Our brain continuously processes complex visual information, constructing a comprehensive understanding of the world around us from fleeting 2D retinal impressions. At the Brains and Machines Lab at BGU, we endeavor to understand human visual cognition by employing neural network modeling and model-driven experiments. Our research utilizes a range of techniques, including psychophysics, eye-tracking, and functional magnetic resonance imaging (fMRI), to probe the mechanisms underlying high-order human vision. By leveraging deep learning, we develop computational hypotheses that could explain the observed behavioral and neural data. We then design and conduct innovative model-driven experiments to empirically test these hypotheses, closing the loop between theory and experiment.
* Denotes equal contribution.
Synthesizing Controversial Stimuli (a tutorial with PyTorch)github.com/kriegeskorte-lab/controversial_stimuli_tutorial
This is a PyTorch tutorial on synthesizing controversial stimuli to disentangle the predictions of object recognition models. This tutorial was presented at CCN 2021 (Cognitive Computational Neuroscience).
Fully funded positions for Master's and Doctoral students are available! We are looking for highly motivated individuals with a passion for the intersection between deep learning and human cognition. If that sounds like you, please send your CV, current grade sheet, and a one-paragraph description of your scientific interests to firstname.lastname@example.org.
Lab: Building 90, Room 4
PI Office: Building 93B, Room 4
Address: Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, David Ben Gurion Blvd 1, Be'er Sheva, Israel