Bryan Tripp

The paper examines spatial convergence patterns of sensory inputs in the mouse isocortex, introducing the topography dimension (TD) and its relationship with neural activity and hierarchy.

This study investigates how training data properties affect the alignment of deep neural networks with mouse visual cortex activity, finding that naturalistic environments significantly improve similarity.

This paper models contour integration in visual processing using a neural network and finds it enhances contour detection in synthetic tasks but is not essential for natural image processing.

The paper introduces MouseNet, a biologically constrained CNN model of the mouse visual cortex, and shows it achieves 2/3rds of VGG16's ImageNet performance while recapitulating neural representations.

The paper introduces a convolutional neural network model of the mouse visual cortex constrained by neuroanatomical data and provides code for research.