{"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/demixed-principal-component-analysis-of","title":"Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters","arxiv_id":"1410.6031","date":"2014-10-22","proceeding":null,"authors":["Dmitry Kobak","Wieland Brendel","Christos Constantinidis","Claudia E. Feierstein","Adam Kepecs","Zachary F. Mainen","Ranulfo Romo","Xue-Lian Qi","Naoshige Uchida","Christian K. Machens"],"abstract":"Neurons in higher cortical areas, such as the prefrontal cortex, are known to\nbe tuned to a variety of sensory and motor variables. The resulting diversity\nof neural tuning often obscures the represented information. Here we introduce\na novel dimensionality reduction technique, demixed principal component\nanalysis (dPCA), which automatically discovers and highlights the essential\nfeatures in complex population activities. We reanalyze population data from\nthe prefrontal areas of rats and monkeys performing a variety of working memory\nand decision-making tasks. In each case, dPCA summarizes the relevant features\nof the population response in a single figure. The population activity is\ndecomposed into a few demixed components that capture most of the variance in\nthe data and that highlight dynamic tuning of the population to various task\nparameters, such as stimuli, decisions, rewards, etc. Moreover, dPCA reveals\nstrong, condition-independent components of the population activity that remain\nunnoticed with conventional approaches.","url_abs":"http://arxiv.org/abs/1410.6031v1","url_pdf":"http://arxiv.org/pdf/1410.6031v1.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":"demixed-principal-component-analysis-of","repo_url":"https://github.com/wielandbrendel/dPCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"demixed-principal-component-analysis-of","repo_url":"https://github.com/machenslab/dPCA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1410.6031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1410.6031"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wielandbrendel/dPCA","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/machenslab/dPCA","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"de05f38405877c6c","entry":"classification","repo":"wielandbrendel/dPCA","repo_kind":"official","path":"python/dPCA/utils.py","file_url":"https://github.com/wielandbrendel/dPCA/blob/HEAD/python/dPCA/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"de05f38405877c6c"}},{"code_sha256_prefix":"9b2e2949401b72c2","entry":"denoise_mask","repo":"wielandbrendel/dPCA","repo_kind":"official","path":"python/dPCA/utils.py","file_url":"https://github.com/wielandbrendel/dPCA/blob/HEAD/python/dPCA/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9b2e2949401b72c2"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}