1 code implementation • 25 Feb 2022 • Furkan Ozcelik, Bhavin Choksi, Milad Mozafari, Leila Reddy, Rufin VanRullen
Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research.
1 code implementation • NeurIPS Workshop SVRHM 2021 • Bhavin Choksi, Milad Mozafari, Rufin VanRullen, Leila Reddy
The human hippocampus possesses "concept cells", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality.
1 code implementation • 8 Jun 2021 • Andrea Alamia, Milad Mozafari, Bhavin Choksi, Rufin VanRullen
That is, we let the optimization process determine whether top-down connections and predictive coding dynamics are functionally beneficial.
2 code implementations • NeurIPS 2021 • Bhavin Choksi, Milad Mozafari, Callum Biggs O'May, Benjamin Ador, Andrea Alamia, Rufin VanRullen
The reconstruction errors are used to iteratively update the network's representations across timesteps, and to optimize the network's feedback weights over the natural image dataset-a form of unsupervised training.
1 code implementation • CoNLL (EMNLP) 2021 • Benjamin Devillers, Bhavin Choksi, Romain Bielawski, Rufin VanRullen
Vision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets.
2 code implementations • NeurIPS Workshop SVRHM 2020 • Zhaoyang Pang, Callum Biggs O'May, Bhavin Choksi, Rufin VanRullen
Finally we validated our conclusions in a deeper network (VGG): adding the same predictive coding feedback dynamics again leads to the perception of illusory contours.
1 code implementation • NeurIPS Workshop SVRHM 2020 • Bhavin Choksi, Milad Mozafari, Callum Biggs O'May, B. ADOR, Andrea Alamia, Rufin VanRullen
The reconstruction errors are used to iteratively update the network’s representations across timesteps, and to optimize the network's feedback weights over the natural image dataset--a form of unsupervised training.