Alessandro Gifford
@alessandrogifford.bsky.social
Postdoc in computational neuroscience at Freie Universität Berlin, with Radoslaw Cichy.
https://www.alegifford.com/
https://www.alegifford.com/
(6/7) Out-of-distribution generalization tests on NSD-synthetic reveal differences between brain models that are not detected by in-distribution tests => self-supervised deep neural networks better explain neural responses than their task-supervised counterparts.
March 11, 2025 at 5:05 PM
(6/7) Out-of-distribution generalization tests on NSD-synthetic reveal differences between brain models that are not detected by in-distribution tests => self-supervised deep neural networks better explain neural responses than their task-supervised counterparts.
(5/7) NSD-synthetic is a stricter testbed that highlights deficiencies of brain encoding models not observable with in-distribution tests.
March 11, 2025 at 5:05 PM
(5/7) NSD-synthetic is a stricter testbed that highlights deficiencies of brain encoding models not observable with in-distribution tests.
(4/7) NSD-synthetic’s fMRI responses are out-of-distribution with respect to NSD-core.
March 11, 2025 at 5:05 PM
(4/7) NSD-synthetic’s fMRI responses are out-of-distribution with respect to NSD-core.
(3/7) NSD-synthetic’s fMRI responses reliably encode stimulus-related information over visual cortex.
March 11, 2025 at 5:05 PM
(3/7) NSD-synthetic’s fMRI responses reliably encode stimulus-related information over visual cortex.
(2/7) NSD-synthetic consists of 7T fMRI responses from the eight NSD subjects for 284 carefully controlled synthetic images, divided into 8 classes.
March 11, 2025 at 5:05 PM
(2/7) NSD-synthetic consists of 7T fMRI responses from the eight NSD subjects for 284 carefully controlled synthetic images, divided into 8 classes.