{"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/latent-dynamical-variables-produce-signatures","title":"Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems","arxiv_id":"2008.04435","date":"2020-08-10","proceeding":null,"authors":[],"abstract":"Understanding the activity of large populations of neurons is difficult due\nto the combinatorial complexity of possible cell-cell interactions. To reduce\nthe complexity, coarse-graining had been previously applied to experimental\nneural recordings, which showed over two decades of scaling in free energy,\nactivity variance, eigenvalue spectra, and correlation time, hinting that the\nmouse hippocampus operates in a critical regime. We model the experiment by\nsimulating conditionally independent binary neurons coupled to a small number\nof long-timescale stochastic fields and then replicating the coarse-graining\nprocedure and analysis. This reproduces the experimentally-observed scalings,\nsuggesting that they may arise from coupling the neural population activity to\nlatent dynamic stimuli. Further, parameter sweeps for our model suggest that\nemergence of scaling requires most of the cells in a population to couple to\nthe latent stimuli, predicting that even the celebrated place cells must also\nrespond to non-place stimuli.","url_abs":"http://arxiv.org/abs/2008.04435v1","url_pdf":"http://arxiv.org/pdf/2008.04435v1.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":"latent-dynamical-variables-produce-signatures","repo_url":"https://github.com/mcmorre/placerg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Hippocampus"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}