[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124686-en":3,"doc-seo-124686-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124686,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Strong and weak principles of Bayesian machine learning for systems neuroscience - Doctor of Philosophy dissertation","Strong and weak principles of Bayesian machine learning for systems neuroscience. Neural recording and behavioural measurements have expanded rapidly, creating large datasets that demand principled modelling methods. Drawing on Bayesian machine learning, the work separates approaches into weak models for data analysis that embed prior knowledge into the pipeline, and strong models that treat brain dynamics as implementing Bayesian inference. The dissertation reviews both directions, then develops new weak latent-variable models for unsupervised inference of dimensionality and topology, a strong framework for Bayesian continual learning, and a reinforcement-learning-as-Bayesian-inference planning model explaining hippocampal replay and flexible behaviour.","Strong and weak principles of Bayesian machine learning for systems neuroscience  \nKristopher Torp Jensen  \nDepartment of Engineering  \nUniversity of Cambridge  \nThis dissertation is submitted for the degree of  \nDoctor of Philosophy  \nJesus College April 2023  \nDeclaration  \nI hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and Acknowledgements. This dissertation contains fewer than 65,000 words including appendices, footnotes, tables and equations and has fewer than 150 figures.  \nKristopher Torp Jensen April 2023  \nStrong and weak principles of Bayesian machine learning  \nfor systems neuroscience  \nKristopher Torp Jensen  \nNeuroscientists are recording neural activity and behaviour at a rapidly increasing scale. This provides an unprecedented window into the neural underpinnings of behaviour, while also pushing the need for new techniques to analyse and model these large-scale datasets. Inspiration for such tools can be found in the Bayesian machine learning literature, which provides a set of principled techniques that allow us to perform inference in complex problem settings with large parameter spaces. When applied to neural population recordings, we propose that these approaches can be divided into ‘weak’ and ‘strong’ models of neural data. The weak models consist of tools for analysing experimental data, which build our own prior knowledge of neural circuits directly into the analysis pipeline. In contrast, strong Bayesian models of neural dynamics posit that the brain itself performs something akin to Bayesian inference. In this view, we can interpret our Bayesian machine learning models as algorithmic or mechanistic models of the learning processes and computations taking place in the biological brain. In this work, we first provide an overview of Bayesian machine learning and its applications to neuroscience, highlighting how both the strong and weak approaches have improved our understanding of neural computations in recent years. We then develop several new models in this field, which provide insights into neural computations ranging from motor control to navigation and decision making. These models can be grouped into three broad categories. First, we construct a series of new ‘weak’ latent variable models that allow us to infer the dimensionality and topology of neural data in an unsupervised manner. We highlight the utility of such approaches on synthetic data and across several biological circuits involved in motor control and navigation. Second, we propose a new method for Bayesian continual learning and relate it to longitudinal recordings of neural activity as a ‘strong’ model of biological learning and memory. Finally, we develop a new ‘strong’ model of planning and decision making through the lens of reinforcement learning formulated as Bayesian inference. In contrast to previous network models, we explicitly build in the capacity for planning-by-simulation and show that this explains many features of both human behaviour and rodent hippocampal replays. This results in a new theory of the role of hippocampus in flexible planning. The new methods developed in this work both expand the Bayesian toolbox available to systems neuroscientists and provide new insights into the neural computations driving natural behaviours.  \nAcknowledgements  \nScience is collaborative. Much of this work has therefore been done in collaboration with my excellent colleagues as specified in detail here. I have worked with Ta-Chu Kao and Jasmine Stone on large parts of Section 3.1, some of which was included in the MPhil thesis of Jasmine Stone and which has also been ","cbCaieRQzi9l81pk","https://ap.wps.com/l/cbCaieRQzi9l81pk","pdf",11805723,1,214,"English","en",105,"# List of figures\n# List of tables\n# 1 Introduction","[{\"question\":\"What distinguishes “weak” and “strong” Bayesian models in this dissertation?\",\"answer\":\"Weak models focus on analysing experimental neural data by embedding prior knowledge into the analysis pipeline. Strong Bayesian models interpret brain dynamics as performing something akin to Bayesian inference, turning the models into mechanistic descriptions of learning and computation.\"},{\"question\":\"What new weak models are developed for neural data?\",\"answer\":\"The dissertation develops new weak latent-variable models that infer the dimensionality and topology of neural data in an unsupervised manner, demonstrated on synthetic data and multiple biological circuits in motor control and navigation.\"},{\"question\":\"How does the dissertation connect strong Bayesian learning to neural recordings?\",\"answer\":\"It proposes a new method for Bayesian continual learning and relates it to longitudinal recordings of neural activity as a strong model of biological learning and memory.\"}]","Strong and weak principles of Bayesian machine learning for systems neuroscience - Doctor of Philosophy dissertation | PDF",1785893911,539,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"strong-and-weak-principles-of-bayesian-machine-learning-for-systems-neuroscience-doctor-of-philosophy-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/strong-and-weak-principles-of-bayesian-machine-learning-for-systems-neuroscience-doctor-of-philosophy-dissertation/124686/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What distinguishes “weak” and “strong” Bayesian models in this dissertation?","Question",{"text":75,"@type":76},"Weak models focus on analysing experimental neural data by embedding prior knowledge into the analysis pipeline. Strong Bayesian models interpret brain dynamics as performing something akin to Bayesian inference, turning the models into mechanistic descriptions of learning and computation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What new weak models are developed for neural data?",{"text":80,"@type":76},"The dissertation develops new weak latent-variable models that infer the dimensionality and topology of neural data in an unsupervised manner, demonstrated on synthetic data and multiple biological circuits in motor control and navigation.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation connect strong Bayesian learning to neural recordings?",{"text":84,"@type":76},"It proposes a new method for Bayesian continual learning and relates it to longitudinal recordings of neural activity as a strong model of biological learning and memory.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]