Rojin Ziaei

Papers

Total Citations

113

H-Index

About

Rojin Ziaei is an emerging researcher at the intersection of neuroscience and artificial intelligence, with a focus on brain-inspired computational learning. Her work critically examines the fundamental differences between biological neural systems and artificial neural networks (ANNs), exploring how principles drawn from neuroscience can advance machine learning methodologies. Ziaei's most significant contribution is her comprehensive review, "Brain-Inspired Learning in Artificial Neural Networks," which has rapidly accumulated over 108 citations since its 2024 publication — a remarkable testament to its relevance and impact within the field. The work systematically surveys how biological learning mechanisms, such as those observed in the human brain, can inform and improve the design of ANNs, spanning applications from image and speech generation to robotics and game playing. This review has quickly established itself as a key reference for researchers seeking to bridge the gap between cognitive neuroscience and deep learning. Though early in her career, Ziaei's research addresses one of the most compelling open questions in AI development — how to make artificial systems learn more like biological ones — positioning her as a promising voice in neurally-inspired machine intelligence.