Using goal driven deep learning models to understand sensory cortex
Fueled by innovation in the computer vision and artificial intelligence communities, recent developments in computational neuroscience have used goal driven hierarchical convolutional neural networks (HCNNs) to make strides in modeling neural single unit and population responses in higher visual cortical areas. In this Perspective, we review the recent progress in a broader modeling context and describe some of the key technical innovations that have supported it. We then outline how the goal driven HCNN approach can be used to delve even more deeply into understanding the development and organization of sensory cortical processing.
Nature Neuroscience