- Department：
- Position：Postdoctoral Researcher
- Research Field：Computational Neuroscience
- Phone：
- E-mail：kaidi.shao@icpbr.ac.cn
Kaidi Shao started her training as a computational neuroscientist during her Bachelor studies in Automation at East China University of Science and Technology, where she joined the Institute for Cognitive Neurodynamics and did her thesis with Prof. Xiaochuan Pan on transfer entropy. In 2015, she started her master in Neural Information Processing in University of Tuebingen and the Max-Planck Institute for Biological Cybernetics, during which she worked with Dr. Michel Besserve on the modelling of PGO waves. In 2018, she continued with a Ph.D. with Prof. Nikos Logothetis and Dr. Besserve on biophysical and data-driven modelling of transient neural events and event-based causal inference. After getting her doctoral degree in 2022, she joined the International Center for Primate Brain Research as a postdoctoral researcher.
Transient dynamics, Neural events, Causal inference, Operator-theoretic framework, Data-driven modelling.
· Kaidi Shao, Nikos K. Logothetis, Michel Besserve, “Information-theoretic measures of causal influences during transient neural events”, Frontiers in Network Physiology, 2023, DOI: 10.3389/fnetp.2023.1085347
· Kaidi Shao, Juan F. Ramirez-Villegas, Nikos K. Logothetis, Michel Besserve, “A model of Ponto-Geniculo-Occipital waves supports bidirectional control of cortical plasticity across sleep-stages
”, bioRxiv, 2021, DOI: 10.1101/2021.03.16.432817
· Kaidi Shao, Nikos K. Logothetis, Michel Besserve, “Bayesian Information Criterion for Event-based Multi-trial Ensemble data”, arXiv, 2022, doi: https://doi.org/10.48550/arXiv.2204.14096
·Kaidi Shao, Nikos K. Logothetis, Michel Besserve, “Estimating the mechanisms underlying transient dynamics based on peri-event data”, A causal view on dynamical systems, NeurIPS 2022 workshop, 2022•openreview.net, https://scholar.google.de/scholar？hl=en&as_sdt=0%2C5&q=Estimating+the+mechanisms+underlying+transient+dynamics+based+on+peri-event+data&btnG=