Nikita Frolov

Saratov State University, Innopolis University

Papers

Total Citations

H-Index

About

Nikita Frolov is a computational neuroscientist whose work bridges artificial intelligence and brain network analysis. His primary research focuses on applying artificial neural networks (ANNs) to decode complex neural dynamics, with a particular emphasis on understanding human cognitive states and large-scale brain connectivity. Frolov’s most influential contribution is his pioneering study demonstrating that ANNs can detect human uncertainty from neural data, a paper that has garnered 78 citations and opened new avenues for linking machine learning with cognitive neuroscience. In this work, he showed that multilayer perceptrons could classify brain activation patterns associated with decision-making under ambiguity, offering a powerful tool for probing subjective mental states. He further extended this approach to map functional connectivity within the thalamo-cortical network, revealing structural motifs that underlie sensory and motor integration. Though his 2019 paper on this topic has fewer citations, it represents a technically rigorous step toward understanding how distributed brain regions communicate. Frolov’s research is notable for its methodological innovation—using ANNs not just as classifiers but as instruments to reveal hidden structure in neural data. His work stands at the intersection of nonlinear dynamics, neurophysiology, and machine learning, making him a key figure in the growing field of AI-driven neuroscience.