Brain
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Unravelling Complex Brain Networks with Automated 3-D Neural Mapping
-Automated 3-D brain imaging data analysis technology offers more reliable and standardized analysis of the spatial organization of complex neural circuits.-
KAIST researchers developed a new algorithm for brain imaging data analysis that enables the precise and quantitative mapping of complex neural circuits onto a standardized 3-D reference atlas.
Brain imaging data analysis is indispensable in the studies of neuroscience. However, analysis of obtained brain imaging data has been heavily dependent on manual processing, which cannot guarantee the accuracy, consistency, and reliability of the results.
Conventional brain imaging data analysis typically begins with finding a 2-D brain atlas image that is visually similar to the experimentally obtained brain image. Then, the region-of-interest (ROI) of the atlas image is matched manually with the obtained image, and the number of labeled neurons in the ROI is counted.
Such a visual matching process between experimentally obtained brain images and 2-D brain atlas images has been one of the major sources of error in brain imaging data analysis, as the process is highly subjective, sample-specific, and susceptible to human error. Manual analysis processes for brain images are also laborious, and thus studying the complete 3-D neuronal organization on a whole-brain scale is a formidable task.
To address these issues, a KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering developed new brain imaging data analysis software named 'AMaSiNe (Automated 3-D Mapping of Single Neurons)', and introduced the algorithm in the May 26 issue of Cell Reports.
AMaSiNe automatically detects the positions of single neurons from multiple brain images, and accurately maps all the data onto a common standard 3-D reference space. The algorithm allows the direct comparison of brain data from different animals by automatically matching similar features from the images, and computing the image similarity score.
This feature-based quantitative image-to-image comparison technology improves the accuracy, consistency, and reliability of analysis results using only a small number of brain slice image samples, and helps standardize brain imaging data analyses.
Unlike other existing brain imaging data analysis methods, AMaSiNe can also automatically find the alignment conditions from misaligned and distorted brain images, and draw an accurate ROI, without any cumbersome manual validation process.
AMaSiNe has been further proved to produce consistent results with brain slice images stained utilizing various methods including DAPI, Nissl, and autofluorescence.
The two co-lead authors of this study, Jun Ho Song and Woochul Choi, exploited these benefits of AMaSiNe to investigate the topographic organization of neurons that project to the primary visual area (VISp) in various ROIs, such as the dorsal lateral geniculate nucleus (LGd), which could hardly be addressed without proper calibration and standardization of the brain slice image samples.
In collaboration with Professor Seung-Hee Lee's group of the Department of Biological Science, the researchers successfully observed the 3-D topographic neural projections to the VISp from LGd, and also demonstrated that these projections could not be observed when the slicing angle was not properly corrected by AMaSiNe. The results suggest that the precise correction of a slicing angle is essential for the investigation of complex and important brain structures.
AMaSiNe is widely applicable in the studies of various brain regions and other experimental conditions. For example, in the research team’s previous study jointly conducted with Professor Yang Dan’s group at UC Berkeley, the algorithm enabled the accurate analysis of the neuronal subsets in the substantia nigra and their projections to the whole brain. Their findings were published in Science on January 24.
AMaSiNe is of great interest to many neuroscientists in Korea and abroad, and is being actively used by a number of other research groups at KAIST, MIT, Harvard, Caltech, and UC San Diego.
Professor Paik said, “Our new algorithm allows the spatial organization of complex neural circuits to be found in a standardized 3-D reference atlas on a whole-brain scale. This will bring brain imaging data analysis to a new level.”
He continued, “More in-depth insights for understanding the function of brain circuits can be achieved by facilitating more reliable and standardized analysis of the spatial organization of neural circuits in various regions of the brain.”
This work was supported by KAIST and the National Research Foundation of Korea (NRF).
Figure and Image Credit: Professor Se-Bum Paik, KAIST
Figure and Image Usage Restrictions: News organizations may use or redistribute these figures and images, with proper attribution, as part of news coverage of this paper only.
Publication:
Song, J. H., et al. (2020). Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex. Cell Reports. Volume 31, 107682. Available online at https://doi.org/10.1016/j.celrep.2020.107682
Profile:
Se-Bum Paik
Assistant Professor
sbpaik@kaist.ac.kr
http://vs.kaist.ac.kr/
VSNN Laboratory
Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.06.08 View 11929 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 9987 -
Ultrathin but Fully Packaged High-Resolution Camera
- Biologically inspired ultrathin arrayed camera captures super-resolution images. -
The unique structures of biological vision systems in nature inspired scientists to design ultracompact imaging systems. A research group led by Professor Ki-Hun Jeong have made an ultracompact camera that captures high-contrast and high-resolution images. Fully packaged with micro-optical elements such as inverted micro-lenses, multilayered pinhole arrays, and gap spacers on the image sensor, the camera boasts a total track length of 740 μm and a field of view of 73°.
Inspired by the eye structures of the paper wasp species Xenos peckii, the research team completely suppressed optical noise between micro-lenses while reducing camera thickness. The camera has successfully demonstrated high-contrast clear array images acquired from tiny micro lenses. To further enhance the image quality of the captured image, the team combined the arrayed images into one image through super-resolution imaging.
An insect’s compound eye has superior visual characteristics, such as a wide viewing angle, high motion sensitivity, and a large depth of field while maintaining a small volume of visual structure with a small focal length. Among them, the eyes of Xenos peckii and an endoparasite found on paper wasps have hundreds of photoreceptors in a single lens unlike conventional compound eyes. In particular, the eye structures of an adult Xenos peckii exhibit hundreds of photoreceptors on an individual eyelet and offer engineering inspiration for ultrathin cameras or imaging applications because they have higher visual acuity than other compound eyes.
For instance, Xenos peckii’s eye-inspired cameras provide a 50 times higher spatial resolution than those based on arthropod eyes. In addition, the effective image resolution of the Xenos peckii’s eye can be further improved using the image overlaps between neighboring eyelets. This unique structure offers higher visual resolution than other insect eyes.
The team achieved high-contrast and super-resolution imaging through a novel arrayed design of micro-optical elements comprising multilayered aperture arrays and inverted micro-lens arrays directly stacked over an image sensor. This optical component was integrated with a complementary metal oxide semiconductor image sensor.
This is first demonstration of super-resolution imaging which acquires a single integrated image with high contrast and high resolving power reconstructed from high-contrast array images. It is expected that this ultrathin arrayed camera can be applied for further developing mobile devices, advanced surveillance vehicles, and endoscopes.
Professor Jeong said, “This research has led to technological advances in imaging technology. We will continue to strive to make significant impacts on multidisciplinary research projects in the fields of microtechnology and nanotechnology, seeking inspiration from natural photonic structures.”
This work was featured in Light Science & Applications last month and was supported by the National Research Foundation (NRF) of and the Ministry of Health and Welfare (MOHW) of Korea.
Image credit: Professor Ki-Hun Jeong, KAIST
Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.
Publication:
Kisoo Kim, Kyung-Won Jang, Jae-Kwan Ryu, and Ki-Hun Jeong. (2020) “Biologically inspired ultrathin arrayed camera for high-contrast and high-resolution imaging”. Light Science & Applications. Volume 9. Article 28. Available online at https://doi.org/10.1038/s41377-020-0261-8
Profile:
Ki-Hun Jeong
Professor
kjeong@kaist.ac.kr
http://biophotonics.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
Profile:
Kisoo Kim
Ph.D. Candidate
kisoo.kim1@kaist.ac.kr
http://biophotonics.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
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2020.03.23 View 15198 -
A Single Biological Factor Predicts Distinct Cortical Organizations across Mammalian Species
-A KAIST team’s mathematical sampling model shows that retino-cortical mapping is a prime determinant in the topography of cortical organization.-
Researchers have explained how visual cortexes develop uniquely across the brains of different mammalian species. A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has identified a single biological factor, the retino-cortical mapping ratio, that predicts distinct cortical organizations across mammalian species.
This new finding has resolved a long-standing puzzle in understanding visual neuroscience regarding the origin of functional architectures in the visual cortex. The study published in Cell Reports on March 10 demonstrates that the evolutionary variation of biological parameters may induce the development of distinct functional circuits in the visual cortex, even without species-specific developmental mechanisms.
In the primary visual cortex (V1) of mammals, neural tuning to visual stimulus orientation is organized into one of two distinct topographic patterns across species. While primates have columnar orientation maps, a salt-and-pepper type organization is observed in rodents.
For decades, this sharp contrast between cortical organizations has spawned fundamental questions about the origin of functional architectures in the V1. However, it remained unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa, or simply originate from variations in biological parameters under a universal development process.
To identify a determinant predicting distinct cortical organizations, Professor Paik and his researchers Jaeson Jang and Min Song examined the exact condition that generates columnar and salt-and-pepper organizations, respectively. Next, they applied a mathematical model to investigate how the topographic information of the underlying retinal mosaics pattern could be differently mapped onto a cortical space, depending on the mapping condition.
The research team proved that the retino-cortical feedforwarding mapping ratio appeared to be correlated to the cortical organization of each species. In the model simulations, the team found that distinct cortical circuitries can arise from different V1 areas and retinal ganglion cell (RGC) mosaic sizes. The team’s mathematical sampling model shows that retino-cortical mapping is a prime determinant in the topography of cortical organization, and this prediction was confirmed by neural parameter analysis of the data from eight phylogenetically distinct mammalian species.
Furthermore, the researchers proved that the Nyquist sampling theorem explains this parametric division of cortical organization with high accuracy. They showed that a mathematical model predicts that the organization of cortical orientation tuning makes a sharp transition around the Nyquist sampling frequency, explaining why cortical organizations can be observed in either columnar or salt-and-pepper organizations, but not in intermediates between these two stages.
Professor Paik said, “Our findings make a significant impact for understanding the origin of functional architectures in the visual cortex of the brain, and will provide a broad conceptual advancement as well as advanced insights into the mechanism underlying neural development in evolutionarily divergent species.”
He continued, “We believe that our findings will be of great interest to scientists working in a wide range of fields such as neuroscience, vision science, and developmental biology.”
This work was supported by the National Research Foundation of Korea (NRF).
Image credit: Professor Se-Bum Paik, KAIST
Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.
Publication:
Jaeson Jang, Min Song, and Se-Bum Paik. (2020). Retino-cortical mapping ratio predicts columnar and salt-and-pepper organization in mammalian visual cortex. Cell Reports. Volume 30. Issue 10. pp. 3270-3279. Available online at https://doi.org/10.1016/j.celrep.2020.02.038
Profile:
Se-Bum Paik
Assistant Professor
sbpaik@kaist.ac.kr
http://vs.kaist.ac.kr/
VSNN Laboratory
Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
Profile:
Jaeson Jang
Ph.D. Candidate
jaesonjang@kaist.ac.kr
Department of Bio and Brain Engineering, KAIST
Profile:
Min Song
Ph.D. Candidate
night@kaist.ac.kr
Program of Brain and Cognitive Engineering, KAIST
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2020.03.11 View 11739 -
Professor Jong Chul Ye Appointed as Distinguished Lecturer of IEEE EMBS
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was appointed as a distinguished lecturer by the International Association of Electrical and Electronic Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS). Professor Ye was invited to deliver a lecture on his leading research on artificial intelligence (AI) technology in medical video restoration. He will serve a term of two years beginning in 2020.
IEEE EMBS's distinguished lecturer program is designed to educate researchers around the world on the latest trends and technology in biomedical engineering. Sponsored by IEEE, its members can attend lectures on the distinguished professor's research subject.
Professor Ye said, "We are at a time where the importance of AI in medical imaging is increasing.” He added, “I am proud to be appointed as a distinguished lecturer of the IEEE EMBS in recognition of my contributions to this field.”
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2020.02.27 View 8258 -
Cancer cell reversion may offer a new approach to colorectal cancer treatment
A novel approach to reverse the progression of healthy cells to malignant ones may offer a more effective way to eradicate colorectal cancer cells with far fewer side effects, according to a team of researchers based in South Korea.
Colorectal cancer, or cancer of the colon, is the third most common cancer in men and the second most common in women worldwide. South Korea has the second highest incident rate of colorectal cancer in the world, topped only by Hungary, according to the World Cancer Research Fund.
Their results were published as a featured cover article on January 2 in Molecular Cancer Research, a journal of the American Association for Cancer Research.
Led by Kwang-Hyun Cho, a professor and associate vice president of research at KAIST , the researchers used a computational framework to analyze healthy colon cells and colorectal cancer cells. They found that some master regulator proteins involved in cellular replication helped healthy colon cells mature, or differentiate into their specific cell type, and remain healthy. One particular protein, called SETDB1, suppressed the helpful proteins, forcing new cells to remain in a state of immaturity with the potential to become cancerous.
“This suggests that differentiated cells have an inherent resistance mechanism against malignant transformation and indicates that cellular reprogramming is indispensable for malignancy,” said Cho. “We speculated that malignant properties might be eradicated if the tissue-specific gene expression is reinstated — if we repress SETDB1 and allow the colon cells to mature and differentiate as they would normally.”
Image credit: Kwang-Hyun Cho, KAIST
Image restriction: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.
Using human-derived cells, Cho and his team targeted the tissue-specific gene expression programs identified in their computational analysis. These are the blueprints for the proteins that eventually help immature cells differentiate into tissue-specific cell types, such as colon cells. When a person has a genetic mutation, or has exposure to certain environmental factors, this process can go awry, leading to an overexpression of unhelpful proteins, such as SEDTB1.
The researchers specifically reduced the amount of SEDTB1 in these tissue-specific gene expression programs, which allowed the cells to mature and fully differentiate into colon cells.
“Our experiment also shows that SETDB1 depletion combined with cytotoxic drugs might be potentially beneficial to anticancer treatment,” Cho said. Cytotoxic drugs are often used for cancer treatment because the type of medicine contains chemicals that are toxic to cancer cells which can prevent them from replicating or growing. He noted that this combination could be more effective in treating cancer by transforming the cancer cell state into a less malignant or resistant state. He eventually pursues a cancer reversion therapy alone instead of conventional cytotoxic drug therapy since the cancer reversion therapy can provide a much less painful experience for patients with cancer who often have severe side effects from treatments intended to kill off cancerous cells, such as chemotherapy.
The researchers plan to continue studying how to return cancer cells to healthier states, with the ultimate goal of translating their work to therapeutic treatment for patients with colorectal cancer.
“I think our study of cancer reversion would eventually change the current medical practice of treating cancer toward the direction of keeping the patient’s quality of life while minimizing the side effects of current anti-cancer therapies,” Cho said.
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This work was funded by KAIST and the National Research Foundation of Korea grants funded by the Korean government, the Ministry of Science and Information and Communication Technology.
Other authors include Soobeom Lee, Chae Young Hwang and Dongsan Kim, all of whom are affiliated with the Laboratory for Systems Biology and Bio-Inspired Engineering in the Department of Bio and Brain Engineering at KAIST; Chansu Lee and Sung Noh Hong, both with the Department of Medicine, and Seok-Hyung Kim of the Department of Pathology in the Samsung Medical Center at the Sungkyunkwan University School of Medicine.
-Profile
Professor Kwang-Hyun Cho
ckh@kaist.ac.kr
http://sbie.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
https://www.kaist.ac.kr
2020.01.31 View 5869 -
New Insights into How the Human Brain Solves Complex Decision-Making Problems
A new study on meta reinforcement learning algorithms helps us understand how the human brain learns to adapt to complexity and uncertainty when learning and making decisions. A research team, led by Professor Sang Wan Lee at KAIST jointly with John O’Doherty at Caltech, succeeded in discovering both a computational and neural mechanism for human meta reinforcement learning, opening up the possibility of porting key elements of human intelligence into artificial intelligence algorithms. This study provides a glimpse into how it might ultimately use computational models to reverse engineer human reinforcement learning.
This work was published on Dec 16, 2019 in the journal Nature Communications. The title of the paper is “Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning.”
Human reinforcement learning is an inherently complex and dynamic process, involving goal setting, strategy choice, action selection, strategy modification, cognitive resource allocation etc. This a very challenging problem for humans to solve owing to the rapidly changing and multifaced environment in which humans have to operate. To make matters worse, humans often need to often rapidly make important decisions even before getting the opportunity to collect a lot of information, unlike the case when using deep learning methods to model learning and decision-making in artificial intelligence applications.
In order to solve this problem, the research team used a technique called 'reinforcement learning theory-based experiment design' to optimize the three variables of the two-stage Markov decision task - goal, task complexity, and task uncertainty. This experimental design technique allowed the team not only to control confounding factors, but also to create a situation similar to that which occurs in actual human problem solving.
Secondly, the team used a technique called ‘model-based neuroimaging analysis.’ Based on the acquired behavior and fMRI data, more than 100 different types of meta reinforcement learning algorithms were pitted against each other to find a computational model that can explain both behavioral and neural data. Thirdly, for the sake of a more rigorous verification, the team applied an analytical method called ‘parameter recovery analysis,’ which involves high-precision behavioral profiling of both human subjects and computational models.
In this way, the team was able to accurately identify a computational model of meta reinforcement learning, ensuring not only that the model’s apparent behavior is similar to that of humans, but also that the model solves the problem in the same way as humans do.
The team found that people tended to increase planning-based reinforcement learning (called model-based control), in response to increasing task complexity. However, they resorted to a simpler, more resource efficient strategy called model-free control, when both uncertainty and task complexity were high. This suggests that both the task uncertainty and the task complexity interact during the meta control of reinforcement learning. Computational fMRI analyses revealed that task complexity interacts with neural representations of the reliability of the learning strategies in the inferior prefrontal cortex.
These findings significantly advance understanding of the nature of the computations being implemented in the inferior prefrontal cortex during meta reinforcement learning as well as providing insight into the more general question of how the brain resolves uncertainty and complexity in a dynamically changing environment. Identifying the key computational variables that drive prefrontal meta reinforcement learning, can also inform understanding of how this process might be vulnerable to break down in certain psychiatric disorders such as depression and OCD. Furthermore, gaining a computational understanding of how this process can sometimes lead to increased model-free control, can provide insights into how under some situations task performance might break down under conditions of high cognitive load.
Professor Lee said, “This study will be of enormous interest to researchers in both the artificial intelligence and human/computer interaction fields since this holds significant potential for applying core insights gleaned into how human intelligence works with AI algorithms.”
This work was funded by the National Institute on Drug Abuse, the National Research Foundation of Korea, the Ministry of Science and ICT, Samsung Research Funding Center of Samsung Electronics.
Figure 1 (modified from the figures of the original paper doi:10.1038/s41467-019-13632-1). Computations implemented in the inferior prefrontal cortex during meta reinforcement learning. (A) Computational model of human prefrontal meta reinforcement learning (left) and the brain areas whose neural activity patterns are explained by the latent variables of the model. (B) Examples of behavioral profiles. Shown on the left is choice bias for different goal types and on the right is choice optimality for task complexity and uncertainty. (C) Parameter recoverability analysis. Compared are the effect of task uncertainty (left) and task complexity (right) on choice optimality.
-Profile
Professor Sang Wan Lee
sangwan@kaist.ac.kr
Department of Bio and Brain Engineering
Director, KAIST Center for Neuroscience-inspired AI
KAIST Institute for Artificial Intelligence (http://aibrain.kaist.ac.kr)
KAIST Institute for Health, Science, and Technology
KAIST (https://www.kaist.ac.kr)
2020.01.31 View 5262 -
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging.
As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE.
He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA.
Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
2019.12.18 View 7874 -
Brain-inspired Artificial Intelligence in Robots
(from left: PhD candidate Su Jin An, Dr. Jee Hang Lee and Professor Sang Wan Lee)
Research groups in KAIST, the University of Cambridge, Japan’s National Institute for Information and Communications Technology, and Google DeepMind argue that our understanding of how humans make intelligent decisions has now reached a critical point in which robot intelligence can be significantly enhanced by mimicking strategies that the human brain uses when we make decisions in our everyday lives.
In our rapidly changing world, both humans and autonomous robots constantly need to learn and adapt to new environments. But the difference is that humans are capable of making decisions according to the unique situations, whereas robots still rely on predetermined data to make decisions.
Despite the rapid progress being made in strengthening the physical capability of robots, their central control systems, which govern how robots decide what to do at any one time, are still inferior to those of humans. In particular, they often rely on pre-programmed instructions to direct their behavior, and lack the hallmark of human behavior, that is, the flexibility and capacity to quickly learn and adapt.
Applying neuroscience in robotics, Professor Sang Wan Lee from the Department of Bio and Brain Engineering, KAIST and Professor Ben Seymour from the University of Cambridge and Japan’s National Institute for Information and Communications Technology proposed a case in which robots should be designed based on the principles of the human brain. They argue that robot intelligence can be significantly enhanced by mimicking strategies that the human brain uses during decision-making processes in everyday life.
The problem with importing human-like intelligence into robots has always been a difficult task without knowing the computational principles for how the human brain makes decisions –in other words, how to translate brain activity into computer code for the robots’ ‘brains’.
However, researchers now argue that, following a series of recent discoveries in the field of computational neuroscience, there is enough of this code to effectively write it into robots. One of the examples discovered is the human brain’s ‘meta-controller’, a mechanism by which the brain decides how to switch between different subsystems to carry out complex tasks. Another example is the human pain system, which allows them to protect themselves in potentially hazardous environments. “Copying the brain’s code for these could greatly enhance the flexibility, efficiency, and safety of robots,” Professor Lee said.
The team argued that this inter-disciplinary approach will provide just as many benefits to neuroscience as to robotics. The recent explosion of interest in what lies behind psychiatric disorders such as anxiety, depression, and addiction has given rise to a set of sophisticated theories that are complex and difficult to test without some sort of advanced situation platform.
Professor Seymour explained, “We need a way of modelling the human brain to find how it interacts with the world in real-life to test whether and how different abnormalities in these models give rise to certain disorders. For instance, if we could reproduce anxiety behavior or obsessive-compulsive disorder in a robot, we could then predict what we need to do to treat it in humans.”
The team expects that producing robot models of different psychiatric disorders, in a similar way to how researchers use animal models now, will become a key future technology in clinical research.
The team also stated that there may also be other benefits to humans and intelligent robots learning, acting, and behaving in the same way. In future societies in which humans and robots live and work amongst each other, the ability to cooperate and empathize with robots might be much greater if we feel they think like us.
Professor Seymour said, “We might think that having robots with the human traits of being a bit impulsive or overcautious would be a detriment, but these traits are an unavoidable by-product of human-like intelligence. And it turns out that this is helping us to understand human behavior as human.”
The framework for achieving this brain-inspired artificial intelligence was published in two journals, Science Robotics (10.1126/scirobotics.aav2975) on January 16 and Current Opinion in Behavioral Sciences (10.1016/j.cobeha.2018.12.012) on February 6, 2019.
Figure 1. Overview of neuroscience - robotics approach for decision-making. The figure details key areas for interdisciplinary study (Current Opinion in Behavioral Sciences)
Figure 2. Brain-inspired solutions to robot learning. Neuroscientific views on various aspects of learning and cognition converge and create a new idea called prefrontal metacontrol, which can inspire researchers to design learning agents that can address various key challenges in robotics such as performance-efficiency-speed, cooperation-competition, and exploration-exploitation trade-offs (Science Robotics)
2019.02.20 View 5483 -
Professor Je-Kyun Park, Awarded by The Korean BioChip Society
On November 9, Je-Kyun Park from the Department of Bio and Brain Engineering at KAIST received an award from the 2017 Fall Meeting of The Korean BioChip Society held in Paradise Hotel Busan, Korea. This year’s meeting recognized Professor Park for developing lab-on-a-chip and microfluidic analytical technologies.
The Korean BioChip Society is a corporation of biochip professional established in 2006 for the development of biochip technology. Every year, the Society selects a recipient based on the nominees’ academic achievements and contributions to bio-fusion industry.
Professor Park served on the international editorial boards of renowned international journals in related fields, including Biosensors and Bioelectronics and Lab on a Chip. He was also the Committee Chairman of MicroTas in 2015.
2017.11.22 View 6814 -
Dr. Hyundoo Hwang Receives a Tenured Position at Monterrey Institute of Technology and Higher Education
Hyundoo Hwang, a former graduate student in the Department of Bio & Brain Engineering at KAIST, has been granted a tenured position at the Monterrey Institute of Technology and Higher Education (ITSEM), Mexico.
Dr. Hwang received his bachelor’s, master’s, and doctoral degree at KAIST and started his professorship at Ulsan National Institute of Science & Technology (UNIST) in Korea. He continued his research in the United States as a professor at Georgia Institute of Technology. He has been acknowledged for the development of an advanced nanotechnology for the diagnosis of rare diseases and research in cell signals. He is one of the leading researchers in an international research project in microelectromechanical systems (MEMS) with participation by researchers from over ten countries. He has been active in commercializing biosensor technology in the U.S. and Mexico.
Since its establishment in 1943, ITSEM has grown to 33 campuses in 25 cities in Mexico. It is the largest university in Latin America with over 90,000 students (47% of its graduate students has oversea research experience). It recruits over 5,000 international students and professors every year.
Dr. Hwang will begin teaching at ITSEM as a professor in the Department of Biomedical Engineering (Ingeniería Biomédica) this fall. He will also conduct research in nano- and micro-technology as a member of Sensors and Devices research group.
Professor Gwang Hyun Cho, head of KAIST's Department of Bio and Brain Engineering said that Dr. Hwang’s tenure professorship at ITSEM demonstrated that the academic program at KAIST—from undergraduate to doctoral—was on par with the international standard. He hoped that more talents from the department would seek academic careers in internationally renowned universities around the world.
2015.08.13 View 5796 -
Professor Kwang-Hyun Cho Recognzied by "Scientist of the Month" Award
Professor Kwang-Hyun Cho of KAIST’s Department of Bio and Brain Engineering received the “Scientist of the Month” award in February 2015 from the Ministry of Science, ICT, and Future Planning of the Republic of Korea and the National Research Foundation of Korea. The award was in recognition of Professor Cho’s contribution to the advanced technique of controlling the death of cancer cells based on systems biology, a convergence research in information technology (IT) and biotechnology.
Professor Cho has published around 140 articles in international journals, including 34 papers in renowned science journals such as Nature, Science, and Cell in the past three years. His work also includes systems biology textbooks and many entries in international academic encyclopaedia.
His field, systems biology, is a new biological research paradigm that identifies and controls the fundamental principles of organisms on a systems level.
A well-known tumour suppressor protein, p53, is known to suppress abnormal cell growth and promote apoptosis of can cells, and thus was a focus of research by many scientists, but its effect has been insignificant and brought many side effects. This was due to the complex function of p53 that controls various positive and negative feedbacks. Therefore, there was a limit to understanding the protein with the existing biological approach.
However, Professor Cho found the kinetic change and function of p53 via a systems biology approach. By applying IT technology to complex biological networks, he also identified the response to stress and the survival and death signal transduction pathways of cardiomyocytes and developed new control methods for cancer cells.
Professor Cho said, “This award served as a momentum to turn over a new leaf.” He added, “I hope convergence research such as my field will bring more innovative ideas on the boundaries of academia.”
2015.02.09 View 11476