{"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/detection-of-persistent-signals-and-its","title":"Detection of persistent signals and its relation to coherent feedforward loops","arxiv_id":"1802.01806","date":"2018-10-11","proceeding":null,"authors":[],"abstract":"Many studies have shown that cells use temporal dynamics of signalling\nmolecules to encode information. One particular class of temporal dynamics is\npersistent and transient signals, i.e. signals of long and short durations\nrespectively. It has been shown that the coherent type-1 feedforward loop with\nan AND logic at the output (or C1-FFL for short) can be used to discriminate a\npersistent input signal from a transient one. This has been done by modelling\nthe C1-FFL, and then use the model to show that persistent and transient input\nsignals give, respectively, a non-zero and zero output. Instead of assuming the\nstructure of C1-FFL, this paper shows that it is possible to deduce the C1-FFL\nmodel from the requirement of discriminating a persistent signal. We do this by\nfirst formulating a statistical detection problem of distinguishing persistent\nsignals from transient ones. The solution of the detection problem is to\ncompute the log-likelihood ratio of observing a persistent signal to a\ntransient signal. We show that, if this log-likelihood ratio is positive, which\nhappens when the signal is likely to be persistent, then it can be\napproximately computed by a C1-FFL. Although the capability of C1-FFL to\ndiscriminate persistent signals is known, this paper adds an information\nprocessing interpretation on how a C1-FFL works as a detector of persistent\nsignals.","url_abs":"http://arxiv.org/abs/1802.01806v3","url_pdf":"http://arxiv.org/pdf/1802.01806v3.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":"detection-of-persistent-signals-and-its","repo_url":"https://github.com/ctchou-unsw/c1ffl-journal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}