Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.

ABSTRACT: Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as machine-challenging tasks (MCTs) and aimed to investigate methods to enhance machine learning for MCTs. Specifically, we start with a natural question: Can a learning mechanism that mimics the human brain lead to the improvement of MCT performances? We hypothesized that a learning mechanism replicating the human brain is effective for tasks where mach

SUBMITTER: Lee J 

PROVIDER: S-EPMC9709416 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature