{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multimodal-neural-networks-better-explain-1","title":"Multimodal neural networks better explain multivoxel patterns in the hippocampus","arxiv_id":"2201.11517","date":"2021-12-11","proceeding":"NeurIPS Workshop SVRHM 2021 12","authors":["Bhavin Choksi","Milad Mozafari","Rufin VanRullen","Leila Reddy"],"abstract":"The human hippocampus possesses \"concept cells\", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality. Recently, similar concept cells were discovered in a multimodal network called CLIP (Radford et at., 2021). Here, we ask whether CLIP can explain the fMRI activity of the human hippocampus better than a purely visual (or linguistic) model. We extend our analysis to a range of publicly available uni- and multi-modal models. We demonstrate that \"multimodality\" stands out as a key component when assessing the ability of a network to explain the multivoxel activity in the hippocampus.","url_abs":"https://arxiv.org/abs/2201.11517v1","url_pdf":"https://arxiv.org/pdf/2201.11517v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multimodal-neural-networks-better-explain-1","repo_url":"https://github.com/bhavinc/mutlimodal-concepts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"CC-BY-4.0"}}],"tasks":[{"task_slug":null,"task_name":"Hippocampus"}],"methods":[{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2201.11517","atlas_url":"https://app.syntology.ai/?focus=2201.11517","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}