Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8957351 | NeuroImage | 2018 | 29 Pages |
Abstract
We describe an optimization algorithm for estimating fwRFÂ models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model's application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRFÂ model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRFÂ model can be used to regress entire deep convolutional networks against brain activity. The ability to use whole networks in a single encoding model yields state-of-the-art prediction accuracy. Our results suggest a wide variety of uses for the feature-weighted receptive field model, from retinotopic mapping with natural scenes, to regressing the activities of whole deep neural networks onto measured brain activity.
Keywords
Related Topics
Life Sciences
Neuroscience
Cognitive Neuroscience
Authors
Ghislain St-Yves, Thomas Naselaris,