TY - JOUR T1 - Brain Serotonergic Fibers Suggest Anomalous Diffusion-Based Dropout in Artificial Neural Networks JF - bioRxiv DO - 10.1101/2022.05.22.492968 SP - 2022.05.22.492968 AU - Christian Lee AU - Zheng Zhang AU - Skirmantas Janušonis Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/05/24/2022.05.22.492968.abstract N2 - Random dropout has become a standard regularization technique in artificial neural networks (ANNs), but it is currently unknown whether an analogous mechanism exists in biological neural networks (BioNNs). If it does, its structure is likely to be optimized by hundreds of millions of years of evolution, which may suggest novel dropout strategies in large-scale ANNs. We propose that the brain serotonergic fibers meet some of the expected criteria because of their ubiquitous presence, stochastic structure, and ability to grow throughout the individual’s lifespan. Since the trajectories of serotonergic fibers can be modeled as paths of anomalous diffusion processes, in this proof-of-concept study we investigated a dropout algorithm based on the superdiffusive fractional Brownian motion (FBM). This research contributes to biologically-inspired regularization in ANNs.Competing Interest StatementThe authors have declared no competing interest. ER -