Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.
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| Title: | Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity. |
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| Authors: | Leite S; CINTESIS - Center for Health Technology and Services Research, Porto, Portugal.; Dare Association, Inc. Boston, Massachusetts, United States of America., Mota B; Laboratory of Experimental Mathematics and Theoretical Biology, Physics Institute, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil., Silva AR; Department of Mechanical Engineering, Faculty of Engineering University of Porto, Porto, Portugal.; INEGI Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal., Commons ML; Dare Association, Inc. Boston, Massachusetts, United States of America.; Beth Israel Deaconess Medical Center, Harvard Medical School, Cambridge, Massachusetts, United States of America., Miller PM; Dare Association, Inc. Boston, Massachusetts, United States of America.; Department of Psychology, Salem State University, Salem, Massachusetts, United States of America., Rodrigues PP; CINTESIS - Center for Health Technology and Services Research, Porto, Portugal. |
| Source: | PloS one [PLoS One] 2023 Aug 31; Vol. 18 (8), pp. e0290743. Date of Electronic Publication: 2023 Aug 31 (Print Publication: 2023). |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0290743 |
