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* Founding editor and Editor-in-chief of online neuroscience journal Molecular Pain (Impact factor 4.13, top 20% among Neuroscience journals) and Molecular Brain. | * Founding editor and Editor-in-chief of online neuroscience journal Molecular Pain (Impact factor 4.13, top 20% among Neuroscience journals) and Molecular Brain. | ||
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- | <img src="/preview/img/ | + | <img src="/preview/img/Inah_lee.png" /> |
- | <h4> | + | <h4>Inah Lee, PhD, Dept. of Psychology, U of Iowa |
- | <a href="http:// | + | <a href="http://inahlee.org/"><font color=gray>Homepage</font></a></h4> |
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- | === | + | === Inah Lee === |
- | * | + | * His research aim is to elucidate biological mechanisms of episodic memory and rule learning using electrophysiology, behavioral neuroscience, neuropharmacology and computational modeling. |
- | * High impact publications in | + | * High impact publications in high-profile journals such as Nature, Nature Neuroscience and Neuron. |
- | * | + | * His work in Nature (2004) provided, for the first time, that neurons in the hippocampus perform a computational function, pattern completion, that had only been suggested by theoreticians and computational modelers for more than 30 years without any experimental proof. |
- | * | + | * His publication record within a short period of time of 7 years is exceptional in the field of systems neuroscience involving animal experiments. |
+ | * Full-time commitment for 5 years. | ||
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== <font color="#037003">'''System and Behavioral unit'''</font> == | == <font color="#037003">'''System and Behavioral unit'''</font> == | ||
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* Chung's cortical surface data filtering technique has been the standard to be compared and to be validated against in the field | * Chung's cortical surface data filtering technique has been the standard to be compared and to be validated against in the field | ||
* His 2001 NeuroImage paper on tensor-based morphometry was the first paper that shows the Jacobian determinant is the only meaningful metric in structural imaging | * His 2001 NeuroImage paper on tensor-based morphometry was the first paper that shows the Jacobian determinant is the only meaningful metric in structural imaging | ||
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+ | <img src="/preview/img/sang_jeong_kim.png" /> | ||
+ | <h4>Sang Jeong Kim, Dept of Physiology, SNU College of Medicine | ||
+ | <a href="http://brain.snu.ac.kr/"><font color=gray>Homepage</font></a></h4> | ||
+ | </html> | ||
+ | === Sang Jeong Kim === | ||
+ | * Research focus on the cellular and molecular mechanisms of information storage and its related brain diseases by combining cutting-edge techniques such as patch clamping, Ca imaging, confocal microscopy, UVphotolysis and field/single unit recording from isolated neurons, brain slices and in-vivo animals. | ||
+ | * High impact publications in major journals such as Nature, Neuron, and Journal of Neuroscience, etc, making over 300 score of the total impact factor during last five years. | ||
+ | * Published a comprehensive review article in Neuron suggesting that ubiquitous synaptic plasticity is necessary to account for the rich phenomenon of memory storage in the neural network. | ||
+ | * Editorial board of the Journal of Neurophysiology. | ||
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Contents |
To execute our strategic plan (SLBM) effectively, hosting scholars at SNU and invited international scholars will form teams organized around three core research units.
The long-term goal of this unit is to elucidate the molecular and synaptic bases of learning and memory and to establish how alterations in synaptic plasticity contribute to neurological and psychiatric disorders. This unit will study how genetic/molecular/synaptic/neuronal/local circuit-level brain mechanisms enable cognitive faculties such as memory, learning, emotion, and sensation by integrating neurometric techniques at different depths with psychometric techniques such as maze learning tasks and behavioral genetics. Bong-Kiun Kaang will lead this unit joined by Graham Collingridge, Min Zhuo and Sang Jeong Kim.
The long-term goal of this unit is to understand neuronal mechanisms involved in the encoding of sensory inputs, the representation and decoding of relevant information from populations of active neurons, and the maintenance and retrieval of information within those populations. This unit will study how neuronal/local circuit/global circuit-level brain mechanisms enable cognitive processes such as sensation, perception, memory, decision making and attention; the aim is to integrate neurometric techniques including single/multi-unit cell recordings, micro/macro-brain imaging and brain stimulation with psychometric techniques encompassing various forms of perceptual and memory tasks. Computational approaches will play a major role in bridging studies from individual researchers with expertise using different types of measurement. Sang-Hun Lee will lead this unit teamed up with Randolph Blake, Inah Lee and Marcus Kaiser.
Randolph Blake, PhD, Dept. of Psychology, Vanderbilt University
The long-term goal of this unit is to understand psychiatric disorders, with an eye toward identifying means for correcting those disorders or minimizing their consequences. Furthermore, work carried out in this unit will promote development of computational and mathematical tools for handling neuroimaging data analyses which, in turn, will contribute to integrative evaluation of functional, structural and biochemical aspects of the brain, both disordered and healthy. This unit, led by Jun Soo Kwon, consists of two subunits. The ‘Clinical Neuroscience’ subunit, comprising Jun Soo Kwon and Sohee Park, will focus on studies of the pathophysiology of mental/brain disorders, including schizophrenia, depression, bipolar disorder and obsessive-compulsive disorder, they will deploy an integrative set of neurometric (large-scale brain imaging techniques on human brains) and psychometric (various forms of high cognitive tasks) measurement tools. The ‘Computational Anatomy’ subunit, led by the collaboration between Jae Sung Lee and Moo K. Chung, will integrate neurometric data obtained from functional (fMRI, PET) and structural (DTI, MRI) images - major large-scale neurometric data from humans in modern cognitive neuroscience - into a single, coherent processing and analysis framework. The successful development of such tools will substantially help the other units to advance understanding of cortical and subcortical circuitries crucial for the target cognitive functions.