[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-638972-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-638972-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","sequential-optimal-design-of-neurophysiology-experiments","Sequential Optimal Design of Neurophysiology Experiments","","Sequential adaptive experimental design can greatly reduce trial counts when estimating parametric neural models, but neurophysiology applications are constrained by severe real-time computation demands. This work introduces a fast algorithm that selects the most informative stimulus by maximizing mutual information between observed data and unknown generalized linear model parameters. It exploits posterior log-concavity and asymptotic normality, requiring only low-rank matrix operations and a two-dimensional search, with quadratic scaling in GLM dimensionality. Approximations preserve asymptotically optimal parameter-uncertainty reduction, and simulations show about an order-of-magnitude faster convergence than random nonadaptive stimuli, while supporting adaptation to spike-history effects and slow nonstationary drifts.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/sequential-optimal-design-of-neurophysiology-experiments/638972/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/sequential-optimal-design-of-neurophysiology-experiments/638972.png","ImageObject",300,407,{"name":42,"@type":43},"MrHarris58","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-03","2026-10-22",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of the proposed method?","Question",{"text":63,"@type":64},"The method aims to adapt neurophysiology experiment design sequentially by choosing stimuli that maximize mutual information between data and the parameters of a generalized linear model.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the algorithm make real-time computation feasible?",{"text":68,"@type":64},"It leverages posterior log concavity and asymptotic normality, using only low-rank matrix manipulations and a two-dimensional search for optimal stimulus selection.",{"name":70,"@type":61,"acceptedAnswer":71},"What performance benefits are reported compared with random nonadaptive stimuli?",{"text":72,"@type":64},"Simulations indicate that mutual-information-based stimulus selection speeds convergence to optimal parameter values by about an order of magnitude versus random (nonadaptive) stimuli.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},638972,1793115298,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":94,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":144},687212988360,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Sequential Optimal Design of Neurophysiology Experiments  \nJeremy Lewi  \n[jeremy@lewi.us](jeremy@lewi.us)  \nBioengineering Graduate Program, Wallace H. Coulter Department of Biomedical Engineering, Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A. [http://www.lewilab.org](http://www.lewilab.org)  \nRobert Butera  \n[rbutera@ece.gatech.edu](rbutera@ece.gatech.edu)  \nSchool of Electrical and Computer Engineering, Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.  \nLiam Paninski  \n[liam@stat.columbia.edu](liam@stat.columbia.edu)  \nDepartment of Statistics and Center for Neurotheory, Columbia University, New York, NY 10027, U.S.A. [http://www.stat.columbia.edu/](http://www.stat.columbia.edu/)∼liam  \nAdaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high-dimensional systems, optimizing neurophysiology experiments requires computing high-dimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model(GLM)that we want tofittothe neuron’s activity. Werelyon important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only low-rank matrix manipulations and a two-dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making realtime adaptive experimental design feasible even for high-dimensional stimulus and parameter spaces. For example, we require roughly 10 milliseconds on a desktop computer to optimize a 100-dimensional stimulus. Despite using some approximations to make the algorithm efficient, our algorithm asymptotically decreases the uncertainty about the model parameters at a rate equal to the maximum rate predicted by an asymptotic analysis. Simulation results show that picking stimuli by maximizing the mutual information can speed up convergence to the optimal values of the parameters by an order of magnitude compared  \nNeural Computation 21, 619–687 (2009)  2008 Massachusetts Institute of Technology  \n620 J. Lewi, R. Butera, and L. Paninski  \nto using random (nonadaptive) stimuli. Finally, applying our design procedure to real neurophysiology experiments requires addressing thenonstationarities that we would expect to see in neural responses; our algorithm can efficiently handle both fast adaptation due to spike history effects and slow, nonsystematic drifts in a neuron’s activity.  \n1 Introduction    \nIn most neurophysiology experiments, data are collected according to a design that is finalized before the experiment begins. During the experiment, the data already collected are rarely analyzed to evaluate the quality of the design. These data, however, often contain information that couldbe used toredesign experiments to better test hypotheses (Fedorov, 1972; Chaloner & Verdinelli,1995;Kontsevich&Tyler,1999;Warmuthetal.,2003;Roy, Ghosal,& Rosenberger, in press) . Adaptive experimental designs are particularly valuable in domains where data are expensive or limited. In neuroscience, experiments often require training and caring for animals, which can be time-consuming and costly. As a result of these costs, neuroscientists are often unable to conduct large numbers of trials using different subjects. The inability to collect enough data makes it difficult for them to investigate high-dimensional, complex neural systems. By using adaptive experimental designs, neuroscientists could potentially collect data more efficiently. In this article, we develop an effic","cbCaipVXaAR1FKGZ","https://ap.wps.com/l/cbCaipVXaAR1FKGZ","pdf",1345985,"English","# Introduction\n## Problem motivation and limitations of nonadaptive designs\n## Modeling neural nonlinearities with GLM and stimulus/response history\n## Notation for controllable vs fixed input components\n## Infomax and i.i.d. design schematics","[{\"question\":\"What is the main goal of the proposed method?\",\"answer\":\"The method aims to adapt neurophysiology experiment design sequentially by choosing stimuli that maximize mutual information between data and the parameters of a generalized linear model.\"},{\"question\":\"How does the algorithm make real-time computation feasible?\",\"answer\":\"It leverages posterior log concavity and asymptotic normality, using only low-rank matrix manipulations and a two-dimensional search for optimal stimulus selection.\"},{\"question\":\"What performance benefits are reported compared with random nonadaptive stimuli?\",\"answer\":\"Simulations indicate that mutual-information-based stimulus selection speeds convergence to optimal parameter values by about an order of magnitude versus random (nonadaptive) stimuli.\"}]","Sequential Optimal Design of Neurophysiology Experiments | PDF",1792700694,176]