fMRI and neural encoding models: Voxel receptive fields (continued)

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Presentation transcript:

fMRI and neural encoding models: Voxel receptive fields (continued)

A voxel receptive field model of visual cortex: Kay et al, Nature, 2008

A voxel receptive field model of visual cortex: Kay et al, Nature, 2008

A voxel receptive field model of visual cortex: Kay et al, Nature, 2008

A specific voxel’s receptive field

Generative and discriminative models From Christopher Bishop book: Pattern Recongition and Machine Learning

Huth et al. (2012) Semantic space in cortex

Principal Components Analysis (PCA) http://web.media.mit.edu/~tristan/phd/dissertation/figures/PCA.jpg

Representing categories in WordNet

Representing multiple semantic principal components

A closer look at semantic space

What do the components mean?

Highly distributed representations

How much of each region’s activation does the model explain?

Summary: Voxel receptive field models Generative model of neural activation, as opposed to a discriminative model Often called encoding models vs. decoding models Useful for synthesising (generating) inferred inputs, e.g. the image someone was seeing Still leaves lots of neural function unexplained, but a very useful step forward nonetheless