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Secret to Drug Addiction Relapse Found: Brain's Addiction Circuit Identified
<(From Left) Dr. Minju Jeong,(UCSD), Prof. Byung Kook Lim (UCSD), Prof. Se-Bum Paik (KAIST)>
Drug addiction carries an extremely high risk of relapse, as cravings can be reignited by minor stimuli even long after one has stopped using. Previously, this phenomenon was attributed to a decline in the function of the prefrontal cortex (PFC), which regulates impulses. However, a joint international research team has recently revealed that the cause of addiction relapse is not a simple decline in brain function, but rather an imbalance in specific neural circuits.
KAIST announced on March 9th that a research team led by Prof. Se-Bum Paik from the Department of Brain and Cognitive Sciences and Prof. Byung Kook Lim from the University of California, San Diego (UCSD) has identified the core principle by which specific inhibitory neurons in the prefrontal cortex regulate cocaine-seeking behavior.
In particular, the research team focused on parvalbumin-positive (PV) inhibitory neurons, which regulate the balance of neural signals by suppressing the activity of other neurons in the brain. They confirmed that these cells act as a "brake gate" that controls excitatory signals in the brain and serve as a crucial factor in determining drug-seeking behavior that emerges after withdrawal.
The prefrontal cortex (PFC) of our brain can properly perform its "braking" function to suppress impulses when excitatory and inhibitory signals are in balance. To investigate how chronic drug exposure disrupts this balance, the research team conducted cocaine administration experiments on mice. During this process, they tracked when inhibitory neurons in the PFC were activated and how they sent signals to downstream brain regions.
The experimental results showed that parvalbumin (PV) cells, which account for about 60-70% of the inhibitory neurons in the PFC, were highly active when the mice attempted to seek cocaine. However, when "extinction training"—training to stop seeking the drug—was conducted, the activity of these cells significantly decreased. This demonstrates that the activity patterns of PV cells are not permanently fixed by addiction but can be readjusted through the extinction process.
<Figure 1. Experimental design illustrating cocaine self-administration and longitudinal tracking of prefrontal cortical neural activity during cocaine-seeking behavior>
The research team confirmed that artificially suppressing PV cell activity significantly reduced cocaine-seeking behavior in mice. Conversely, activating these cells caused the drug-seeking behavior to persist even after the extinction process. This effect was specifically observed in drug-addiction behavior and did not appear with general rewards like sugar water. Furthermore, this phenomenon was not observed in somatostatin (SOM) cells—another type of inhibitory neuron—indicating that PV cells selectively regulate drug addiction behavior.
<Figure 2. Comparison of single-neuron activity, population activity patterns, and behavioral modulation of prefrontal inhibitory neurons across different stages of cocaine-seeking behavior>
The team also identified the specific brain circuit through which these PV cells operate. Signals originating from the prefrontal cortex are transmitted to the reward circuit of the Ventral Tegmental Area (VTA), a key brain region related to reward. This pathway emerged as the central channel for regulating addiction behavior, determining whether or not to seek the drug again. In this process, PV neurons act as a "regulatory switch," controlling the flow of signals to influence dopamine signaling and deciding whether to maintain or suppress addictive behavior.
In short, the study revealed that addiction relapse is not due to an overall functional decline of the prefrontal cortex, but is determined by whether PV neurons regulate the neural pathway connecting the PFC to the reward circuit.
<Figure 3. Schematic illustrating the prefrontal–reward circuit mechanism that determines drug-seeking behavior>
Prof. Se-Bum Paik stated, "This research shows that drug addiction is a circuit-level problem arising from a collapse in the regulatory balance of specific neurons and downstream neural circuits. The discovery that parvalbumin (PV) cells act as a 'gate' for addictive behavior will provide a crucial lead for developing precision-targeted treatment strategies in the future."
This study was led by Dr. Minju Jeong (UCSD) as the first author, with Prof. Byung Kook Lim (UCSD) and Prof. Se-Bum Paik (KAIST) serving as co-corresponding authors. The findings were published online on February 26 in Neuron, a premier journal in the field of neuroscience.
Paper Title: Distinct Interneuronal Dynamics Selectively Gate Target-Specific Cortical Projections in Drug Seeking
DOI: 10.1016/j.neuron.2026.01.002
Full Author List: Minju Jeong, Seungdae Baek, Qingdi Wang, Li Yao, Eun Ji Lee, Arturo Marroquin Rivera, Joann Jocelynn Lee, Hyeonseok Jang, Dhananjay Bambah-Mukku, Christine Hyun-Seung Mun, Tyler Boesen, Sumit Nanda, Cheol Ryong Ku, Hong-wei Dong, Benoit Labonté, Se-Bum Paik, and Byung Kook Lim.
This research was conducted with the support of the Basic Research Program in Science and Engineering of the National Research Foundation of Korea.
2026.03.10 View 1104 -
KAIST Develops mRNA Platform That Remains Effective Even in Aging and Obesity
<(From Left) Dr. Subin Yoon, Ph.D candidate Hyeonggon Cho, Prof. Jae-Hwan Nam, Prof. Young-suk Lee>
Since the COVID-19 pandemic, mRNA vaccines have gained attention as a next-generation pharmaceutical technology. mRNA therapeutics work by delivering genetic instructions that enable cells to produce specific proteins for therapeutic effects. However, their efficacy has been reported to decline in elderly individuals or patients with obesity. To address this limitation, Korean researchers have newly designed a key regulatory region of mRNA that improves therapeutic protein production efficiency, developing a next-generation mRNA platform that maintains effectiveness even in aging and obesity conditions.
KAIST (President Kwang Hyung Lee) announced on the 10th of March that a joint research team led by Professor Young-suk Lee of the Department of Bio and Brain Engineering and Professor Jae-Hwan Nam of The Catholic University of Korea (President Jun-Gyu Choi) has developed a new mRNA platform by precisely designing the sequence of the 5′ untranslated region (5′UTR)*, a key regulatory region of mRNA.*5′ untranslated region (5′UTR): A region of mRNA that initiates and regulates protein production. The design of this region influences both the amount and speed of protein synthesis.
The research team analyzed large-scale bioinformatics datasets to identify 5′UTR sequences that enable proteins to be produced more efficiently across diverse cellular environments. When applied, the designed sequences significantly enhanced protein production and immune responses even in preclinical models of aging and obesity.
mRNA is a long single-stranded RNA molecule that serves as the blueprint for producing proteins required by the body. It consists of several components: the 5′UTR, which initiates and regulates the rate of protein production; the coding sequence (CDS), which contains the genetic information for a specific protein; the 3′ untranslated region (3′UTR), which helps maintain mRNA stability within cells; and the poly(A) tail, which further enhances stability and supports protein synthesis.
Among these components, the 5′UTR and 3′UTR do not determine the type of protein produced, but they play a critical role in regulating how efficiently the protein is synthesized. For this reason, these regions are receiving increasing attention as key bioengineering platforms for improving the performance of various mRNA therapeutics, including vaccines and treatments.
<Schematic Diagram of mRNA Therapeutic Design and Validation Using Bioinformatics>
To identify highly efficient 5′UTR sequences capable of promoting protein production across multiple tissues and cellular environments, the team conducted an integrated analysis of large-scale biological datasets. This included multiple analytical approaches such as RNA sequencing (RNA-seq) for analyzing gene activity across tissues, single-cell RNA sequencing (scRNA-seq) for examining gene expression at the individual cell level, and ribosome profiling (Ribo-seq) for measuring actual protein translation efficiency.
The researchers also focused on the fact that in aging or obesity conditions, cells often experience high levels of stress—particularly oxidative stress—which can reduce their ability to synthesize proteins. When the newly designed mRNA therapeutics were applied to preclinical models of aging and obesity, the results showed significantly improved protein production and immune responses compared with existing approaches. This research is expected to be applicable not only to mRNA vaccines but also to a wide range of biopharmaceutical technologies, including gene therapies and immunotherapies.
<Multimodal Bio–Big Data Analysis–Based mRNA Therapeutic Design (AI-Generated Image)>
Professor Young-suk Lee of KAIST Department of Bio and Brain Engineering stated, “This study identified a design strategy that enables mRNA to produce proteins more efficiently by analyzing large-scale biological data,” adding, “This technology will provide an important foundation for ensuring that mRNA vaccines and therapeutics remain effective even in environments where drug efficacy may decline, such as in elderly or obese patients.”
In this study, Dr. Subin Yoon from The Catholic University of Korea and doctoral candidate Hyeonggon Cho from KAIST participated as co-first authors. The research findings were published online on January 2 in the internationally renowned journal Molecular Therapy (IF = 12.0), a leading journal in gene and cell therapy.
(Paper title: ”Designing 5′UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity” DOI: https://doi.org/10.1016/j.ymthe.2025.12.060)
This research was supported by the Excellent Young Researcher Program and the Bio-Medical Technology Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, the Infectious Disease Response Innovative Technology Support Program of the Ministry of Food and Drug Safety, and the Infectious Disease Prevention and Therapeutics Technology Development Program of the Korea Health Industry Development Institute.
2026.03.10 View 1213 -
KAIST Develops Brain-Like AI… Thinks One More Time Even When Predictions Are Wrong
<(From left) Professor Sang Wan Lee, Myoung Hoon Ha, and Dr. Yoondo Sung>
Artificial intelligence now plays Go, paints pictures, and even converses like a human. However, there remains a decisive difference: AI requires far more electricity than the human brain to operate. Scientists have long asked the question, “How can the brain learn so intelligently using so little energy?” KAIST researchers have moved one step closer to the answer.
KAIST (President Kwang Hyung Lee) announced on the 29th that a research team led by Distinguished Professor Sang Wan Lee of the Department of Brain and Cognitive Sciences has developed a new technology that applies the learning principles of the human brain to deep learning, enabling stable training even in deep artificial intelligence models.
Our brain does not passively receive the world. Instead of merely perceiving what is happening in the present, it first predicts what will happen next and, when reality differs from that prediction, adjusts itself to reduce the difference (i.e., prediction error). This is similar to anticipating an opponent’s next move in Go and changing strategy if the prediction turns out to be wrong. This mode of information processing is known as “Predictive Coding.”
< Predictive Coding (PC) Module >
Scientists have attempted to apply this principle to AI, but encountered difficulties. As neural networks become deeper, errors tend to concentrate in specific layers or vanish altogether, repeatedly leading to performance degradation.
The research team mathematically identified the cause of this problem and proposed a new solution. The key idea is simple: instead of predicting only the final outcome, the AI is designed to also predict how its prediction errors will change in the future. The team refers to this as “Meta Prediction.” In simple terms, it is an AI that “thinks once more about its mistakes.” When this method was applied, learning proceeded stably in deep neural networks without halting.
<Analysis of Instability in Predictive Coding Model Errors>
The experimental results were also impressive. In 29 out of 30 experiments, the proposed method achieved higher accuracy than the current standard AI training method, backpropagation. Backpropagation is the representative learning method in which AI “goes backward by the amount of error and corrects it.”
Conventional AI training methods (backpropagation) require tightly interconnected layers, meaning the entire network must be computed and updated simultaneously. In contrast, this new approach demonstrates that, like the brain, large AI models can be effectively trained even when learning occurs in a distributed and partially independent manner.
<Performance Comparison of Predictive Coding Models>
This technology is expected to expand into various fields where power efficiency is critical, including neuromorphic computing, robot AI that must adapt to changing environments, and edge AI operating within devices.
Distinguished Professor Sang Wan Lee stated, “The key to this research is not simply imitating the structure of the brain, but enabling AI to follow the brain’s learning principles themselves,” adding, “We have opened the possibility of artificial intelligence that learns efficiently like the brain.”
This study was conducted with Dr. Myoung Hoon Ha as the first author and Professor Sang Wan Lee as the corresponding author. The paper was accepted to the International Conference on Learning Representations (ICLR 2026) and was published online on January 26.
※ Paper title: “Stable and Scalable Deep Predictive Coding Networks with Meta Prediction Errors”Original paper: https://openreview.net/forum?id=kE5jJUHl9i¬eId=e6T5T9cYqO
This research was supported by the Ministry of Science and ICT and the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the Digital Global Research Support Program (joint research with Microsoft Research), the Samsung Electronics SAIT NPRC Program, and the SW Star Lab Program.
2026.03.06 View 1591 -
KAIST-Yonsei Team Identifies Origin Cells for Malignant Brain Tumor Common in Young Adults
<Dr. Jung Won Park, (Upper Right) Professor Jeong Ho Lee, Professor Seok-Gu Kang>
IDH-mutant glioma, caused by abnormalities in a specific gene (IDH), is the most common malignant brain tumor among young adults under the age of 50. It is a refractory brain cancer that is difficult to treat due to its high recurrence rate. Until now, treatment has focused primarily on removing the visible tumor mass. However, a Korean research team has discovered for the first time that normal brain cells acquire the initial IDH mutation and spread out through the cortex long before a visible tumor mass harboring additional cancer mutations forms, opening a new path for early diagnosis and treatment to suppress recurrence.
KAIST announced on January 9th that a joint research team led by Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering and Professor Seok-Gu Kang from the Department of Neurosurgery at Yonsei University Severance Hospital has identified that IDH-mutant gliomas originate from Glial Progenitor Cells (GPCs) present in normal brain tissue.
Glial Progenitor Cells (GPC): Cells that exist in the normal brain and can become the starting point for malignant brain tumors if genetic mutations occur.
Through precise analysis of tumor tissue obtained via extensive resection surgery and the surrounding normal cerebral cortex, the research team discovered that "cells of origin" harboring the IDH mutation already existed within brain tissue that appeared normal to the naked eye.
< Brain-Derived Refractory Brain Tumor Origin Cells (AI-Generated Image) >
This result proves for the first time that malignant brain tumors do not emerge suddenly at a specific point in time, but rather begin within a normal brain and progress slowly over a long period.
The research team then used "spatial transcriptomics"—a cutting-edge analysis technology that shows "which genes are operating where" simultaneously—to confirm that these origin cells with mutations were indeed Glial Progenitor Cells (GPCs) located in the cerebral cortex.
Furthermore, they successfully reproduced the process of brain tumor development in an animal model by introducing the same genetic "driver mutation" found in patients into the GPCs of mice.
This study is a significant expansion of previous research identifying the "origin" of IDH wildtype malignant brain tumors. In 2018, the joint research team led a paradigm shift in brain tumor research by revealing that IDH wildtype glioblastoma, a representative malignant brain tumor, originates not from the tumor body itself, but from neural stem cells in the subventricular zone—the source of new brain cells in the adult brain (Lee et al., Nature, 2018).
The current study clarifies that even though "IDH wildtype glioblastoma" and "IDH-mutant glioma" are both types of brain cancer, their starting cells and points of origin are entirely different, proving that different types of brain tumors have fundamentally different developmental processes.
< Mechanistic Elucidation of Malignant Brain Tumor Development Induced by IDH Mutations and Subsequent Genetic Alterations in Normal Cortical Glial Progenitor Cells >
Professor Seok-Gu Kang (Co-Corresponding Author) stated, "Brain tumors may not start exactly where the tumor mass is visible. A target approach focused on the origin cells and the site of origin according to the brain tumor subtype will serve as a crucial clue to changing the paradigm of early diagnosis and recurrence suppression treatment."
Based on these research results, Sovagen Co., Ltd, a faculty startup from KAIST, is developing an innovative RNA-based drug to suppress the evolution and recurrence of IDH-mutant malignant brain tumors. Additionally, Severance Hospital is pursuing the development of technologies to detect and control early mutant cells in refractory brain tumors through the Korea-US Innovative Result Creation R&D project.
Dr. Jung Won Park (Postdoctoral Researcher at KAIST Graduate School of Medical Science and Engineering), a neurosurgeon and the sole first author of the study, said, "This achievement was made possible by combining KAIST’s world-class basic science research capabilities with the clinical expertise of Yonsei Severance Hospital. The question I kept asking while treating patients—'Where does this tumor originate?'—was the starting point of this research."
The findings were published on January 8th in the world-renowned academic journal Science.
Paper Title: IDH-mutant gliomas arise from glial progenitor cells harboring the initial driver mutation
DOI: 10.1126/science.adt0559
Authors: Jung Won Park (KAIST, First Author), Seok-Gu Kang (Yonsei Severance Hospital, Corresponding Author), Jeong Ho Lee (KAIST, Sovagen, Corresponding Author)
This research was conducted with support from the Suh Kyung-bae Science Foundation, the National Research Foundation of Korea, the Ministry of Science and ICT, the Ministry of Health and Welfare, and the Korea Health Industry Development Institute (Physician-Scientist Training Program).
2026.01.09 View 1753 -
KAIST Awakens dormant immune cells inside tumors to attack cancer
<(From Left) Professor Ji-Ho Park, Dr. Jun-Hee Han from the Department of Bio and Brain Engineering>
Within tumors in the human body, there are immune cells (macrophages) capable of fighting cancer, but they have been unable to perform their roles properly due to suppression by the tumor. KAIST researchers have overcome this limitation by developing a new therapeutic approach that directly converts immune cells inside tumors into anticancer cell therapies.
KAIST (President Kwang Hyung Lee) announced on the 30th that a research team led by Professor Ji-Ho Park of the Department of Bio and Brain Engineering has developed a therapy in which, when a drug is injected directly into a tumor, macrophages already present in the body absorb it, produce CAR (a cancer-recognizing device) proteins on their own, and are converted into anticancer immune cells known as “CAR-macrophages.”
Solid tumors—such as gastric, lung, and liver cancers—grow as dense masses, making it difficult for immune cells to infiltrate tumors or maintain their function. As a result, the effectiveness of existing immune cell therapies has been limited.
CAR-macrophages, which have recently attracted attention as a next-generation immunotherapy, have the advantage of directly engulfing cancer cells while simultaneously activating surrounding immune cells to amplify anticancer responses.
However, conventional CAR-macrophage therapies require immune cells to be extracted from a patient’s blood, followed by cell culture and genetic modification. This process is time-consuming, costly, and has limited feasibility for real-world patient applications.
To address this challenge, the research team focused on “tumor-associated macrophages” that are already accumulated around tumors.
They developed a strategy to directly reprogram immune cells in the body by loading lipid nanoparticles—designed to be readily absorbed by macrophages—with both mRNA encoding cancer-recognition information and an immunostimulant that activates immune responses.
In other words, in this study, CAR-macrophages were created by “directly converting the body’s own macrophages into anticancer cell therapies inside the body.”
<Figure . Schematic illustration of the strategy for in vivo CAR-macrophage generation and cancer cell eradication via co-delivery of CAR mRNA and immunostimulants using lipid nanoparticles (LNPs)>
When this therapeutic agent was injected into tumors, macrophages rapidly absorbed it and began producing proteins that recognize cancer cells, while immune signaling was simultaneously activated. As a result, the generated “enhanced CAR-macrophages” showed markedly improved cancer cell–killing ability and activated surrounding immune cells, producing a powerful anticancer effect.
In animal models of melanoma (the most dangerous form of skin cancer), tumor growth was significantly suppressed, and the therapeutic effect was shown to have the potential to extend beyond the local tumor site to induce systemic immune responses.
Professor Ji-Ho Park stated, “This study presents a new concept of immune cell therapy that generates anticancer immune cells directly inside the patient’s body,” adding that “it is particularly meaningful in that it simultaneously overcomes the key limitations of existing CAR-macrophage therapies—delivery efficiency and the immunosuppressive tumor environment.”
This research was led by Jun-Hee Han, Ph.D., of the Department of Bio and Brain Engineering at KAIST as the first author, and the results were published on November 18 in ACS Nano, an international journal in the field of nanotechnology.
※ Paper title: “In Situ Chimeric Antigen Receptor Macrophage Therapy via Co-Delivery of mRNA and Immunostimulant,” Authors: Jun-Hee Han (first author), Erinn Fagan, Kyunghwan Yeom, Ji-Ho Park (corresponding author), DOI: 10.1021/acsnano.5c09138
This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea.
2025.12.31 View 3384 -
Presenting a Brain-Like Next-Generation AI Semiconductor that Sees and Judges Instantly
< (From left) Professor Sanghun Jeon, Ph.D candidate Seungyeob Kim, Postdoctoral researcher Hongrae Cho, Ph.D candidates Sang-ho Lee and Taeseung Jung, and M.S candidate Seonjae Park >
With the advancement of Artificial Intelligence (AI), the importance of ultra-low-power semiconductor technology that integrates sensing, computation, and memory into a single unit is growing. However, conventional structures face challenges such as power loss due to data movement, latency, and limitations in memory reliability. A Korean research team has drawn international academic attention by presenting core technologies for an integrated ‘Sensor–Compute–Store’ AI semiconductor to solve these issues.
KAIST announced on December 31st that Professor Sanghun Jeon’s research team from the School of Electrical Engineering presented a total of six papers at the ‘International Electron Devices Meeting (IEEE IEDM 2025)’—the world’s most prestigious semiconductor conference—held in San Francisco from December 8 to 10. Among these, the papers were simultaneously selected as a Highlight Paper and a Top Ranked Student Paper.
Highlight Paper: Monolithically Integrated Photodiode–Spiking Circuit for Neuromorphic Vision with In-Sensor Feature Extraction [Link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=255]
Top Ranked Student Paper: A Highly Reliable Ferroelectric NAND Cell with Ultra-thin IGZO Charge Trap Layer; Trap Profile Engineering for Endurance and Retention Improvement [Link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=124]
The research on the M3D integrated neuromorphic vision sensor, selected as a highlight paper, is a semiconductor that stacks the human eye and brain within a single chip. Simply put, the sensors that detect light and the circuits that process signals like a brain are made into very thin layers and stacked vertically in one chip, implementing a structure where the process of 'seeing' and 'judging' occurs simultaneously.
Through this, the research team completed the world's first "In-Sensor Spiking Convolution" platform, where AI computation technology that "sees and judges at the same time" takes place directly within the camera sensor.
< Figure 1. Summary of research on vertically stacked optical signal-to-spike frequency converter for AI >
< Figure 2. Representative diagram of the development of a 2T-2C near-pixel analog computing cell based on oxide thin-film transistors >
Previously, this technology required several stages: capturing an image (sensor), converting it to digital (ADC), storing it in memory (DRAM), and then calculating (CNN). However, this new technology eliminates unnecessary data movement as the calculation happens immediately within the sensor. As a result, it has become possible to implement real-time, ultra-low-power Edge AI with significantly reduced power consumption and dramatically improved response speeds.
Based on this approach, the research team presented six core technologies at the conference covering all layers of AI semiconductors, from input to storage. They simultaneously created neuromorphic semiconductors that operate like the brain using much less electricity while utilizing existing semiconductor processes, along with next-generation memory optimized for AI.
First, on the sensor side, they designed the system so that judgment occurs at the sensor stage rather than having separate components for capturing images and calculating. Consequently, power consumption decreased and response speeds increased compared to the conventional method of taking a photo and sending it to another chip for calculation.
< Figure 3. Schematic diagram of a next-generation biomimetic tactile system using neuromorphic devices >
< Figure 4. Representative diagram of NC-NAND development research based on Ultra-thin-Mo and Sub-3.5 nm HZO >
Furthermore, in the field of memory, they implemented a next-generation NAND flash that uses the same materials but operates at lower voltages, lasts longer, and can store data stably even when the power is turned off. Through this, they presented a foundational technology that satisfies the requirements for high-capacity, high-reliability, and low-power memory necessary for AI.
< Figure 5. Representative diagram of next-generation 3D FeNAND memory development research >
< Figure 6. Representative diagram of research on charge behavior characterization and quantitative analysis methodology for next-generation FeNAND memory >
Professor Sanghun Jeon, who led the research, stated, "This research is significant in that it demonstrates that the entire hierarchy can be integrated into a single material and process system, moving away from the existing AI semiconductor structure where sensing, computation, and storage were designed separately." He added, "Moving forward, we plan to expand this into a next-generation AI semiconductor platform that encompasses everything from ultra-low-power Edge AI to large-scale AI memory."
Meanwhile, this research was conducted with support from basic research projects of the Ministry of Science and ICT and the National Research Foundation of Korea, as well as the Center for Heterogeneous Integration of Extreme-scale & Property Semiconductors (CH³IPS). It was carried out in collaboration with Samsung Electronics, Kyungpook National University, and Hanyang University.
2025.12.31 View 1462 -
Uncovering brain’s secret to stable yet flexible learning – paving the way for human-like AI
<(From Left) Professor Sang Wan Lee, Ph.D candidate Yoondo Sung, (Upper Left) Dr. Mattia Rigotti>
Humans possess a remarkable balance between stability and flexibility, enabling them to quickly establish new plans and adjust goals even in the face of sudden changes. However, "Model-Free reinforcement learning," which is widely used in robotics and exemplified by AlphaGo’s famous match against Lee Sedol, struggle to achieve these two capabilities simultaneously. KAIST's research team has discovered that the secret lies in the unique information processing method within the prefrontal cortex, a principle that could serve as the foundation for developing "Brain-like AI” that is both flexible and stable.
KAIST announced on December 14th that a research team led by Professor Sang Wan Lee from the Department of Brain and Cognitive Sciences, in collaboration with IBM AI Research, has deciphered how the human brain manages goal changes in uncertain situations, suggesting a new direction for next-generation reinforcement learning.
The research team highlighted a critical limitation of current reinforcement learning models: they lose the balance between flexibility for goals pursuit and stability in uncertain environments. Humans, however, achieve both simultaneously. The team hypothesized that this difference arises from how the prefrontal cortex represents information.
Using functional MRI (fMRI) experiments, reinforcement learning models, and advanced AI analyses, the team revealed that the human prefrontal cortex has a unique embedding structure that represents "goal information" and "uncertainty information" separately to prevent interference. Individuals with more distinct separation between these channels were able to adapt strategies when goals shifted, while maintaining stable judgment despite environmental uncertainty. The team likened this mechanism to "multiplexing" in communication technology, where multiple signals are transmitted simultaneously without interference.
In this way, the human prefrontal cortex operates through two "channels": one that sensitively tracks goal changes to ensure flexibility in decision-making, and another that isolate environmental uncertainty to maintain stable judgment.
An interesting point is that the prefrontal cortex goes beyond simple executing control guided by the first channel; it uses the second channel to actually choose which learning strategy to use depending on the situation.
This demonstrates the brain’s "meta-learning capabilities," meaning it learns not only what to learn but also how to learn – by choosing which learning strategy to use. This is why humans remain resilient in constantly changing situations.
The implication of this research extend across various fields, including the analysis of individual reinforcement and meta-learning abilities, personalized education design, cognitive diagnosis, and human-computer interaction (HCI). Moreover, embedding brain-inspired representation structures into AI could lead to "brain-like thinking AI", allowing AI to better understand human intentions and values, reducing dangerous judgments, and enabling safer cooperation with humans.
<Figure 1. Balance between Flexibility and Stability in Humans and AI>
<Figure 2. Topological Structure of Goal Representation in the Prefrontal Cortex and Environmental Uncertainty Information>
Lead researcher Professor Sang Wan Lee emphasized the significance of the findings: "This study clarifies the brain's fundamental operating principles—from flexibly following changing goals to stably establishing plans—from an AI perspective. These principles will serve as a core foundation for next-generation AI, allowing it to adapt like a human and learn more safely and intelligently."
This study featured PhD candidate Yoondo Sung as the first author and Dr. Mattia Rigotti of IBM AI Research as the second author, with Professor Sang Wan Lee serving as the corresponding author. The research results were published on November 26 in the international academic journal Nature Communications.
(Paper Title: Factorized embedding of goal and uncertainty in the lateral prefrontal cortex guides stably flexible learning / DOI: 10.1038/s41467-025-66677-w)
Notably, this research was conducted with support from the "Frontier R&D Project" of the Ministry of Science and ICT.
2025.12.15 View 768 -
Depression is Not Only a Disease of the Mind. KAIST Discovers the Immune-Brain Connection
<(From Left) Ph.D candidate Insook Ahn from KAIST, Professor Jinju Han from KAIST, (Upper Left) Yangsik Kim from Inhan University School of Medicine, Ph.D candidate Soyeon Chang(psychiatrist)>
Major depressive disorder (MDD) is characterized by a lowered mood and loss of interest, contributing not only to difficulties in academic and professional life but also as a major cause of suicide in South Korea. However, there are currently no objective biological markers that can be used for diagnosis or treatment. Amidst this, a research team from KAIST has revealed that depression is not merely a problem of the mind or brain, but is deeply connected to abnormalities in the body's overall immune response. They found that this immune abnormality affects brain function, and the 'Immune Neural Axis' imbalance is the core mechanism of depression, opening up the possibility for the discovery of new biomarkers and the development of new drugs for depression treatment.
KAIST announced on the November 20th that Professor Jinju Han's research team from the Graduate School of Medical Science and Engineering (GSMSE) at KAIST, in collaboration with Professor Yangsik Kim's research team (Ph.D., KAIST GSMSE) from Inha University School of Medicine, performed a multi-omics analysis combining plasma proteomic analysis, WBC single-cell analysis, and patient-derived brain organoids (mini-brains). This study focused on female patients with MDD who exhibited 'Atypical Features' (such as hypersomnia and overeating) and 'Psychotic Symptoms'(such as auditory hallucinations and idea of reference), which are different from typical depression symptoms, and who also had impaired reality judgment.
Sduio
■ "Immune Cells and Brain Function are Altered Together" A New Biological Clue for Depression
The research team simultaneously examined genetic changes in immune cells in the blood and changes in nervous-system-related proteins. The results confirmed a breakdown in the balance of immune-neural interaction in patients with depression.
MDD, especially in young women, often presents with atypical symptoms (hypersomnia, overeating, mood reactivity, etc.), which increases the risk of a later diagnosis of bipolar disorder. Furthermore, about 40% of patients are classified as treatment-resistant depression, showing no response to various antidepressants.
Consequently, there has been a continuous call for the development of new therapeutic strategies and the discovery of biomarkers based on immunity and metabolism, moving beyond the traditional drug-centric approach.
■ World's First Integration of "Leukocyte Single-Cell Analysis + Brain Organoid" A New Paradigm for Psychiatric Research
The research team presented the world's first precision medicine approach by integrating plasma proteomics, leukocyte single-cell transcriptome analysis, and analysis of brain organoids created from patient-derived induced pluripotent stem cells (iPSCs).
The results showed that patients with atypical depression exhibited high levels of stress, anxiety, and depression. Furthermore, proteins crucial for inter-neuronal signaling (DCLK3 and CALY) were significantly elevated compared to normal levels, and Complement Protein C5, which strongly enhances the body's immune response, was also increased. This indicates that both 'brain function' and 'immune function' are excessively activated and out of balance within the body.
This finding confirms a clue that depression is not merely a mood issue but is connected to biological changes occurring throughout the entire body. Upon examining the immune cells of depression patients, genetic changes were found that make inflammatory responses in the body occur more easily and strongly than usual. This implies that the entire bodily immune system is in a state of excessive activation, and this immune/inflammatory abnormality may influence the development of depression.
The patient-derived brain organoids showed accompanying growth retardation and abnormal neural development, supporting the possibility that immune abnormalities interact with changes in brain function to exacerbate the disease.
■ "Immune-Neural Axis Imbalance is the Core Mechanism of Atypical Depression"
This study integrated clinical data, single-cell omics, proteomics, and brain organoids to demonstrate that the 'Imbalance of the Immune-Neural Axis' is the core mechanism of MDD accompanied by atypical and psychotic symptoms.
<Integration of clinical symptoms, blood analysis, and patient-derived brain organoid analysis in women with major depressive disorder>
Professor Jinju Han stated, "This achievement presents a new precision medicine model for psychiatric research," adding, "We anticipate that this will actively lead to biomarker discovery and new drug development."
This accomplishment was published online in the world-renowned international scientific journal, Advanced Science, on October 31st.
※ Paper Title: Exploration of Novel Biomarkers through a Precision Medicine Approach Using Multi-omics and Brain Organoids in Patients with Atypical Depression and Psychotic Symptoms DOI: https://doi.org/10.1002/advs.202508383
※ Author Information: Soyeon Chang (Inha University, Co-First Author), Seok-Ho Choi, Jiyoung Lee, Yangsik Kim (Inha University, Corresponding Author), Insook Ahn (KAIST, Co-First Author), and Jinju Han (KAIST, Corresponding Author)
This research was supported by the National Research Foundation of Korea and the Korea Health Industry Development Institute.
2025.11.20 View 18982 -
AI Opens a New Era in Medical Science and Bio
< (From left) KAIST Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, Seunghoon Hong, Sang Yup Lee >
KAIST announced on the 14th of November that it has been selected as a major participating institution in the 'Lunit Consortium' for the 'AI Specialized Foundation Model Development Project' supervised by the Ministry of Science and ICT, and has officially started developing an AI foundation model for the medical science and bio fields. Through this project, KAIST plans to develop an 'AI Foundation Model Specialized for Medical Science' that encompasses the entire lifecycle of bio and medical data, and lead the creation of an AI based life science innovation ecosystem. The 'Lunit Consortium' includes 7 companies-Lunit, Trillion Labs, Kakao Healthcare, Igenscience, SK Biopharm, and Rebellion-along with 9 medical and research institutions, including KAIST, Seoul National University, NYU, National Health Insurance Service Ilsan Hospital, and Yonsei Severance Hospital. This consortium will be supported by 256 state of the art B200 GPUs to build and demonstrate a 'Chain of Evidence-Based Full-Cycle Medical Science AI Model', an AI system that connects and analyzes medical data from beginning to end, and a 'Multi-Agent Service', a system where multiple AIs collaborate to perform diagnosis and prediction. KAIST's participation in this project involves a joint research team formed by professors from the School of Computing and the Kim Jaechul Graduate School of AI. Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, and Seunghoon Hong will serve as the research team, and Vice President for Research Sang Yup Lee will take on an advisory role. The research team is not merely collecting data but they are establishing a strategy (L1~L7 stages) to precisely process and systematically manage medical and life science data so that the AI can actually learn and utilize it. Through this, they plan to develop and verify an AI model that connects and analyzes diverse life science data, including medical information, gene/protein data, and new drug candidates. The data the research team aims to integrate includes a wide range from language to actual patient treatment information. Specifically, L1 represents language data, L2 is the structure of molecules, L3 is proteins and antibodies, L4 is omics data encompassing genetic and protein information, L5 is drug information, L6 is medical science research and clinical data, and L7 is real-world clinical data obtained from actual hospitals. In essence, the data handled by the AI connects everything from speech and text to molecules, proteins, drugs, clinical research, and actual patient treatment information.
< The process of training AI by viewing X ray images and doctor's interpretation (text) together (MedViLL from Professor Jae-Yoon Choi' s lab) >
Vice President Sang Yup Lee is a world-renowned scholar in the fields of synthetic biology and systems metabolic engineering, leading the establishment of a bio manufacturing platform and policy advice through the convergence of life science, engineering, and AI. He advises on the analysis of life information (omics) such as genes and proteins and designs a feedback system for verifying experimental results, supporting the Korean-developed medical AI model to secure international reliability and competitiveness. Vice President Lee stated, "AI technology is breaking down the boundaries of life science and engineering, creating a new paradigm for knowledge creation," adding, "KAIST will utilize full cycle medical science data to accelerate the era where AI uncovers the causes of diseases and predicts treatments." KAIST President Kwang Hyung Lee said, "KAIST will contribute to creating an AI-based life science innovation ecosystem, lead the innovation of national strategic industries through world-class AI-bio convergence research, and drive the progress of human health and science and technology." The model developed in the Lunit Consortium will be released as an Open License for commercial use, and is expected to expand into various medical and healthcare services such as national health chatbots. With this participation, KAIST plans to strengthen research on AI-based life science data infrastructure establishment, medical AI standardization, and AI ethics and policy advice, leading the AI transition of national bio and medical science research.
2025.11.14 View 2550 -
City AI Research Institute Selected for Ministry of Science and ICT's Brain Pool (BP) Institutional Recruitment Program
<Professor Yoonjin Yoon from the Department of Civil and Environmental Engineering at KAIST>
KAIST's City AI Research Institute (Director: Professor Yoonjin Yoon) has been selected for the Ministry of Science and ICT's Brain Pool (BP) Institutional Recruitment Program. This achievement is the culmination of a joint proposal spearheaded by Institute Director Professor Yoonjin Yoon, along with Professor Soyoung In of the Department of Civil and Environmental Engineering and Professor Sujin Han of the School of Electrical Engineering. It is the result of high praise for the institute's research capabilities in the field of Urban AI and its potential for international collaboration.
This BP project, with a total budget of 2.1 billion KRW, will be carried out over 28 months. It plans to actively pursue AI research focused on solving urban problems by inviting renowned overseas scholars to focus on three core areas: Geospatial AI, Climate AI, and Physical AI. Through this, the institute aims to develop core AI technologies based on a collaboration system involving industry, academia, research institutions, and government. This will lead the way in sustainable urban growth and the transition to an 'Cognitive City,' continuing research to proactively diagnose and respond to various issues that citizens can experience firsthand.
This project is particularly significant as it is a female-centered institutional Brain Pool project. KAIST plans to systematically support the growth of early-career female researchers and actively expand the participation of next-generation female scientists and engineers in international research networks. This is expected to significantly contribute to the development of female research personnel and the strengthening of research leadership, areas that are relatively lacking in domestic science and engineering fields.
Furthermore, through long-term joint research with researchers from world-leading universities such as MIT, NYU, UIUC, UBC, USF, and the University of Toronto, the City AI Research Institute is set to become a leading Urban AI research hub in Korea and Asia.
Moving forward, the institute will continue to dedicate itself to core research for responding to the complex challenges of future cities and advancing innovative technology through artificial intelligence, based on global cooperation.
2025.10.10 View 2044 -
Next Generation Robots Roaming Shipyards and City Centers
< Diden Robotics Research Team Co., Ltd (Leftmost person in the front row is CEO Joon-Ha Kim)>
KAIST announced on the September 30th that domestic robot startups, founded on KAIST research achievements, are driving new innovation at shipyards and urban worksites.
An industrial walking robot that freely climbs walls and ceilings and a humanoid walking robot that walks through downtown Gangnam are attracting attention as they enter the stage of commercialization. The stars are DIDEN Robotics Co., Ltd. and Eurobotics Co., Ltd.
Diden Robotics is providing a new breakthrough in the industrial automation market, including the shipbuilding industry, by commercializing its innovative 'Seungwol (Ascend and Cross) Robot' technology, which allows it to move freely and work on steel walls and ceilings. Eurobotics is commercializing world-class humanoid walking technology, and this achievement is scheduled to be officially presented at the international humanoid robot conference, 'Humanoids 2025,' to be held on October 1st.
< Diden Robot's Outer Plate (Longi) and Welding Test >
Diden Robotics is a robotics startup jointly founded in March 2024 by four alumni from the KAIST Mechanical Engineering Hu-bo Lab DRCD research team (Professor Hae-Won Park). Its flagship product, 'DIDEN 30,' is a quadrupedal robot designed for use in high-risk work environments that are difficult for humans to access, combining autonomous driving technology, a foot-shaped leg structure, and magnetic feet.
The 'DIDEN 30' successfully completed the 'Longitudinal (longi) Overcoming Test,' in which it stepped over steel stiffeners (longitudinals) densely installed as part of the structure at a ship construction site, proving its potential for field deployment. Currently, the company is conducting research to enhance its functionality so it can stably pass through access holes, the narrow entryways inside ships. It is also pushing for performance improvements so it can be deployed for real tasks such as welding, inspection, and painting starting in the second half of 2026.
A next-generation bipedal walking robot, 'DIDEN Walker,' is also under development. Targeting the completion of a prototype in the fourth quarter of 2025, it is being designed for stable walking in cramped and complex industrial environments. Plans are also underway to equip it with an upper-body manipulator for automated welding in the shipbuilding industry.
Diden Robotics is accelerating the advancement of its proprietary 'Physical AI' technology. The core is the self-developed AI learning platform, 'DIDEN World,' which applies an offline reinforcement learning method where the AI generates optimal motion data in a virtual simulation beforehand and learns without trial and error, increasing learning efficiency and stability.
< Diden Robot (DIDEN 30) >
Furthermore, to actually implement the AI technology, the company is internalizing its hardware and advancing its 3D recognition technology, which serves as the robot's 'eyes.' It is aiming for a completely autonomous walking system that requires no worker intervention by 2026, using technology such as 3D mapping based on four cameras.
In addition to this technological development, Diden Robotics successfully performed the longitudinal overcoming, Seungwol test, and welding work on blocks under construction through a joint development with Samsung Heavy Industries in September. This is a significant achievement, meaning Diden Robotics' technology has been validated in actual industrial settings, moving beyond the laboratory level.
Meanwhile, Diden Robotics is collaborating with major domestic shipyards, including Samsung Heavy Industries, HD Hyundai Samho, Hanwha Ocean, and HD Korea Shipbuilding & Offshore Engineering, to develop site-customized robots.
Joon-Ha Kim, CEO of Diden Robotics, stated, "The successful tests at the Samsung Heavy Industries site proved the practicality and stability of our technology. We will establish ourselves as a leading company in solving labor shortages and driving automation in the shipbuilding industry."
< (Eurobotics Research Team Co., Ltd.)(Leftmost person in the top row is CEO Byung-ho Yoo) >
Eurobotics is an autonomous walking startup jointly founded by three alumni from Professor Hyun Myung's research team at KAIST. It is promoting the commercialization of autonomous walking technology for indoor and outdoor industrial sites, including rough terrain. In a recently released video, a humanoid equipped with control technology developed by Eurobotics attracted attention by walking naturally through the crowd in downtown Gangnam.
The core technology is the 'Blind Walking Controller.' It determines locomotion based only on internal information without external sensors like cameras or LiDAR, enabling stable walking regardless of day, night, or weather. The robot performs locomotion by 'imagining' the terrain without precise terrain modeling, demonstrating robust performance with the same controller across various environments such as sidewalks, downhill slopes, and stairs.
This technology originated from the quadrupedal walking competition at the 2023 International Conference on Robotics and Automation (ICRA), where Professor Myung's lab participated, and proved its world-class capability by winning, beating MIT by a large margin. At the time, Byungg-ho Yoo, CEO of Eurobotics, led the team, and Co-CTOs Min-ho Oh and Dong-kyu Lee directly participated in developing the core autonomous walking technology. Based on this, they continued further development tailored to the humanoid environment and have entered the commercialization stage.
< Eurobotics' Humanoid Walking >
Byung-ho Yoo, CEO of Eurobotics, emphasized, "This video is the first step toward complete humanoid autonomous walking. We will develop KAIST's research achievements into technologies that can be immediately utilized in industrial settings."
Hyeonmin Bae, Head of the KAIST Startup Center, said, "We will provide close support from the initial stages to help the on-campus robotics industry grow actively and assist them in settling down stably."
Kwang Hyung Lee, President of KAIST, stated, "This achievement is a representative case showing that KAIST's fundamental technologies are rapidly spreading to industrial fields through startups. KAIST will continue to actively support innovative entrepreneurship based on challenging research and help lead the global robotics industry."
※ https://2025humanoids.org https://www.seoulairobot.com/
2025.09.30 View 5703 -
KAIST Develops Semiconductor Neuron that Remembers and Responds Like the Brain
<(From left, clockwise) Professor Kyung Min Kim, Min-Gu Lee, Dae-Hee Kim, Dr. Han-Chan Song, Tae-Uk Ko, Moon-Gu Choi, and Eun-Young Kim>
The human brain does more than simply regulate synapses that exchange signals; individual neurons also process information through “intrinsic plasticity,” the adaptive ability to become more sensitive or less sensitive depending on context. Existing artificial intelligence semiconductors, however, have struggled to mimic this flexibility of the brain. A KAIST research team has now developed next-generation, ultra-low-power semiconductor technology that implements this ability as well, drawing significant attention.
KAIST (President Kwang Hyung Lee) announced on September 28 that a research team led by Professor Kyung Min Kim of the Department of Materials Science and Engineering developed a “Frequency Switching Neuristor” that mimics “intrinsic plasticity,” a property that allows neurons to remember past activity and autonomously adjust their response characteristics.
“Intrinsic plasticity” refers to the brain’s adaptive ability- for example, becoming less startled when hearing the same sound repeatedly, or responding more quickly to a specific stimulus after repeated training. The “Frequency Switching Neuristor” is an artificial neuron device that autonomously adjusts the frequency of its signals, much like how the brain becomes less startled by repeated stimuli or, conversely, increasingly sensitive through training.
The research team combined a “volatile Mott memristor,” which reacts momentarily before returning to its original state, with a “non-volatile memristor,” which remembers input signals for long periods of time. This enabled the implementation of a device that can freely control how often a neuron fires (its spiking frequency).
<Figure 1. Conceptual comparison between a neuron and a frequency-tunable neuristor. The intrinsic plasticity of brain neurons regulates excitability through ion channels. Similarly, the frequency-tunable neuristor uses a volatile Mott device to generate current spikes, while a non-volatile VCM device adjusts resistance states to realize comparable frequency modulation characteristics>
In this device, neuronal spike signals and memristor resistance changes influence each other, automatically adjusting responses. Put simply, it reproduces within a single semiconductor device how the brain becomes less startled by repeated sounds or more sensitive to repeated stimuli.
To verify the effectiveness of this technology, the researchers conducted simulations with a “sparse neural network.” They found that, through the neuron’s built-in memory function, the system achieved the same performance with 27.7% less energy consumption compared to conventional neural networks.
They also demonstrated excellent resilience: even if some neurons were damaged, intrinsic plasticity allowed the network to reorganize itself and restore performance. In other words, artificial intelligence using this technology consumes less electricity while maintaining performance, and it can compensate for partial circuit failures to resume normal operation.
Professor Kyung Min Kim, who led the research, stated, “This study implemented intrinsic plasticity, a core function of the brain, in a single semiconductor device, thereby advancing the energy efficiency and stability of AI hardware to a new level. This technology, which enables devices to remember their own state and adapt or recover even from damage, can serve as a key component in systems requiring long-term stability, such as edge computing and autonomous driving.”
This research was carried out with Dr. Woojoon Park (now at Forschungszentrum Jülich, Germany) and Dr. Hanchan Song (now at ETRI) as co-first authors, and the results were published online on August 18 in Advanced Materials (IF 26.8), a leading international journal in materials science.
※ Paper title: “Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing,” DOI: 10.1002/adma.202502255
This research was supported by the National Research Foundation of Korea and Samsung Electronics.
2025.09.30 View 3001