{"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/neurons-eye-view-inferring-features-of","title":"Neuron's Eye View: Inferring Features of Complex Stimuli from Neural Responses","arxiv_id":"1512.01408","date":"2015-12-04","proceeding":null,"authors":["Xin","Chen","Jeffrey M Beck","John M. Pearson"],"abstract":"Experiments that study neural encoding of stimuli at the level of individual\nneurons typically choose a small set of features present in the world ---\ncontrast and luminance for vision, pitch and intensity for sound --- and\nassemble a stimulus set that systematically varies along these dimensions.\nSubsequent analysis of neural responses to these stimuli typically focuses on\nregression models, with experimenter-controlled features as predictors and\nspike counts or firing rates as responses. Unfortunately, this approach\nrequires knowledge in advance about the relevant features coded by a given\npopulation of neurons. For domains as complex as social interaction or natural\nmovement, however, the relevant feature space is poorly understood, and an\narbitrary \\emph{a priori} choice of features may give rise to confirmation\nbias. Here, we present a Bayesian model for exploratory data analysis that is\ncapable of automatically identifying the features present in unstructured\nstimuli based solely on neuronal responses. Our approach is unique within the\nclass of latent state space models of neural activity in that it assumes that\nfiring rates of neurons are sensitive to multiple discrete time-varying\nfeatures tied to the \\emph{stimulus}, each of which has Markov (or semi-Markov)\ndynamics. That is, we are modeling neural activity as driven by multiple\nsimultaneous stimulus features rather than intrinsic neural dynamics. We derive\na fast variational Bayesian inference algorithm and show that it correctly\nrecovers hidden features in synthetic data, as well as ground-truth stimulus\nfeatures in a prototypical neural dataset. To demonstrate the utility of the\nalgorithm, we also apply it to cluster neural responses and demonstrate\nsuccessful recovery of features corresponding to monkeys and faces in the image\nset.","url_abs":"http://arxiv.org/abs/1512.01408v2","url_pdf":"http://arxiv.org/pdf/1512.01408v2.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":"neurons-eye-view-inferring-features-of","repo_url":"https://github.com/pearsonlab/spiketopics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"state-space-models","task_name":"State Space Models"}],"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}