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Task success in trained spiking neuronal network models coincides with emergence of cross-stimulus-modulated inhibition
Yuqing Zhu, Chadbourne M.B. Smith, Mufeng Tang, Franz Scherr, View ORCID ProfileJason N. MacLean
doi: https://doi.org/10.1101/2023.08.29.555334
Yuqing Zhu
1Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
2Department of Neuroscience, Pomona College, Claremont, California, United States of America
Chadbourne M.B. Smith
3Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
4Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, United States of America
Mufeng Tang
5Department of Statistics, University of Chicago, Chicago, Illinois, United States of America
6Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, England, United Kingdom
Franz Scherr
7Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
8Huawei Technologies Switzerland, Zurich, Switzerland
Jason N. MacLean
1Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
3Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America

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Posted August 31, 2023.
Task success in trained spiking neuronal network models coincides with emergence of cross-stimulus-modulated inhibition
Yuqing Zhu, Chadbourne M.B. Smith, Mufeng Tang, Franz Scherr, Jason N. MacLean
bioRxiv 2023.08.29.555334; doi: https://doi.org/10.1101/2023.08.29.555334
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