AI
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KAIST Proposes a New Way to Circumvent a Long-time Frustration in Neural Computing
The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.
KAIST (President Kwang-Hyung Lee) announced on the 23rd of October that Professor Se-Bum Paik 's research team in the Department of Brain Cognitive Sciences solved the weight transport problem*, a long-standing challenge in neural network learning, and through this, explained the principles that enable resource-efficient learning in biological brain neural networks.
*Weight transport problem: This is the biggest obstacle to the development of artificial intelligence that mimics the biological brain. It is the fundamental reason why large-scale memory and computational work are required in the learning of general artificial neural networks, unlike biological brains.
Over the past several decades, the development of artificial intelligence has been based on error backpropagation learning proposed by Geoffery Hinton, who won the Nobel Prize in Physics this year. However, error backpropagation learning was thought to be impossible in biological brains because it requires the unrealistic assumption that individual neurons must know all the connected information across multiple layers in order to calculate the error signal for learning.
< Figure 1. Illustration depicting the method of random noise training and its effects >
This difficult problem, called the weight transport problem, was raised by Francis Crick, who won the Nobel Prize in Physiology or Medicine for the discovery of the structure of DNA, after the error backpropagation learning was proposed by Hinton in 1986. Since then, it has been considered the reason why the operating principles of natural neural networks and artificial neural networks will forever be fundamentally different.
At the borderline of artificial intelligence and neuroscience, researchers including Hinton have continued to attempt to create biologically plausible models that can implement the learning principles of the brain by solving the weight transport problem.
In 2016, a joint research team from Oxford University and DeepMind in the UK first proposed the concept of error backpropagation learning being possible without weight transport, drawing attention from the academic world. However, biologically plausible error backpropagation learning without weight transport was inefficient, with slow learning speeds and low accuracy, making it difficult to apply in reality.
KAIST research team noted that the biological brain begins learning through internal spontaneous random neural activity even before experiencing external sensory experiences. To mimic this, the research team pre-trained a biologically plausible neural network without weight transport with meaningless random information (random noise).
As a result, they showed that the symmetry of the forward and backward neural cell connections of the neural network, which is an essential condition for error backpropagation learning, can be created. In other words, learning without weight transport is possible through random pre-training.
< Figure 2. Illustration depicting the meta-learning effect of random noise training >
The research team revealed that learning random information before learning actual data has the property of meta-learning, which is ‘learning how to learn.’ It was shown that neural networks that pre-learned random noise perform much faster and more accurate learning when exposed to actual data, and can achieve high learning efficiency without weight transport.
< Figure 3. Illustration depicting research on understanding the brain's operating principles through artificial neural networks >
Professor Se-Bum Paik said, “It breaks the conventional understanding of existing machine learning that only data learning is important, and provides a new perspective that focuses on the neuroscience principles of creating appropriate conditions before learning,” and added, “It is significant in that it solves important problems in artificial neural network learning through clues from developmental neuroscience, and at the same time provides insight into the brain’s learning principles through artificial neural network models.”
This study, in which Jeonghwan Cheon, a Master’s candidate of KAIST Department of Brain and Cognitive Sciences participated as the first author and Professor Sang Wan Lee of the same department as a co-author, will be presented at the 38th Neural Information Processing Systems (NeurIPS), the world's top artificial intelligence conference, to be held in Vancouver, Canada from December 10 to 15, 2024. (Paper title: Pretraining with random noise for fast and robust learning without weight transport)
This study was conducted with the support of the National Research Foundation of Korea's Basic Research Program in Science and Engineering, the Information and Communications Technology Planning and Evaluation Institute's Talent Development Program, and the KAIST Singularity Professor Program.
2024.10.23 View 995 -
A Korean research team develops a new clinical candidate for fatty liver disease
A team of Korean researchers have succeeded in developing a new drug candidate for the treatment of non-alcoholic fatty liver disease (NAFLD) acting on peripheral tissues. To date, there has not been an optimal treatment for non-alcoholic steatohepatitis (NASH), and this discovery is expected to set the grounds for the development of new drugs that can safely suppress both liver fat accumulation and liver fibrosis at the same time.
A joint research team led by Professor Jin Hee Ahn from Gwangju Institute of Science and Technology (GIST) and Professor Hail Kim from the KAIST Graduate School of Medical Science and Engineering developed a new chemical that can suppress disease-specific protein (HTR2A) through years of basic research. The team also revealed to have verified its efficacy and safety through preclinical tests (animal tests) at JD Bioscience Inc., a start-up company founded by Professor Ahn.
Although NAFLD has a prevalence rate as high as 20-30%, and about 5% of the global adult population suffers from NASH, there are no commercial drugs targeting them to date. NAFLD is a chronic disease that starts from the fatty liver and progresses into steatohepatitis, fibrosis, cirrhosis, and liver cancer. The mortality rate of patients increases with accompanied cardiovascular diseases and liver-related complications, and appropriate treatment in the early stage is hence necessary.
< Figure 1. Strategy and history of 5HT2A antagonists. Library and rational design for the development of compound 11c as a potent 5HT2A antagonist. Previous research efforts were discontinued due to limited oral absorption and safety. A therapeutic candidate to overcome this problem was identified and phase 1 clinical trials are currently in progress. >
The new synthetic chemical developed by the joint GIST-KAIST research is an innovative drug candidate that shows therapeutic effects on NASH based on a dual action mechanism that inhibits the accumulation of fat in the liver and liver fibrosis by suppressing the serotonin receptor protein 5HT2A.
The research team confirmed its therapeutic effects in animal models for NAFLD and NASH, in which hepatic steatosis and liver fibrosis* caused by fat accumulation in the liver were suppressed simultaneously by 50-70%.
*fibrosis: stiffening of parts of the liver, also used as a major indicator to track the prognosis of steatosis
The research team explained that the material was designed with optimal polarity and lipid affinity to minimize its permeability across the blood-brain barrier. It therefore does not affect the brain, and causes little side effects in the central nervous system (CNS) such as depression and suicidal ideations, while demonstrating excellent inhibition on its target protein present in tissues outside brain (IC50* = 14 nM). The team also demonstrated its superior efficacy in improving liver fibrosis when compared to similar drugs in the phase 3 clinical trial.
*IC50 (half maximal inhibitory concentration): the concentration at which a chemical suppresses 50% of a particular biological function
< Figure 2. GM-60106 (11c)'s effect on obesity: When GM-60106 was administered to an obese animal model (mice) for 2 months, body weight, body fat mass, and blood sugar were significantly reduced (a-d). In addition, the steatohepatitis level (NAFLD Activity Score) and the expression of genes of the treated mice involved in adipogenesis along with blood/liver fat decreased (e-h) >
Based on the pharmacological data obtained through preclinical trials, the team evaluated the effects of the drug on 88 healthy adults as part of their phase 1 clinical trial, where the side effects and the safe dosage of a drug are tested against healthy adults. Results showed no serious side effects and a good level of drug safety.
In addition, a preliminary efficacy evaluation on eight adults with steatohepatitis is currently underway.
Professor Jin Hee Ahn said, “The aim of this research is to develop a treatment for NASH with little side effects and guaranteed safety by developing a new target. The developed chemical is currently going through phase 1 of the global clinical trial in Australia through JD Bioscience Inc., a bio venture company for innovative drug development.” he added, “The candidate material the research team is currently developing shows not only a high level of safety and preventative effects by suppressing fat accumulation in the liver, but also a direct therapeutic effect on liver fibrosis. This is a strength that distinguishes our material from other competing drugs.”
< Figure 3. Efficacy of GM-60106 (11c) on liver fibrosis: When GM-60106 was administered to a steatohepatitis model (mice) for 3 months, the expression of genes associated with tissue fibrosis was significantly reduced (b-c). As a result of a detailed analysis of the tissues of the animal model, it was confirmed that the rate of tissue fibrosis was reduced and the expression rate of genes related to tissue fibrosis and inflammation was also significantly reduced (e-h). >
Professor Hail Kim from KAIST said, “Until now, this disease did not have a method of treatment other than weight control, and there has been no attempt to develop a drug that can be used for non-obese patients.” He added, “Through this research, we look forward to the development of various treatment techniques targeting a range of metabolic diseases including NASH that do not affect the weight of the patient.”
This study, conducted together by the research teams led by Professor Ahn from GIST and Professor Kim from KAIST, as well as the research team from JD Bioscience Inc., was supported by the Ministry of Science and ICT, and the National New Drug Development Project. The results of this research were published by Nature Communications on January 20.
The team also presented the results of their clinical study on the candidate material coded GM-60106 targeting metabolic abnormality-related MASH* at NASH-TAG Conference 2024, which was held in Utah for three days starting on January 4, which was selected as an excellent abstract.
*MASH (Metabolic Dysfunction-Associated Steatohepatitis): new replacement term for NASH
2024.02.21 View 4669 -
Face Detection in Untrained Deep Neural Networks
A KAIST team shows that primitive visual selectivity of faces can arise spontaneously in completely untrained deep neural networks
Researchers have found that higher visual cognitive functions can arise spontaneously in untrained neural networks. A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has shown that visual selectivity of facial images can arise even in completely untrained deep neural networks.
This new finding has provided revelatory insights into mechanisms underlying the development of cognitive functions in both biological and artificial neural networks, also making a significant impact on our understanding of the origin of early brain functions before sensory experiences.
The study published in Nature Communications on December 16 demonstrates that neuronal activities selective to facial images are observed in randomly initialized deep neural networks in the complete absence of learning, and that they show the characteristics of those observed in biological brains.
The ability to identify and recognize faces is a crucial function for social behavior, and this ability is thought to originate from neuronal tuning at the single or multi-neuronal level. Neurons that selectively respond to faces are observed in young animals of various species, and this raises intense debate whether face-selective neurons can arise innately in the brain or if they require visual experience.
Using a model neural network that captures properties of the ventral stream of the visual cortex, the research team found that face-selectivity can emerge spontaneously from random feedforward wirings in untrained deep neural networks. The team showed that the character of this innate face-selectivity is comparable to that observed with face-selective neurons in the brain, and that this spontaneous neuronal tuning for faces enables the network to perform face detection tasks.
These results imply a possible scenario in which the random feedforward connections that develop in early, untrained networks may be sufficient for initializing primitive visual cognitive functions.
Professor Paik said, “Our findings suggest that innate cognitive functions can emerge spontaneously from the statistical complexity embedded in the hierarchical feedforward projection circuitry, even in the complete absence of learning”.
He continued, “Our results provide a broad conceptual advance as well as advanced insight into the mechanisms underlying the development of innate functions in both biological and artificial neural networks, which may unravel the mystery of the generation and evolution of intelligence.” This work was supported by the National Research Foundation of Korea (NRF) and by the KAIST singularity research project.
-PublicationSeungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Baik, “Face detection in untrained deep neural network,” Nature Communications 12, 7328 on Dec.16, 2021
(https://doi.org/10.1038/s41467-021-27606-9)
-ProfileProfessor Se-Bum PaikVisual System and Neural Network LaboratoryProgram of Brain and Cognitive EngineeringDepartment of Bio and Brain EngineeringCollege of EngineeringKAIST
2021.12.21 View 8385 -
A Self-Made Couple in Their 90s Donates to KAIST
A self-made elderly couple in their 90s made a 20 billion KRW donation to KAIST on March 13. Chairman of Samsung Brush Sung-Hwan Chang and his wife Ha-Ok Ahn gave away their two properties valued at 20 billion in Nonhyon-dong in Seoul to KAIST during a ceremony on March 13 in Seoul.
Chairman Chang, 92, made a huge fortune starting his business manufacturing cosmetic brushes. Building two factories in China, he expanded his business to export to high-end cosmetic companies. Chairman Chang, a native of North Korea, is a refugee who fled his hometown with his sister at age 18 during the Korean War. He said remembering his mother who was left behind in North Korea was the most painful thing.
“We always wanted to help out people in need when we would earn enough money. We were inspired by our friends at our retirement community who made a donation to KAIST several years ago. We believe this is the right time to make this decision,” said Chairman Chang.
The couple lives in same retirement community, a famous place for many successful businessmen and wealthy retired figures, located in Yongin, Kyonggi-do with Chairmen Beang-Ho Kim, Chun-Shik Cho, and Chang-Keun Son. With their gift, KAIST established Kim Beang-Ho & Kim Sam-Youl ITC Building as well as the Cho Chun-Shik Graduate School of Green Transportation. The four senior couples’ donations amount to 76.1 billion KRW.
“It would be the most meaningful way if we could invest in KAIST for the country’s future,” said Chairman Chang. “I talked a lot with Chairman Kim on how KAIST utilizes its donations and have developed a strong belief in the future of KAIST.”
Chairman and Mrs. Chang already toured the campus several times at the invitation of President Kwang-Hyung Lee and President Lee himself presented the vision of KAIST to the couple. The couple also attended President Lee’s inauguration ceremony on March 8.
President Lee thanked the couple for their donation, saying “I take my hat off to Chairman Chang and his wife for their generous donation that was amassed over their lifetime. They lived very fiscally responsible lives. We will efficiently utilize this fund for educating future global talents."
(END)
2021.03.15 View 7175 -
Professor Mu-Hyun Baik Honored with the POSCO TJ Park Prize
Professor Mu-Hyun Baik at the Department of Chemistry was honored to be the recipient of the 2021 POSCO TJ Park Prize in Science. The POSCO TJ Park Foundation awards every year the individual or organization which made significant contribution in science, education, community development, philanthropy, and technology.
Professor Baik, a renowned computational chemist in analyzing complicated chemical reactions to understand how molecules behave and how they change. Professor Baik was awarded in recognition of his pioneering research in designing numerous organometallic catalysts with using computational molecular modelling. In 2016, he published in Science on the catalytic borylation of methane that showed how chemical reactions can be carried out using the natural gas methane as a substrate.
In 2020, he reported in Science that electrodes can be used as functional groups with adjustable inductive effects to change the chemical reactivity of molecules that are attached to them, closely mimicking the inductive effect of conventional functional groups. This constitutes a potentially powerful new way of controlling chemical reactions, offering an alternative to preparing derivatives to install electron-withdrawing functional groups.
Joined at KAIST in 2015, Professor Baik also serves as associate director at the Center for Catalytic Hydrocarbon Functionalization at the Institute for Basic Science (IBS) since 2015. Among the many recognitions and awards that he received include the Kavli Fellowship by the Kavli Foundation and the National Academy of Science in the US in 2019 and the 2018 Friedrich Wilhelm Bessel Award by the Alexander von Humboldt Foundation in Germany.
2021.03.11 View 7474 -
A Biological Strategy Reveals How Efficient Brain Circuitry Develops Spontaneously
- A KAIST team’s mathematical modelling shows that the topographic tiling of cortical maps originates from bottom-up projections from the periphery. -
Researchers have explained how the regularly structured topographic maps in the visual cortex of the brain could arise spontaneously to efficiently process visual information. This research provides a new framework for understanding functional architectures in the visual cortex during early developmental stages.
A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has demonstrated that the orthogonal organization of retinal mosaics in the periphery is mirrored onto the primary visual cortex and initiates the clustered topography of higher visual areas in the brain.
This new finding provides advanced insights into the mechanisms underlying a biological strategy of brain circuitry for the efficient tiling of sensory modules. The study was published in Cell Reports on January 5.
In higher mammals, the primary visual cortex is organized into various functional maps for neural tuning such as ocular dominance, orientation selectivity, and spatial frequency selectivity. Correlations between the topographies of different maps have been observed, implying their systematic organizations for the efficient tiling of sensory modules across cortical areas.
These observations have suggested that a common principle for developing individual functional maps may exist. However, it has remained unclear how such topographical organizations could arise spontaneously in the primary visual cortex of various species.
The research team found that the orthogonal organization in the primary visual cortex of the brain originates from the spatial organization in bottom-up feedforward projections. The team showed that an orthogonal relationship among sensory modules already exists in the retinal mosaics, and that this is mirrored onto the primary visual cortex to initiate the clustered topography.
By analyzing the retinal ganglion cell mosaics data in cats and monkeys, the researchers found that the structure of ON-OFF feedforward afferents is organized into a topographic tiling, analogous to the orthogonal intersection of cortical tuning maps.
Furthermore, the team’s analysis of previously published data collected on cats also showed that the ocular dominance, orientation selectivity, and spatial frequency selectivity in the primary visual cortex are correlated with the spatial profiles of the retinal inputs, implying that efficient tiling of cortical domains can originate from the regularly structured retinal patterns.
Professor Paik said, “Our study suggests that the structure of the periphery with simple feedforward wiring can provide the basis for a mechanism by which the early visual circuitry is assembled.”
He continued, “This is the first report that spatially organized retinal inputs from the periphery provide a common blueprint for multi-modal sensory modules in the visual cortex during the early developmental stages. Our findings would make a significant impact on our understanding the developmental strategy of brain circuitry for efficient sensory information processing.”
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:
Song, M, et al. (2021) Projection of orthogonal tiling from the retina to the visual cortex. Cell Reports 34, 108581. Available online at https://doi.org/10.1016/j.celrep.2020.108581
Profile:
Se-Bum Paik, Ph.D
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:
Min Song
Ph.D. Candidate
night@kaist.ac.kr
Program of Brain and Cognitive Engineering
Profile:
Jaeson Jang, Ph.D.
Researcher
jaesonjang@kaist.ac.kr
Department of Bio and Brain Engineering, KAIST
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2021.01.14 View 6904 -
Chairman Soo-Young Lee Named Among the Heroes of Philanthropy in Asia
Chairman Soo-Young Lee from the KAIST Development Foundation was named one of 15 philanthropists who made the biggest donations in the Asia-Pacific region by Forbes Asia on November 11.
The annual Heroes of Philanthropy list features the 15 the most generous individual philanthropists who are donating from their personal fortunes, not through companies. This year, the biggest philanthropies donated to make a difference in wide arrays of sectors such as Covid-19 relief to education and the arts.
Chairman Lee donated totaling 68 billion KRW to KAIST in July. Her donation marked the largest donation KAIST has ever received. She is one of two Korean philanthropists that Forbes selected. Honorary Chairman of GS Caltex Dong-Soo Huh also made the list.
Her donation will establish the Soo-Young Lee Science Education Foundation to support ‘the Singularity Professor program’ that KAIST is launching. She expressed confidence that her donation will fund KAIST researchers to make breakthroughs that will lead to a Nobel Prize.
“Without the advancement of science and technology, Korea cannot be one of the top countries in the world. I believe KAIST can make it with our all supports,” she frequently said when asked why she selected KAIST for her donation.
Chairman Lee previously made generous donations in 2012 and 2016 and said she plans to make another gift to KAIST in the very near future.
2020.11.13 View 5471 -
Before Eyes Open, They Get Ready to See
- Spontaneous retinal waves can generate long-range horizontal connectivity in visual cortex. -
A KAIST research team’s computational simulations demonstrated that the waves of spontaneous neural activity in the retinas of still-closed eyes in mammals develop long-range horizontal connections in the visual cortex during early developmental stages.
This new finding featured in the August 19 edition of Journal of Neuroscience as a cover article has resolved a long-standing puzzle for understanding visual neuroscience regarding the early organization of functional architectures in the mammalian visual cortex before eye-opening, especially the long-range horizontal connectivity known as “feature-specific” circuitry.
To prepare the animal to see when its eyes open, neural circuits in the brain’s visual system must begin developing earlier. However, the proper development of many brain regions involved in vision generally requires sensory input through the eyes.
In the primary visual cortex of the higher mammalian taxa, cortical neurons of similar functional tuning to a visual feature are linked together by long-range horizontal circuits that play a crucial role in visual information processing.
Surprisingly, these long-range horizontal connections in the primary visual cortex of higher mammals emerge before the onset of sensory experience, and the mechanism underlying this phenomenon has remained elusive.
To investigate this mechanism, a group of researchers led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering at KAIST implemented computational simulations of early visual pathways using data obtained from the retinal circuits in young animals before eye-opening, including cats, monkeys, and mice.
From these simulations, the researchers found that spontaneous waves propagating in ON and OFF retinal mosaics can initialize the wiring of long-range horizontal connections by selectively co-activating cortical neurons of similar functional tuning, whereas equivalent random activities cannot induce such organizations.
The simulations also showed that emerged long-range horizontal connections can induce the patterned cortical activities, matching the topography of underlying functional maps even in salt-and-pepper type organizations observed in rodents. This result implies that the model developed by Professor Paik and his group can provide a universal principle for the developmental mechanism of long-range horizontal connections in both higher mammals as well as rodents.
Professor Paik said, “Our model provides a deeper understanding of how the functional architectures in the visual cortex can originate from the spatial organization of the periphery, without sensory experience during early developmental periods.”
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). Undergraduate student Jinwoo Kim participated in this research project and presented the findings as the lead author as part of the Undergraduate Research Participation (URP) Program at KAIST.
Figures and image credit: Professor Se-Bum Paik, KAIST
Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only.
Publication:
Jinwoo Kim, Min Song, and Se-Bum Paik. (2020). Spontaneous retinal waves generate long-range horizontal connectivity in visual cortex. Journal of Neuroscience, Available online athttps://www.jneurosci.org/content/early/2020/07/17/JNEUROSCI.0649-20.2020
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: Jinwoo Kim
Undergraduate Student
bugkjw@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
(END)
2020.08.25 View 10347 -
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
(END)
2020.06.08 View 11940 -
Breastfeeding Helps Prevent Mothers from Developing Diabetes after Childbirth
A team of South Korean researchers found that lactation can lower the incidence and reduce the risk of maternal postpartum diabetes. The researchers identified that lactation increases the mass and function of pancreatic beta cells through serotonin production. The team suggested that sustained improvements in pancreatic beta cells, which can last for years even after the cessation of lactation, improve mothers’ metabolic health in addition to providing health benefits for infants.
Pregnancy imposes a substantial metabolic burden on women through weight gain and increased insulin resistance. Various other factors, including a history of gestational diabetes, maternal age, and obesity, further affect women’s risk of progressing to diabetes after delivery, and the risk of postpartum diabetes increases more in women who have had gestational diabetes and/or repeated deliveries.
Diabetes-related complications include damage to blood vessels, which can lead to cardiovascular and cerebrovascular diseases such as heart attack and stroke, and problems with the nerves, eyes, kidneys, and many more. Since diabetes can pose a serious threat to mothers’ metabolic health, the management of maternal metabolic risk factors is important, especially in the peripartum period. Previous epidemiological studies have reported that lactation reduces the risk of postpartum diabetes, but the mechanisms underlying this benefit have remained elusive.
The study, published in Science Translational Medicine on April 29, explains the biology underpinning this observation on the beneficial effects of lactation. Professor Hail Kim from the Graduate School of Medical Science and Engineering at KAIST led and jointly conducted the study in conjunction with researchers from the Seoul National University Bundang Hospital (SNUBH) and Chungnam National University (CNU) in Korea, and the University of California, San Francisco (UCSF) in the US.
In their study, the team observed that the milk-secreting hormone ‘prolactin’ in lactating mothers not only promotes milk production, but also plays a major role in stimulating insulin-secreting pancreatic beta cells that regulate blood glucose in the body.
The researchers also found that ‘serotonin’, known as a chemical that contributes to wellbeing and happiness, is produced in pancreatic beta cells during lactation. Serotonin in pancreatic beta cells act as an antioxidant and reduce oxidative stress, making mothers’ beta cells healthier. Serotonin also induces the proliferation of beta cells, thereby increasing the beta cell mass and helping maintain proper glucose levels.
The research team conducted follow-up examinations on a total of 174 postpartum women, 85 lactated and 99 non-lactated, at two months postpartum and annually thereafter for at least three years. The results demonstrated that mothers who had undergone lactation improved pancreatic beta cell mass and function, and showed improved glucose homeostasis with approximately 20mg/dL lower glucose levels, thereby reducing the risk of postpartum diabetes in women. Surprisingly, this beneficial effect was maintained after the cessation of lactation, for more than three years after delivery.
Professor Kim said, “We are happy to prove that lactation benefits female metabolic health by improving beta cell mass and function as well as glycemic control.”
“Our future studies on the modulation of the molecular serotonergic pathway in accordance with the management of maternal metabolic risk factors may lead to new therapeutics to help prevent mothers from developing metabolic disorders,” he added.
This work was supported by grants from the National Research Foundation (NRF) and the National Research Council of Science and Technology (NST) of Korea, the National Institutes of Health (NIH), the Larry L. Hillblom Foundation, and the Health Fellowship Foundation.
Image credit: Professor Hail Kim, 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:
Moon, J. H et al. (2020) ‘Lactation improves pancreatic β cell mass and function through serotonin production.’ Science Translational Medicine, 12, eaay0455. Available online at https://doi.org/10.1126/scitranslmed.aay0455
Profile: Hail Kim, MD, PhD
hailkim@kaist.edu
Associate Professor
Graduate School of Medical Science and Engineering (GSMSE)
Korea Advanced Institute of Science and Technology (KAIST)
Profile: Hak Chul Jang, MD, PhD
janghak@snu.ac.kr
Professor
Division of Endocrinology and Metabolism
Seoul National University Bundang Hospital (SNUBH)
President
Korean Diabetes Association
Profile: Joon Ho Moon, MD, PhD
moonjoonho@gmail.com
Clinical Fellow
Division of Endocrinology and Metabolism
SNUBH
Profile: Hyeongseok Kim, MD, PhD
hskim85kor@gmail.com
Assistant Professor
Chungnam National University (CNU)
Profile: Professor Michael S. German, MD
Michael.German@ucsf.edu
Professor
Diabetes Center
University of California, San Francisco (UCSF)
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2020.04.29 View 16188 -
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 11748 -
AI Graduate School to Take the Lead in Shaping the Future of AI
KAIST opened its AI Graduate School on August 26 with its first cohort of 22 Master’s and 10 PhD students for the 2019 fall semester. The new graduate school will provide students with a multidisciplinary curriculum incorporating the five key fields of healthcare, autonomous vehicles, manufacturing, security, and emerging technologies, and will offer 18 courses this semester.
KAIST was selected as one of the first three AI graduate schools that the Korean government will financially endorse to nurture top-tier AI specialists. The government will provide 9 billion KRW and KAIST will invest an additional 4.2 billion KRW in the school over the next five years.
KAIST aims to foster top-tiered AI engineers who will work for advancing emergent technologies for the Fourth Industrial Revolution. The school will produce original technologies by driving high-risk, innovative AI research projects and will be the main supplier of highly competent engineers who will lead the industry and advance the global market.
KAIST has a long history of AI research and has a top-level AI education and research infrastructure. In 1990, KAIST launched the first AI research center in Korea. Since then, KAIST has taken the lead in the field by making breakthroughs in intelligent sensing information systems and AI platforms. About 20 percent of the faculty members at KAIST, or about 120 professors, are conducting AI-related research while offering 136 AI-related courses.
The Dean of the AI Graduate School, Song Chong, said, “Our faculty members are the cream of the crop and are all in their early 40s. Although we started with only eight professors, we will employ 20 full-time professors by 2023 and will spare no effort to make the world’s best AI research hub and develop the brightest minds.”
Dean Chong said that three professors are already listed in the top ten when measured by the number of publications from the top two AI conferences, Neural Information Processing System (NIPS) and ICML (International Conference on Machine Learning). KAIST has several highly recognized faculty members who have published more than 10 NIPS/ICML papers over nine years, winning numerous awards including the ACM Sigmetrics Rising Star Award, Google AI Focused Research Award, and INFORMS Applied Probability Best Publication Award.
The number of students attempting to gain admission to the school is also very high. The admission office said that the percentage of applicants being offered admission stood at 9.1 percent. From next year, the school plans to increase the number of enrollments to 40 Master’s and 20 PhD students.
The school will also open the AI Graduate School Research Center in Songnam City next month and expand its collaboration with local companies in the Songnam and Pangyo region, both emerging techno and ICT valleys. With the placement of 60 research personnel in the center, the school plans to play a leading role in building the companies’ technical competitiveness.
The government’s keen interest was well highlighted with the attendance of many dignitaries including the Mayor of Daejeon City Tae-Jong Huh, Vice Minister of Science and ICT Won-Ki Min, and National Assemblyman Sang-Min Lee.
KAIST President Sung-Chul Shin stressed the importance of AI as a growth engine, saying, “AI will be a game changer and a key enabler of major industries. But the winner takes all in industry. Therefore, without producing the world’s top technology, we will not survive in the global market. To foster highly competitive specialists who will take the lead in this industry, we will educate students who can converge multiple disciplines and contribute to national growth and beyond in the years ahead.”
2019.08.27 View 5593