NIAI Lab @ Hanyang

Neuro-inspired AI group

Our group is interested in understanding principles that underlie learning and inference in the brain and machines, mainly by using tools from machine learning and statistics.

Publications

Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
Yoon, K.
In review (2025) [pdf]
Neuroscience Point Process GNN Attractor Net Memory

A Multimodal Approach to Alzheimer’s Diagnosis: Geometric Insights from Cube Copying and Cognitive Assessments
Hwang, U., Yoon, K.
In preparation (2025) [pdf]
GNN Medical AI Neuroscience

Osteoporosis Prediction from Hand X-ray Images Using Segmentation-for-Classification and Self-Supervised Learning
Hwang, U., Lee, C., Yoon, K.
In review (2024) [pdf]
SSL Medical AI Optimal Transport U-Net

Hierarchical joint graph learning and multivariate time series forecasting
Kim, J., Lee, H., Yu, S., Hwang, U., Jung, W., Park, M., Yoon, K.
NeurIPS Workshop on Temporal Graph Learning (2023) [pdf]
GNN Time-series Attention

Towards better generalization with flexible representation of multi-module graph neural networks
Lee, H., Yoon, K.
Transactions on Machine Learning Research (TMLR) (2023) [pdf]
GNN Meta Learning MoE Generalization

Graph neural networks for connectivity inference in spatially patterned neural responses
Park, T., Kim, J., Kang, D., Yoon, K.
NeurIPS Workshop on Symmetry and Geometry in Neural Representations (2022) [pdf],[poster]
GNN Neuroscience Time-series

Two-argument activation functions learn soft XOR operations like cortical neurons
Kim, J., Orhan, E., Yoon, K.*, Pitkow, X.*
IEEE Access (2022) [pdf]
XOR Neuroscience Generalization

Inference in probabilistic graphical models by graph neural networks
Yoon, K., Liao, R., Xiong, Y., Zhang, L., Fetaya, E., Urtasun, R., Zemel, R., Pitkow, X.
ICML Workshop on Tractable Probabilistic Modeling (2019) [pdf]
ICLR Workshop (2018) [pdf]
PGM GNN Generalization

Reviving and improving recurrent backpropagation
Liao, R., Xiong, Y., Fetaya, E., Zhang, L., Yoon, K., Pitkow, X., Urtasun, R., Zemel, R.
ICML (2018) [pdf]
PGM GNN Hopfield Net RBP

A map-like micro-organization of grid cells in the medial entorhinal cortex
Gu, Y., Lewallen, S., Kinkhabwala, A. A., Domnisoru, C., Yoon, K., Gauthier, J., Fiete, I. R., Tank, D. W.
Cell (2018) [pdf]
Navigation Attractor Net Module VR Neuroscience

Grid cell responses in 1D environments assessed as slices through a 2D lattice
Yoon, K.*, Lewallen, S.*, Kinkhabwala, A. A., Tank, D. W., Fiete, I. R.
Neuron (2016) [pdf],[SI]
Navigation Attractor Net Memory VR Neuroscience

Specific evidence of low-dimensional continuous attractor dynamics in grid cells
Yoon, K., Buice, M. A., Barry, C., Hayman, R., Burgess, N., Fiete, I. R.
Nature Neuroscience (2013)
[pdf],[SI],[F1000]
Navigation Attractor Net Memory Neuroscience

Conference/Workshop

Lee, H., Hwang, U., Yu, S., Lee, C., Yoon, K. “Osteoporosis prediction from hand and wrist x-rays using image segmentation and self-supervised learning”, Machine Learning for Health (ML4H) (2023) [pdf]
Park, T., Yoon, K. “Connectivity inference of biological neural networks through systematic perturbations”, Korea Computer Congress, Jeju, South Korea (2022)
Kim, J., Yoon, K. “Time-series forecasting with graph structure learning”, Korea Computer Congress, Jeju, South Korea (2022)
Kang, D., Yoon, K. “Cortical surface segmentation based on graph neural networks using non- spherical spatial coordinates”, Korea Computer Congress, Jeju, South Korea (2022)
Yoon, K., Orhan, E., Kim, J., Pitkow X. “Two-argument activation functions learn soft XOR operations like cortical neurons”, CRS21–Dialogues on Neural and Machine Intelligence, Lisbon, Portugal (2021) [poster]
Park, H., Yoon, K. “Degree matters: assessing the generalization of graph neural network”, IEEE IC-NIDC, Beijing, China (2021)