{"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/towards-a-theory-of-cortical-columns-from","title":"Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size","arxiv_id":"1611.00294","date":"2017-04-21","proceeding":null,"authors":[],"abstract":"Neural population equations such as neural mass or field models are widely\nused to study brain activity on a large scale. However, the relation of these\nmodels to the properties of single neurons is unclear. Here we derive an\nequation for several interacting populations at the mesoscopic scale starting\nfrom a microscopic model of randomly connected generalized integrate-and-fire\nneuron models. Each population consists of 50 -- 2000 neurons of the same type\nbut different populations account for different neuron types. The stochastic\npopulation equations that we find reveal how spike-history effects in\nsingle-neuron dynamics such as refractoriness and adaptation interact with\nfinite-size fluctuations on the population level. Efficient integration of the\nstochastic mesoscopic equations reproduces the statistical behavior of the\npopulation activities obtained from microscopic simulations of a full spiking\nneural network model. The theory describes nonlinear emergent dynamics like\nfinite-size-induced stochastic transitions in multistable networks and\nsynchronization in balanced networks of excitatory and inhibitory neurons. The\nmesoscopic equations are employed to rapidly simulate a model of a local\ncortical microcircuit consisting of eight neuron types. Our theory establishes\na general framework for modeling finite-size neural population dynamics based\non single cell and synapse parameters and offers an efficient approach to\nanalyzing cortical circuits and computations.","url_abs":"http://arxiv.org/abs/1611.00294v3","url_pdf":"http://arxiv.org/pdf/1611.00294v3.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":"towards-a-theory-of-cortical-columns-from","repo_url":"https://github.com/schwalger/mesopopdyn_gif","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}