Jiakun Fu

This paper incorporates inhibition into deep neural networks for neural prediction, showing it improves surround suppression learning but not cross-orientation inhibition.

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 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.

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

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

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