Abstract
Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.
Highlights
An important goal of contemporary cognitive neuroscience is to characterize the relationship between stimulus features and human brain activity
We show that this normative encoding model learns sparse, topographic and invariant stimulus features from tens of thousands of grayscale natural image patches without supervision, and reproduces the population behavior of simple and complex cells
We find that these stimulus features significantly better drive blood-oxygen-level dependent hemodynamic responses in early visual areas than Gabor wavelets–the fundamental building blocks of the conventional approach
Summary
An important goal of contemporary cognitive neuroscience is to characterize the relationship between stimulus features and human brain activity. This relationship can be studied from two distinct but complementary perspectives of encoding and decoding [1]. The encoding perspective is concerned with how certain aspects of the environment are stored in the brain and uses models that predict brain activity in response to certain stimulus features. The decoding perspective uses models that predict specific stimulus features from stimulus-evoked brain activity and is concerned with how specific aspects of the environment are retrieved from the brain. With the advent of functional magnetic resonance imaging (fMRI), encoding and decoding in fMRI has made it possible to noninvasively characterize the relationship between stimulus features and human brain activity via localized changes in blood-oxygen-level dependent (BOLD) hemodynamic responses to sensory or cognitive stimulation [5]
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