English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

While significant advancements in artificial intelligence (AI) have catalyzedprogress across various domains, its full potential in understanding visualperception remains underexplored. We propose an artificial neural networkdubbed VISION, an acronym for "Visual Interface System for Imaging Output ofNeural activity," to mimic the human brain and show how it can fosterneuroscientific inquiries. Using visual and contextual inputs, this multimodalmodel predicts the brain's functional magnetic resonance imaging (fMRI) scanresponse to natural images. VISION successfully predicts human hemodynamicresponses as fMRI voxel values to visual inputs with an accuracy exceedingstate-of-the-art performance by 45%. We further probe the trained networks toreveal representational biases in different visual areas, generateexperimentally testable hypotheses, and formulate an interpretable metric toassociate these hypotheses with cortical functions. With both a model andevaluation metric, the cost and time burdens associated with designing andimplementing functional analysis on the visual cortex could be reduced. Ourwork suggests that the evolution of computational models may shed light on ourfundamental understanding of the visual cortex and provide a viable approachtoward reliable brain-machine interfaces.

Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex