{"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-entropy-estimation-for-binary-spike","title":"Bayesian entropy estimation for binary spike train data using parametric prior knowledge","arxiv_id":null,"date":"2013-12-01","proceeding":"NeurIPS 2013 12","authors":["Evan W. Archer","Il Memming Park","Jonathan W. Pillow"],"abstract":"Shannon's entropy is a basic quantity in information theory, and a  fundamental building block for the analysis of neural codes.   Estimating the entropy of a discrete distribution from samples is an important and difficult problem that has received considerable   attention in statistics and theoretical neuroscience.  However,  neural responses have characteristic statistical structure that   generic entropy estimators fail to exploit.  For example, existing  Bayesian entropy estimators make the naive assumption that all spike   words are equally likely a priori, which makes for an  inefficient allocation of prior probability mass in cases where   spikes are sparse.  Here we develop Bayesian estimators for the  entropy of binary spike trains using priors designed to flexibly   exploit the statistical structure of simultaneously-recorded spike  responses.  We define two prior distributions over spike words using   mixtures of Dirichlet distributions centered on simple parametric  models.  The parametric model captures high-level statistical   features of the data, such as the average spike count in a spike  word, which allows the posterior over entropy to concentrate more   rapidly than with standard estimators (e.g., in cases where the  probability of spiking differs strongly from 0.5). Conversely, the   Dirichlet distributions assign prior mass to distributions far from  the parametric model, ensuring consistent estimates for arbitrary   distributions.  We devise a compact representation of the data and  prior that allow for computationally efficient implementations of   Bayesian least squares and empirical Bayes entropy estimators with  large numbers of neurons.  We apply these estimators to simulated   and real neural data and show that they substantially outperform  traditional methods.","url_abs":"http://papers.nips.cc/paper/4873-bayesian-entropy-estimation-for-binary-spike-train-data-using-parametric-prior-knowledge","url_pdf":"http://papers.nips.cc/paper/4873-bayesian-entropy-estimation-for-binary-spike-train-data-using-parametric-prior-knowledge.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-entropy-estimation-for-binary-spike","repo_url":"https://github.com/pillowlab/CDMentropy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}