning in primate visual area V4 supports topological organization

The paper uses deep learning to analyze single-cell neural responses in primate visual area V4, finding functional clustering that mirrors artificial vision systems.

Foundation model of neural activity predicts response to new stimulus types and anatomy

This paper trains a foundation model on neural activity data to predict responses to new stimuli and anatomical features, showing strong generalization across mice and tasks.

Bipartite invariance in mouse primary visual cortex

The paper identifies a novel bipartite invariance in mouse V1 neurons, where receptive fields encode shift-invariant high-frequency textures and fixed low-frequency patterns, contributing to segmentation.

Pattern completion and disruption characterize contextual modulation in the visual cortex

The paper uses CNNs to analyze contextual modulation in visual cortex by synthesizing surround patterns, finding that pattern completion enhances responses while disruption suppresses them, consistent across mice and macaques.

Energy Guided Diffusion for Generating Neurally Exciting Images

This paper introduces Energy Guided Diffusion (EGG) to generate neurally exciting images, outperforming gradient ascent in generalization and efficiency for macaque V4 models.

It takes neurons to understand neurons: Digital twins of visual cortex synthesize neural metamers

The paper synthesizes neural metamers for the mouse visual system using deep encoding models and shows they reproduce neural activity across different animals.

Distinct organization of two cortico-cortical feedback pathways

The paper identifies distinct feedback circuit motifs in cortico-cortical pathways, showing different functional dynamics in motor-sensory and visual feedback circuits.

A flow-based latent state generative model of neural population responses to natural images

This paper introduces a flow-based generative model to capture neural population responses to natural images, outperforming existing models in predicting variability and learning latent factors related to behavior and brain area.

Behavioral state tunes mouse vision to ethological features through pupil dilation

The paper shows that pupil dilation in mice shifts color selectivity in the visual cortex towards ultraviolet stimuli, enhancing detection of ethological features like aerial predators.

Petascale neural circuit reconstruction: automated methods

Cloud-run convolutional nets aligned 19,939 serial EM sections of mouse visual cortex despite cracks and folds, segmented neurons and detected 523 million synapses; code and the reconstruction are public.

Generalization in data-driven models of primary visual cortex

This paper introduces a novel readout method for DNNs trained on mouse V1 data, achieving better generalization across animals and outperforming existing models.

Simultaneous spike-time locking to multiple frequencies

The paper investigates how neurons can lock their firing to multiple frequencies and shows that this phenomenon provides a richer sensory representation than previously assumed.

Cell type composition and circuit organization of neocortical radial clones

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.

A global map of orientation tuning in mouse visual cortex

The paper uses advanced imaging techniques to show that mouse visual cortex has a global orientation tuning map, challenging previous assumptions about its structure.

Inception in visual cortex: in vivo-silico loops reveal most exciting images

The paper develops inception loops to identify Most Exciting Images for V1 neurons in mice, finding that these images outperform linear model stimuli and reveal complex spatial features.

Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

A deep recurrent encoding model predicts spiking of thousands of mouse V1 neurons during natural video and carries over to noise stimuli when only its last layer is retrained.