Edgar Y. Walker

The paper shows that neural activity across multiple brain regions reflects Bayesian belief updating during a multi-dimensional inference task in monkeys.

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

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

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.

The paper surveys data science practices in neuroscience collaborations to identify challenges and propose improvements in data management and training.

The paper develops a theoretical framework to quantify nonlinear neural decoding and applies it to analyze neural activity related to perceptual choices in monkeys.

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.

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

The paper introduces DataJoint Elements, a resource providing modular workflow designs for organizing data and computations in neurophysiology experiments.

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

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.

The paper investigates how uncertainty is encoded in primate visual cortex during a visual categorization task and shows that population activity encodes likelihood functions that predict decisions better than point estimates.

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

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.

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