Haiguang Wen

Variational Autoencoder: An Unsupervised Model for Modeling and Decoding fMRI Activity in Visual Cortex

This paper uses a variational autoencoder to model and decode fMRI activity in the visual cortex, finding lower prediction accuracy compared to CNNs but successful decoding of latent variables.

Task-Evoked Functional Connectivity Does Not Explain Functional Connectivity Differences Between Rest and Task Conditions

The paper investigates how task-evoked functional connectivity differs from resting-state connectivity and finds that spontaneous networks, not task-evoked ones, mainly explain these differences.

Deep Recurrent Neural Network Reveals a Hierarchy of Process Memory during Dynamic Natural Vision

This paper introduces a deep recurrent neural network to model process memory in visual processing and shows it better predicts cortical responses to natural movies than a standard CNN.

Musical Imagery Involves the Wernicke’s Area in Bilateral and Anti-Correlated Network Interactions in Musicians

This study investigates how musicians process musical imagery compared to actual music perception, finding distinct network interactions involving Wernicke’s area.

Deep Residual Network Reveals a Nested Hierarchy of Distributed Cortical Representation for Visual Categorization

The paper uses a deep residual network to map cortical representations of 64,000 visual objects, revealing a nested hierarchy organized by category specificity and spatial scale.

Transferring and Generalizing Deep-Learning-based Neural Encoding Models across Subjects

This study develops methods to transfer and generalize deep-learning-based encoding models across subjects using Bayesian inference and incremental data training, applied to fMRI data from naturalistic video watching.