Website

http://www.snl.salk.edu/~eugene/

Latest Publications

• Kim, R. Sejnowski, T. J. Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks, Nature Neuroscience, 24(1): 129-139, 2021 (PDF)
• Knickrehm, J. Kim, R. Lainscsek, C. Sampson, A. L. Gur, R. C. Gur, R. E. The Cogs Investigators, Sejnowski, T. J. LIGHT, G. A. Machine learning approaches to characterize neurophysiological and cognitive measures predictive of schizophrenia diagnosis, Society for Neuroscience Abstracts, 2019
• Kim, R., Sejnowski, T. J. Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory, BioaRxiv, doi.org/10.1101/842302, 2019 (PDF)
• Kim, K., Li, Y., Sejnowski, T. J. Simple Framework for Constructing Functional Spiking Recurrent Neural Networks, BioaRxiv, doi.org/10.1101/579706, 2019 (PDF)
• Kim, R. Li, Y. Sejnowski, T. J. Simple Framework for Constructing Functional Spiking Recurrent Neural Networks, Proceedings of the National Academy of Sciences, U.S.A., 116, 22811-22820, 2019 (PDF)
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