Decoding the Mind

for Humanity

& Discovery

Who we are

HINEC is a world-leading neuroscience research institute advancing our understanding of the human brain through innovative technologies and groundbreaking discoveries.

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What drives us

We are driven by the pursuit of understanding — a future where neurological disorders are treatable and the mysteries of consciousness are unlocked.

Our NeuroVision™ platform achieves:
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Neural Signal Accuracy

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Synaptic Mapping

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Processing Speed

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BCI Efficiency

Our Mission

Understanding

the Human Mind

We hold the capacity and responsibility to transform neuroscience for the betterment of humanity. We advance cognitive understanding through innovation and discovery.

Platform

High-Order Integrated

Neural Connections

Our computational platform models the brain as simplicial complexes and hypergraphs, capturing synergistic neural assemblies invisible to traditional pairwise connectivity analysis.

TOPOLOGY

Simplicial Complex Mapping

Model brain connectivity as high-dimensional simplicial complexes, capturing synergistic interactions beyond pairwise edges.

INFORMATION THEORY

O-Information Quantification

Distinguish synergy-dominated from redundancy-dominated neural assemblies using information-theoretic metrics on GPU-accelerated pipelines.

SIGNAL PROCESSING

Hypergraph Signal Processing

Tensor-based hypergraph analysis enabling multi-region co-fluctuation detection across entire cortical networks in real time.

DEEP LEARNING

Directed Semi-Simplicial Networks

State-of-the-art deep learning architectures that decode brain activity by processing directed high-order motifs up to tetrahedrons.

k > 4
Interaction Order
200+
Brain Regions
GPU
JAX-Accelerated
Real-time
Signal Processing
Publications

Latest Research

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Feb 2026

Topological Turning Points in Lifespan Brain Connectivity Reveal Five Critical Transition Stages

S. Chun, W. Choi, T. Han, M. Koilybay et al.

PERSISTENT HOMOLOGY
Nature Neuroscience
Jan 2026

Synergistic Neural Assemblies in Frontoparietal Networks Encode Higher-Order Cognitive States

W. Choi, S. Chun, M. Koilybay

SYNERGY / REDUNDANCY
Science
Dec 2025

Directed Semi-Simplicial Neural Networks for Brain State Classification from EEG Signals

T. Han, M. Koilybay, S. Chun

DEEP LEARNING
NeurIPS 2025
Nov 2025

Hypergraph Signal Processing Reveals Multi-Region Co-Fluctuation Patterns in Resting-State fMRI

M. Koilybay, T. Han, W. Choi, S. Chun

HYPERGRAPH ANALYSIS
Cell Reports
Oct 2025

O-Information Quantification at Scale: GPU-Accelerated Higher-Order Interaction Detection in Whole-Brain Connectomes

S. Chun, W. Choi, T. Han

COMPUTATIONAL METHODS
Nature Methods
Sep 2025

Global Constraints Oriented Multi-Resolution Learning of Brain Structure from Raw Neural Recordings

W. Choi, M. Koilybay, S. Chun

NEURAL ARCHITECTURE
PNAS