Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity.

ABSTRACT: Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the co

SUBMITTER: Leite S 

PROVIDER: S-EPMC10470958 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature