{"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/network-plasticity-as-bayesian-inference","title":"Network Plasticity as Bayesian Inference","arxiv_id":"1504.05143","date":"2015-04-20","proceeding":null,"authors":["David Kappel","Stefan Habenschuss","Robert Legenstein","Wolfgang Maass"],"abstract":"General results from statistical learning theory suggest to understand not\nonly brain computations, but also brain plasticity as probabilistic inference.\nBut a model for that has been missing. We propose that inherently stochastic\nfeatures of synaptic plasticity and spine motility enable cortical networks of\nneurons to carry out probabilistic inference by sampling from a posterior\ndistribution of network configurations. This model provides a viable\nalternative to existing models that propose convergence of parameters to\nmaximum likelihood values. It explains how priors on weight distributions and\nconnection probabilities can be merged optimally with learned experience, how\ncortical networks can generalize learned information so well to novel\nexperiences, and how they can compensate continuously for unforeseen\ndisturbances of the network. The resulting new theory of network plasticity\nexplains from a functional perspective a number of experimental data on\nstochastic aspects of synaptic plasticity that previously appeared to be quite\npuzzling.","url_abs":"http://arxiv.org/abs/1504.05143v1","url_pdf":"http://arxiv.org/pdf/1504.05143v1.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":"network-plasticity-as-bayesian-inference","repo_url":"https://github.com/fournierlouis/synaptic_sampling_rbm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"learning-theory","task_name":"Learning Theory"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1504.05143","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}