뇌과학의 마지막 질문, 그것은 바로 "인간의 지능은 어떻게 작동하는가?"입니다. 고작 20W만으로 작동하는 인간의 뇌는 50-10,000만 배의 전력을 소모하는 AI에 비해 에너지 효율이 극도로 높습니다. 또한, 환각(Hallucination) 등의 오류에 빠지지 않고 진정한 창의성을 발휘한다는 점에서 여전히 AI를 압도합니다. 수억 년 동안 진화해 온 뇌 유전자들이 만들어낸 경이로운 결과입니다. 하지만 역설적으로, 핵심 지능 유전자의 손실은 심각한 지적장애를 유발합니다.

우리 연구실은 이 지적장애 어린이들의 머릿속에 어떤 연산이 잘못되었는지 연구하고 있습니다. 마우스 모델과 단백질 수준 연구에서부터 첨단 in vivo 현미경과 AI모델을 이용한 실험 등을 통해 지능의 핵심적인 기전들을 밝히고 있으며, 이들 환자를 위한 첨단 유전자 치료제(AAV/ASO) 후보를 개발하고 있습니다.

We study how rare Intellectual Disabilities(ID) impact brain computations. By combining excellent basic science with translational innovation, we aim to develop novel CNS gene therapies. Our goal is to reveal how the dynamics of synaptic proteins shape neuronal activity, unlocking the molecular logic of how they contribute to learning and perception. 

 

2005 B.S., Biology, Seoul National University, Seoul 

2011 Ph.D., Biology, Lab of Sukwoo Choi, Seoul National University, Seoul 

2013-2020 Postdoctoral fellowship, Lab of Richard L. Huganir, Johns Hopkins University School of Medicine, Baltimore 

2005-2011 Graduate student, Lab of Sukwoo Choi, Seoul National University 

2011-2013 Postdoctoral Research Fellow, Lab of Sukwoo Choi, Seoul National University 

2013-2020 Postdoctoral Research Fellow, Lab of Richard L. Huganir, Johns Hopkins University School of Medicine 

2020-2021 Research Associate, Lab of Richard L. Huganir, Johns Hopkins University School of Medicine 

2021-2024 Instructor, Department of Neuroscience, Johns Hopkins University School of Medicine 

2025-2025 Assistant Professor, Department of Neurology, Johns Hopkins University School of Medicine

Our mission is to reveal the molecular logic of our intelligence in health and disease. We use advanced molecular biological tools and state-of-the-art neuroscience to test the role of synaptic and neuronal molecules in the dynamics of the living brain.

Artificial neural networks have been heavily inspired by the brain’s architecture, guiding our journey to discovering the keys to intelligence. We now find ourselves at a pivotal moment: today's AI systems surpass biological circuits in certain tasks, yet we still lack a fundamental understanding of the mechanisms behind the brain’s superior cognitive flexibility and efficiency. At Ingie Hong’s Quantitative Intelligence Lab, we are dedicated to unraveling the principles that enable the mammalian cortex to achieve remarkable feats of intelligence, including rapid learning, generalization, and inference across vast stores of memory.

A single neuron’s response depends on its synaptic connections and intrinsic properties, which are dictated by the expression of neuronal genes. However, the role of these molecules in brain computations remains largely uncharted territory. Focusing on the mouse visual cortex as a starting point for broader generalization, and using large-scale electrophysiology, advanced microscopy, and machine learning, we have begun to uncover the impact of key synaptic genes on cortical processing and their role in the brain’s “working algorithm” (Hong et al., Nature, 2024). Our molecular tools, including gene therapy vectors and antisense oligonucleotides, show promise as effective therapeutic candidates.

Our research will advance the nascent field of 'neurocomputational therapeutics'—innovative genetic and pharmacological tools that address biases in neural activity. These tools will not only facilitate the development of novel mechanism-based treatments for brain disorders but also inspire the next generation of intelligent artificial neural networks.

For a full publication list, see Ingie’s NCBI Bibliography or Google Scholar page.

Barão, S.^✉, Xu, Y., Hong, I.^✉, Müller, U., & Huganir, R. L.^✉ (2025). Nature Communications, 16(1), 11060.  

Brill, J., Clarke, B., Hong, I.^✉, & Huganir, R. L.^✉ (2025). Proceedings of the National Academy of Sciences, 122(18), e2427288122. 

Hong, I.^✉, Kim, J., Hainmueller, T., Kim, D. W., Keijser, J., Johnson, R. C., Park, S. H., Limjunyawong, N., Yang, Z., Cheon, D., Hwang, T., Agarwal, A., Cholvin, T., Krienen, F. M., McCarroll, S. A., Dong, X., Leopold, D. A., Blackshaw, S., Sprekeler, H., … Huganir, R. L.^✉ (2024). Nature, 635(8038), 398–405.  

Lopez-Ortega, E., Choi, J. Y., Hong, I., Roth, R. H., Cudmore, R. H., & Huganir, R. L. (2024). Cell Reports, 43(4), 113966.  

Araki, Y., Rajkovich, K. E., Gerber, E. E., Gamache, T. R., Johnson, R. C., Tran, T. H. N., Liu, B., Zhu, Q., Hong, I., Kirkwood, A., & Huganir, R. (2024). Science, 383(6686), eadk1291.  

Araki, Y.*, Gerber, E. E.*, Rajkovich, K. E.*, Hong, I.*, Johnson, R. C., Lee, H.-K., Kirkwood, A., & Huganir, R. L. (2023). Proceedings of the National Academy of Sciences, 120(37), e2308891120.  

Graves, A. R., Roth, R. H., Tan, H. L., Zhu, Q., Bygrave, A. M., Lopez-Ortega, E., Hong, I., Spiegel, A. C., Johnson, R. C., Vogelstein, J. T., Tward, D. J., Miller, M. I., & Huganir, R. L. (2021). eLife, 10, e66809.  

Severin, D., Hong, S. Z., Roh, S.-E., Huang, S., Zhou, J., Bridi, M. C. D., Hong, I., Murase, S., Robertson, S., Haberman, R. P., Huganir, R. L., Gallagher, M., Quinlan, E. M., Worley, P., & Kirkwood, A. (2021). Proceedings of the National Academy of Sciences, 118(37), e2105388118.  

Zhang, J.-F. *, Liu, B. *, Hong, I. *, Mo, A., Roth, R. H., Tenner, B., Lin, W., Zhang, J. Z., Molina, R. S., Drobizhev, M., Hughes, T. E., Tian, L., Huganir, R. L., Mehta, S., & Zhang, J. (2021). Nature Chemical Biology, 17(1), 39–46.  

Postdoctoral Fellow : 강태욱

Administrative Assistant : 신주영

Undergraduate Intern : 김재근

Homepage : https://ingiehonglab.org/

Contact : Ingiehong@kaist.ac.kr