{"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/linking-connectivity-dynamics-and","title":"Linking connectivity, dynamics and computations in low-rank recurrent neural networks","arxiv_id":"1711.09672","date":"2018-08-28","proceeding":null,"authors":[],"abstract":"Large scale neural recordings have established that the transformation of\nsensory stimuli into motor outputs relies on low-dimensional dynamics at the\npopulation level, while individual neurons exhibit complex selectivity.\nUnderstanding how low-dimensional computations on mixed, distributed\nrepresentations emerge from the structure of the recurrent connectivity and\ninputs to cortical networks is a major challenge. Here, we study a class of\nrecurrent network models in which the connectivity is a sum of a random part\nand a minimal, low-dimensional structure. We show that, in such networks, the\ndynamics are low dimensional and can be directly inferred from connectivity\nusing a geometrical approach. We exploit this understanding to determine\nminimal connectivity required to implement specific computations, and find that\nthe dynamical range and computational capacity quickly increase with the\ndimensionality of the connectivity structure. This framework produces testable\nexperimental predictions for the relationship between connectivity,\nlow-dimensional dynamics and computational features of recorded neurons.","url_abs":"http://arxiv.org/abs/1711.09672v2","url_pdf":"http://arxiv.org/pdf/1711.09672v2.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":"linking-connectivity-dynamics-and","repo_url":"https://github.com/fmastrogiuseppe/lowrank","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.09672","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}