{"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/variational-latent-gaussian-process-for","title":"Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains","arxiv_id":"1604.03053","date":"2016-04-11","proceeding":null,"authors":["Yuan Zhao","Il Memming Park"],"abstract":"When governed by underlying low-dimensional dynamics, the interdependence of\nsimultaneously recorded population of neurons can be explained by a small\nnumber of shared factors, or a low-dimensional trajectory. Recovering these\nlatent trajectories, particularly from single-trial population recordings, may\nhelp us understand the dynamics that drive neural computation. However, due to\nthe biophysical constraints and noise in the spike trains, inferring\ntrajectories from data is a challenging statistical problem in general. Here,\nwe propose a practical and efficient inference method, called the variational\nlatent Gaussian process (vLGP). The vLGP combines a generative model with a\nhistory-dependent point process observation together with a smoothness prior on\nthe latent trajectories. The vLGP improves upon earlier methods for recovering\nlatent trajectories, which assume either observation models inappropriate for\npoint processes or linear dynamics. We compare and validate vLGP on both\nsimulated datasets and population recordings from the primary visual cortex. In\nthe V1 dataset, we find that vLGP achieves substantially higher performance\nthan previous methods for predicting omitted spike trains, as well as capturing\nboth the toroidal topology of visual stimuli space, and the noise-correlation.\nThese results show that vLGP is a robust method with a potential to reveal\nhidden neural dynamics from large-scale neural recordings.","url_abs":"http://arxiv.org/abs/1604.03053v5","url_pdf":"http://arxiv.org/pdf/1604.03053v5.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":"variational-latent-gaussian-process-for","repo_url":"https://github.com/yuanz271/vlgpax","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok"}}],"tasks":[{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.03053","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}