{"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/bayesian-latent-structure-discovery-from","title":"Bayesian latent structure discovery from multi-neuron recordings","arxiv_id":"1610.08465","date":"2016-10-26","proceeding":"NeurIPS 2016 12","authors":["Scott W. Linderman","Ryan P. Adams","Jonathan W. Pillow"],"abstract":"Neural circuits contain heterogeneous groups of neurons that differ in type,\nlocation, connectivity, and basic response properties. However, traditional\nmethods for dimensionality reduction and clustering are ill-suited to\nrecovering the structure underlying the organization of neural circuits. In\nparticular, they do not take advantage of the rich temporal dependencies in\nmulti-neuron recordings and fail to account for the noise in neural spike\ntrains. Here we describe new tools for inferring latent structure from\nsimultaneously recorded spike train data using a hierarchical extension of a\nmulti-neuron point process model commonly known as the generalized linear model\n(GLM). Our approach combines the GLM with flexible graph-theoretic priors\ngoverning the relationship between latent features and neural connectivity\npatterns. Fully Bayesian inference via P\\'olya-gamma augmentation of the\nresulting model allows us to classify neurons and infer latent dimensions of\ncircuit organization from correlated spike trains. We demonstrate the\neffectiveness of our method with applications to synthetic data and\nmulti-neuron recordings in primate retina, revealing latent patterns of neural\ntypes and locations from spike trains alone.","url_abs":"http://arxiv.org/abs/1610.08465v1","url_pdf":"http://arxiv.org/pdf/1610.08465v1.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":"bayesian-latent-structure-discovery-from","repo_url":"https://github.com/slinderman/pyglm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"bayesian-latent-structure-discovery-from","repo_url":"https://github.com/slinderman/pypolyagamma","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.08465","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}