{"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/biological-mechanisms-for-learning-a","title":"Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets","arxiv_id":"1802.02678","date":"2018-02-08","proceeding":null,"authors":["Charles B. Delahunt","Jeffrey A. Riffell","J. Nathan Kutz"],"abstract":"The insect olfactory system, which includes the antennal lobe (AL), mushroom\nbody (MB), and ancillary structures, is a relatively simple neural system\ncapable of learning. Its structural features, which are widespread in\nbiological neural systems, process olfactory stimuli through a cascade of\nnetworks where large dimension shifts occur from stage to stage and where\nsparsity and randomness play a critical role in coding. Learning is partly\nenabled by a neuromodulatory reward mechanism of octopamine stimulation of the\nAL, whose increased activity induces rewiring of the MB through Hebbian\nplasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that\nare the most important for the representation of the learned odor. Based upon\ncurrent biophysical knowledge, we have constructed an end-to-end computational\nmodel of the Manduca sexta moth olfactory system which includes the interaction\nof the AL and MB under octopamine stimulation. Our model is able to robustly\nlearn new odors, and our simulations of integrate-and-fire neurons match the\nstatistical features of in-vivo firing rate data. From a biological\nperspective, the model provides a valuable tool for examining the role of\nneuromodulators, like octopamine, in learning, and gives insight into critical\ninteractions between sparsity, Hebbian growth, and stimulation during learning.\nOur simulations also inform predictions about structural details of the\nolfactory system that are not currently well-characterized. From a machine\nlearning perspective, the model yields bio-inspired mechanisms that are\npotentially useful in constructing neural nets for rapid learning from very few\nsamples. These mechanisms include high-noise layers, sparse layers as noise\nfilters, and a biologically-plausible optimization method to train the network\nbased on octopamine stimulation, sparse layers, and Hebbian growth.","url_abs":"http://arxiv.org/abs/1802.02678v1","url_pdf":"http://arxiv.org/pdf/1802.02678v1.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":"biological-mechanisms-for-learning-a","repo_url":"https://github.com/charlesDelahunt/SmartAsABug","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}