Taliah Muhammad

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.

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

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

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

~2-4x SNR gains and 20-300x compression on calcium and voltage imaging, from a patch-wise penalized matrix decomposition that splits signal from temporally uncorrelated noise without hyperparameter tuning.

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.