{"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/built-to-last-functional-and-structural","title":"Built to Last: Functional and structural mechanisms in the moth olfactory network mitigate effects of neural injury","arxiv_id":"1808.01279","date":"2020-09-11","proceeding":null,"authors":[],"abstract":"Most organisms suffer neuronal damage throughout their lives, which can\nimpair performance of core behaviors. Their neural circuits need to maintain\nfunction despite injury, which in particular requires preserving key system\noutputs. In this work, we explore whether and how certain structural and\nfunctional neuronal network motifs act as injury mitigation mechanisms.\nSpecifically, we examine how (i) Hebbian learning, (ii) high levels of noise,\nand (iii) parallel inhibitory and excitatory connections contribute to the\nrobustness of the olfactory system in the Manduca sexta moth. We simulate\ninjuries on a detailed computational model of the moth olfactory network\ncalibrated to in vivo data. The injuries are modeled on focal axonal swellings,\na ubiquitous form of axonal pathology observed in traumatic brain injuries and\nother brain disorders. Axonal swellings effectively compromise spike train\npropagation along the axon, reducing the effective neural firing rate delivered\nto downstream neurons. All three of the network motifs examined significantly\nmitigate the effects of injury on readout neurons, either by reducing injury's\nimpact on readout neuron responses or by restoring these responses to\npre-injury levels. These motifs may thus be partially explained by their value\nas adaptive mechanisms to minimize the functional effects of neural injury.\nMore generally, robustness to injury is a vital design principle to consider\nwhen analyzing neural systems.","url_abs":"http://arxiv.org/abs/1808.01279v3","url_pdf":"http://arxiv.org/pdf/1808.01279v3.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":"built-to-last-functional-and-structural","repo_url":"https://github.com/charlesDelahunt/BuiltToLast","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}