Ruogu Fang

This paper introduces EmoFB, a deep neural network model that simulates emotional perception by integrating top-down feedback signals, showing improved recognition and alignment with human brain responses.

This paper introduces a biologically inspired deep neural network model for visual emotion processing and shows it accurately predicts human emotion ratings and aligns with amygdala activity.

This study compares emotional responses to AI-generated and naturalistic affective pictures, finding that AI pictures elicit similar self-reported and physiological responses to original stimuli.

Concept2Brain generates synthetic electrophysiological responses to text and pictures using a deep learning model, enabling reproducible EEG datasets and AI-driven brain activity modeling.

DeepDynaForecast uses phylogenetic trees and graph deep learning to predict epidemic transmission dynamics, showing accuracy in simulated and real HIV data.

This study shows that deep neural networks trained on visual object recognition develop emotion-selective neurons, supporting the idea that the visual system can intrinsically represent affective significance.

The paper introduces DeepDynaTree, a phylogenetic-informed graph deep learning model to classify dynamic transmission clusters in infectious disease epidemics, achieving higher accuracy than existing methods.