Based in Seoul National University, College of Medicine
We aim to develop computational models that can be leveraged in clinical and real-life applications.
About NABI
Natural and Artificial Brain Intelligence
NABI is a multidisciplinary community translating clinical experience into computational neuroscience insights.
Focused on current NeuroAI topics
We analyze papers and implement code in deep learning, computational neuroscience, and reinforcement learning.
A multidisciplinary community
Students from medicine, biology, and computer science collaborate and present yearly research.
Research Highlights
Key Papers reviewed at NeuroAI
Groundbreaking studies at the intersection of neuroscience and artificial intelligence
Paper 1
Could a neuroscientist understand a microprocessor?
A classic demonstration that existing neuroscience tools can struggle even on engineered systems.
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Paper 2
Transformer as a hippocampal memory model
Linking hippocampal computation and transformer-like architectures for memory consolidation.
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Paper 3
Meta-reinforcement learning via orbitofrontal cortex
The hypothesis that the OFC plays a role in meta-reinforcement learning is proposed, with two algorithms at distinct timescales.
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Read more about our research
View all posts →Research notes from our monthly meetings
Integration and Competition Between Space and Time in the Hippocampus
Uncovering the trade-off between space and time in the hippocampus: how single neurons competitively balance these dimensions.
Hippocampus as a Generative Circuit for Predictive Coding
This review explores how theta oscillations guide the spontaneous emergence of ring attractors in the hippocampus.
Explaining Modular Representations with Range
An ICLR 2025 paper proposing that range, not just statistical independence, is the key factor that determines whether neural representations are modular or mixed.
Time Encoding in the Lateral Entorhinal Cortex
A presentation of the 2025 Science paper shows how the Lateral Entorhinal Cortex (LEC) encodes time via two mechanisms. A continuous 'slow drift' and abrupt 'shifts' at event boundaries.
