Santiago A. Cadena

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.

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

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

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.

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