Search Results for author:
Found 13 papers, 3 papers with code
ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments
Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments.
Medical Image Fusion for High-Level Analysis: A Mutual Enhancement Framework for Unaligned PAT and MRI
Specifically, a cross-modal style transfer network is introduced to simplify cross-modal registration to single-modal registration.
Suppressing seizure via optimal electrical stimulation to the hub of epileptic brain network
The electrical stimulation on the hub of the epileptic brain network shows remarkable performance as the direct stimulation of SOZ in suppressing seizure dynamics.
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction
Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks.
Controlling network-coupled neural dynamics with nonlinear network control theory
This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics.
Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes
The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions.
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography
Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue.
Mapping effective connectivity by virtually perturbing a surrogate brain
Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain.
Network analysis on cortical morphometry in first-episode schizophrenia
Our work supports that cortical morphological connectivity, which is constructed based on correlations across subjects' cortical thickness, may serve as a tool to study topological abnormalities in neurological disorders.
Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
The results show that STpGCN significantly improves brain decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions.
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr).
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies.
