{"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-structure-of-cascading-neural-systems","title":"Network structure of cascading neural systems predicts stimulus propagation and recovery","arxiv_id":"1812.09361","date":"2019-11-10","proceeding":null,"authors":[],"abstract":"Many neural systems display cascading behavior characterized by uninterrupted\nsequences of neuronal firing. This gap precludes an understanding of how\nvariations in network structure manifest in neural dynamics and either support\nor impinge upon information processing. Here, we develop a theoretical\nunderstanding of how network structure supports information processing through\nnetwork dynamics, and we validate our theory with empirical data. Using a\ngeneralized spiking model and mathematical tools from linear systems theory,\nnetwork control theory, and information theory, we show how network structure\ncan be designed to temporally extend the propagation and recovery of certain\nstimulus patterns. Moreover, we observe cycles as structural and dynamic motifs\nthat are prevalent in such networks. Broadly, our results demonstrate how\ncascading neural networks could contribute to cognitive faculties that require\nlasting activation of neuronal patterns, such as working memory or attention.","url_abs":"http://arxiv.org/abs/1812.09361v2","url_pdf":"http://arxiv.org/pdf/1812.09361v2.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-structure-of-cascading-neural-systems","repo_url":"https://github.com/harangju/cascades","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}