{"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/deep-learning-in-multi-layer-architectures-of","title":"Deep Learning in Multi-Layer Architectures of Dense Nuclei","arxiv_id":"1609.07160","date":"2016-09-22","proceeding":null,"authors":["Yonghua Yin","Erol Gelenbe"],"abstract":"We assume that, within the dense clusters of neurons that can be found in\nnuclei, cells may interconnect via soma-to-soma interactions, in addition to\nconventional synaptic connections. We illustrate this idea with a multi-layer\narchitecture (MLA) composed of multiple clusters of recurrent sub-networks of\nspiking Random Neural Networks (RNN) with dense soma-to-soma interactions, and\nuse this RNN-MLA architecture for deep learning. The inputs to the clusters are\nfirst normalised by adjusting the external arrival rates of spikes to each\ncluster. Then we apply this architecture to learning from multi-channel\ndatasets. Numerical results based on both images and sensor based data, show\nthe value of this novel architecture for deep learning.","url_abs":"http://arxiv.org/abs/1609.07160v2","url_pdf":"http://arxiv.org/pdf/1609.07160v2.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":"deep-learning-in-multi-layer-architectures-of","repo_url":"https://github.com/yinyongh/DenseRandomNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}