{"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/linear-dynamical-neural-population-models","title":"Linear dynamical neural population models through nonlinear embeddings","arxiv_id":"1605.08454","date":"2016-05-26","proceeding":"NeurIPS 2016 12","authors":["Yuanjun Gao","Evan Archer","Liam Paninski","John P. Cunningham"],"abstract":"A body of recent work in modeling neural activity focuses on recovering\nlow-dimensional latent features that capture the statistical structure of\nlarge-scale neural populations. Most such approaches have focused on linear\ngenerative models, where inference is computationally tractable. Here, we\npropose fLDS, a general class of nonlinear generative models that permits the\nfiring rate of each neuron to vary as an arbitrary smooth function of a latent,\nlinear dynamical state. This extra flexibility allows the model to capture a\nricher set of neural variability than a purely linear model, but retains an\neasily visualizable low-dimensional latent space. To fit this class of\nnon-conjugate models we propose a variational inference scheme, along with a\nnovel approximate posterior capable of capturing rich temporal correlations\nacross time. We show that our techniques permit inference in a wide class of\ngenerative models.We also show in application to two neural datasets that,\ncompared to state-of-the-art neural population models, fLDS captures a much\nlarger proportion of neural variability with a small number of latent\ndimensions, providing superior predictive performance and interpretability.","url_abs":"http://arxiv.org/abs/1605.08454v2","url_pdf":"http://arxiv.org/pdf/1605.08454v2.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":"linear-dynamical-neural-population-models","repo_url":"https://github.com/earcher/vilds","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.08454","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}