{"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/interpreting-wide-band-neural-activity-using","title":"Interpreting wide-band neural activity using convolutional neural networks","arxiv_id":null,"date":"2021-08-02","proceeding":"eLife 2021 8","authors":["Markus Frey","Sander Tanni","Catherine Perrodin","Alice O'Leary","Matthias Nau","Jack Kelly","Andrea Banino","Daniel Bendor","Julie Lefort","Christian F Doeller","Caswell Barry"],"abstract":"Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we\r\ndeveloped a deep-learning-framework able to decode sensory and behavioural variables directly\r\nfrom wide-band neural data. The network requires little user input and generalizes across stimuli,\r\nbehaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine\r\nelements of the neural code that are informative about a given variable. We validated this approach\r\nusing data from rodent auditory cortex and hippocampus, identifying a novel representation of\r\nhead direction encoded by putative CA1 interneurons.","url_abs":"https://elifesciences.org/articles/66551","url_pdf":"https://elifesciences.org/articles/66551#downloads","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":[],"tasks":[{"task_slug":"brain-decoding","task_name":"Brain Decoding"},{"task_slug":null,"task_name":"Hippocampus"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-decoding-on-bci-competition-iv-ecog-to","task":"Brain Decoding","dataset":"BCI Competition IV: ECoG to Finger Movements","model":"Multi purpose CNN","rank_in_archive_order":4,"of":7,"metrics":{"Pearson Correlation":"0.52"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}