Alexander S. Ecker

LVentiView automates 3D left ventricular mesh reconstruction and analysis from cardiac MRI, achieving accuracy comparable to expert manual segmentation.

PriMAT tracks and identifies primates in the wild using bounding boxes, achieving 84% accuracy in lemur identification with minimal labeled data.

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 uses convolutional neural networks to investigate wide-field inhibition in the marmoset retina, revealing hidden computational aspects of retinal processing through interpretable models.

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.

The study examines how foraging success and scrounging tolerance affect social relationships in redfronted lemurs, finding that more successful individuals and those scrounged more often receive more affiliative behavior.

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.

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

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

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.

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

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.

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.

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.

The paper uses digital twins to characterize the functional organization of mouse V1, revealing a combinatorial code of nonlinear response properties across 30 modes.

This study investigates how retinal ganglion cells respond to natural scenes, finding that their ON-OFF selectivity varies with the image and is due to non-linear retinal circuit pathways.

The paper develops a divisive normalization model informed by V1 neuron responses to natural images, showing orientation-specific normalization within receptive fields.

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

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 shows that real and model-generated images can be distinguished at V2 scaling, challenging the interpretation of Bouma’s Law in crowding.

This paper uses deep convolutional neural networks to predict spiking activity in primate V1 and finds they outperform traditional models.

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.

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.

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 investigates how attentional fluctuations affect correlated variability in macaque primary visual cortex, finding significant effects at both long and short timescales.

The paper compares texture models using deep CNN features and finds that while CNN models perform well under foveal inspection, no model perfectly matches all textures across viewing conditions.

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