Breedlove, J. L. | bioRxiv Science

bioRxiv2021

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.

neuroscience↗

bioRxiv2018

Human brain activity during mental imagery exhibits signatures of inference in a hierarchical generative model

Humans have long wondered about the function of mental imagery and its relationship to vision. Although visual representations are utilized during imagery, the computations they subserve are unclear. Building on a theory that treats vision as inference about the causes of sensory stimulation in an internal generative model, we propose that mental imagery is inference about the sensory consequences of predicted or remembered causes. The relation between these complementary inferences yields a relation between the brain activity patterns associated with imagery and vision. We show that this relation has the formal structure of an echo that makes encoding of imagined stimuli in low-level visual areas resemble the encoding of seen stimuli in higher areas. To test for evidence of this echo effect we developed imagery encoding models--a new tool for revealing how imagined stimuli are encoded in brain activity. We estimated imagery encoding models from brain activity measured with fMRI while human subjects imagined complex visual stimuli, and then compared these to visual encoding models estimated from a matched viewing experiment. Consistent with an echo effect, imagery encoding models in low-level visual areas exhibited decreased spatial frequency preference and larger, more foveal receptive fields, thus resembling visual encoding models in high-level visual areas where imagery and vision appeared to be almost interchangeable. Our findings support an interpretation of mental imagery as a predictive inference that is conditioned on activity in high-level visual cortex, and is related to vision through shared dependence on an internal model of the visual world.

neuroscience↗