{"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/characterizing-the-nonlinear-structure-of","title":"Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models","arxiv_id":"1904.10441","date":"2019-04-23","proceeding":null,"authors":[],"abstract":"Sensory neurons often have variable responses to repeated presentations of\nthe same stimulus, which can significantly degrade the stimulus information\ncontained in those responses. This information can in principle be preserved if\nvariability is shared across many neurons, but depends on the structure of the\nshared variability and its relationship to sensory encoding at the population\nlevel. The structure of this shared variability in neural activity can be\ncharacterized by latent variable models, although they have thus far typically\nbeen used under restrictive mathematical assumptions. Here we introduce two\nnonlinear latent variable models for analyzing large-scale neural recordings.\nWe first present a general nonlinear latent variable model that is agnostic to\nthe stimulus tuning properties of the individual neurons, and is hence well\nsuited for exploring neural populations whose tuning properties are not well\ncharacterized. This motivates a second class of model, the Generalized Affine\nModel, which simultaneously determines each neuron's stimulus selectivity and a\nset of latent variables that modulate these stimulus-driven responses both\nadditively and multiplicatively. While these approaches can detect very general\nnonlinear relationships in shared neural variability, we find that neural\nactivity recorded in anesthetized primary visual cortex (V1) is best described\nby a single additive and single multiplicative latent variable, i.e. an `affine\nmodel'. In contrast, application of the same models to recordings in awake\nmacaque prefrontal cortex discover more general nonlinearities to compactly\ndescribe the population response variability. These results thus demonstrate\nhow nonlinear latent variable models can be used to describe population\nresponse variability, and suggest that a range of methods is necessary to study\ndifferent brain regions under different experimental conditions.","url_abs":"http://arxiv.org/abs/1904.10441v1","url_pdf":"http://arxiv.org/pdf/1904.10441v1.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":"characterizing-the-nonlinear-structure-of","repo_url":"https://github.com/themattinthehatt/whiteway-et-al-2019-nbdt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}