13 papers
Neural Data Needs Semantic Tokenization: Behavioral Events as Boundaries of Session-Transferable Tokens
Sangyoon Bae, Jiook Cha
Extracellular electrophysiology records a different set of neurons in every session. Neural foundation models embed each neuron and each session into their tokens, so every new ses…
BAT-CLIP: Trimodal Alignment of Brain, Audio and Text
Suhyun Kim, Jinmo Han, Danny Dongyeop Han +9
Decoding and interpreting naturalistic speech from the brain increasingly relies on alignment to pretrained speech and language representation spaces. However, current CLIP-style b…
Image Classification on IBM Quantum Computers
Junghoon Justin Park, Jiook Cha, Jun-gyeong Park +2
Quantum machine learning on real noisy intermediate-scale quantum (NISQ) hardware has remained largely confined to binary or few-class tasks, limited by the cost of on-hardware tra…
PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
Sangyoon Bae, Shinjae Yoo, Jiook Cha
Scientific foundation models are expected to reuse representations under changes in dataset, acquisition protocol, and deployment domain, yet many sequence backbones treat scientif…
Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?
Peter Yongho Kim, Juhyeon Park, Jungwoo Park +4
Modeling long-range spatiotemporal dynamics in functional Magnetic Resonance Imaging (fMRI) remains a key challenge due to the high dimensionality of the four-dimensional signals.…
Q-DIVER: Integrated Quantum Transfer Learning and Differentiable Quantum Architecture Search with EEG Data
Junghoon Justin Park, Yeonghyeon Park, Jiook Cha
Integrating quantum circuits into deep learning pipelines remains challenging due to heuristic design limitations. We propose Q-DIVER, a hybrid framework combining a large-scale pr…
Hybrid Quantum Temporal Convolutional Networks
Junghoon Justin Park, Maria Pak, Sebin Lee +4
Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (H…
Latent-Space Causal Discovery from Indirect Neuroimaging Observations
Sangyoon Bae, Miruna Oprescu, David Keetae Park +2
Neuroimaging does not observe causal variables directly: hemodynamics and volume conduction distort signals so that statistical dependence need not reflect latent neural influence.…
DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations
Danny Dongyeop Han, Yonghyeon Gwon, Ahhyun Lucy Lee +10
Intracranial EEG (iEEG) provides direct, millisecond-scale recordings of human neural activity, but reusable representation learning is difficult because electrode layouts, anatomi…
Scalable Diffusion Transformer for Conditional 4D fMRI Synthesis
Jungwoo Seo, David Keetae Park, Shinjae Yoo +1
Generating whole-brain 4D fMRI sequences conditioned on cognitive tasks remains challenging due to the high-dimensional, heterogeneous BOLD dynamics across subjects/acquisitions an…
CausalMamba: Scalable Conditional State Space Models for Neural Causal Inference
Sangyoon Bae, Jiook Cha
We introduce CausalMamba, a scalable framework that addresses fundamental limitations in fMRI-based causal inference: the ill-posed nature of inferring neural causality from hemody…
Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining
Sangyoon Bae, Mehdi Azabou, Blake Richards +1
Neural recordings exhibit a distinctive form of heterogeneity rooted in differences in cell types, intrinsic circuit dynamics, and stochastic stimulus-response variability that goe…
Macro2Micro: A Rapid and Precise Cross-modal Magnetic Resonance Imaging Synthesis using Multi-scale Structural Brain Similarity
Sooyoung Kim, Joonwoo Kwon, Junbeom Kwon +5
The human brain is a complex system requiring both macroscopic and microscopic components for comprehensive understanding. However, mapping nonlinear relationships between these sc…