Synaptic plasticity-based regularizer for artificial neural networks.

ABSTRACT: Regularization is an important tool for the generalization of ANN models. Due to the lack of constraints, it cannot guarantee that the model will work in a real environment with input data distribution changes. Inspired by neuroplasticity, this paper introduces a bounded regularization method that can be safely applied during the deployment phase. First, the reliability of neuron outputs is improved by extending our recent neuronal masking method to generate new supporting neurons. The model is then regularized by incorporating a synaptic connection module containing conenctions of the generated neurons to their previous layer. These connections are optimized online by introducing a synaptic rewiring process triggered by the information about the input distribution. This process is formula

SUBMITTER: Yousef Q 

PROVIDER: S-EPMC12022306 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature