{"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/differential-covariance-a-new-class-of","title":"Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings","arxiv_id":"1706.02451","date":"2017-06-08","proceeding":null,"authors":["Tiger W. Lin","Anup Das","Giri P. Krishnan","Maxim Bazhenov","Terrence J. Sejnowski"],"abstract":"With our ability to record more neurons simultaneously, making sense of these\ndata is a challenge. Functional connectivity is one popular way to study the\nrelationship between multiple neural signals. Correlation-based methods are a\nset of currently well-used techniques for functional connectivity estimation.\nHowever, due to explaining away and unobserved common inputs (Stevenson et al.,\n2008), they produce spurious connections. The general linear model (GLM), which\nmodels spikes trains as Poisson processes (Okatan et al., 2005; Truccolo et\nal., 2005; Pillow et al., 2008), avoids these confounds. We develop here a new\nclass of methods by using differential signals based on simulated intracellular\nvoltage recordings. It is equivalent to a regularized AR(2) model. We also\nexpand the method to simulated local field potential (LFP) recordings and\ncalcium imaging. In all of our simulated data, the differential\ncovariance-based methods achieved better or similar performance to the GLM\nmethod and required fewer data samples. This new class of methods provides\nalternative ways to analyze neural signals.","url_abs":"http://arxiv.org/abs/1706.02451v1","url_pdf":"http://arxiv.org/pdf/1706.02451v1.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":"differential-covariance-a-new-class-of","repo_url":"https://github.com/tigerwlin/diffCov","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"connectivity-estimation","task_name":"Connectivity Estimation"},{"task_slug":null,"task_name":"Functional Connectivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}