About NABI

Natural and Artificial Brain Intelligence

Based in Seoul National University, College of Medicine

NABI is a research group which aims to translate clinical experience into insights through computational neuroscience. We also aim to leverage real-life data to advance our understanding of the human brain. Ultimately, we aim to develop computational models that can be leveraged in clinical or other real-life applications.

Focused on the latest topics in deep learning, computational neuroscience, and reinforcement learning

We analyze papers and implement code on the latest topics from prominent journals and conferences in deep learning, computational neuroscience, and reinforcement learning.

A multidisciplinary community

Students from various fields, including medicine, biology, and computer science, come together to develop interdisciplinary skills. We hold an end-of-year symposium and a presentation at Seoul National University Hospital so various members can present their research and hold discussions with peers from various fields.

Research Highlights

Key Papers reviewed at NeuroAI

Groundbreaking studies at the intersection of neuroscience and artificial intelligence

Could a neuroscientist understand a microprocessor?

Neuroscience techniques, when applied to a microprocessor demonstrate even human-designed systems may be difficult to understand.

Meta-reinforcement learning via orbitofrontal cortex

The hypothesis that the OFC (orbitofrontal cortex) plays a role in meta-reinforcement learning is proposed, and two distinct algorithms with distinct timescales are proposed.

Transformer as a hippocampal memory model

Based on NMDAR nonlinearity, a circuit model of memory consolidation is proposed, establishing a link between the hippocampus and the transformer architecture.

Prefrontal cortex as a meta-reinforcement learning system

The prefrontal cortex operates as a flexible free-standing system through a process of meta-reinforcement learning.

Human cognition and neuromodulatory integration

By identifying the signatures of system-wide neuromodulatory integration, we advance our knowledge the functional organization of the human brain.

Scaffolding cooperation with deep RL

A novel AI-driven social planner dynamically reshapes human interaction networks to promote cooperation by welcoming defectors into collaborative groups rather than isolating them.

A recurrent planning model explains hippocampal replay

A recurrent neural network model demonstrates how the prefrontal cortex orchestrates planning by sampling imagined action sequences, termed rollouts.

Brain-wide behavior representations in C. elegans

Whole-brain calcium imaging and probabilistic modeling is used to map how distinct neuron classes in C. elegans encode behavior across various states and timescales.

Emergence of syntax in a cerebellar circuit

A biologically-constrained artificial neural network modeled after cerebellar circuity demonstrates a single recurrent circuit can perform word prediction and syntactic structure recognition

Read more about our research

View all posts »

Research notes from our monthly meetings

Memory in Context (Kickoff Session)

Memory in Context (Kickoff Session)

The topic of this year's NABI symposium, we explore how hippocampal circuits reveal the principles behind flexible, context-dependent memory.