Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation.

Studies of the mouse visual system have revealed a variety of visual brain areas that are thought to support a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse's visual cortex, and how it supports a range of behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex and identifying key structural and functional principles underlying that model's success.

Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity.

Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations.

Time-resolved correspondences between deep neural network layers and EEG measurements in object processing.

The ventral visual stream is known to be organized hierarchically, where early visual areas processing simplistic features feed into higher visual areas processing more complex features. Hierarchical convolutional neural networks (CNNs) were largely inspired by this type of brain organization and have been successfully used to model neural responses in different areas of the visual system. In this work, we aim to understand how an instance of these models corresponds to temporal dynamics of human object processing.