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Decoding Movement Trajectories Through a T-Maze Using Point Process Filters Applied to Place Field Data from Rat Hippocampal Region CA1.

, , , , and . Neural Comput., 21 (12): 3305-3334 (2009)

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A Quantitative Representation of Continuous Brain State During Sleep., , , and . NER, page 103-106. IEEE, (2021)Parameter Estimation in Synaptic Coupling Model Using a Point Process Modeling Framework*., , , , and . EMBC, page 2362-2365. IEEE, (2018)Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique., , , , , , and . EMBC, page 3194-3197. IEEE, (2017)Continuous Prediction of Cognitive State Using A Marked-Point Process Modeling Framework., , , , , , and . EMBC, page 2933-2938. IEEE, (2019)Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models., , and . Neural Comput., (2018)Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations., , and . CoRR, (2022)Modeling neural spiking activity in the sub-thalamic nucleus of Parkinson's patients and a healthy primate., , , , , , and . CDC, page 2012-2017. IEEE, (2008)Switching Functional Network Models of Oscillatory Brain Dynamics., , and . IEEECONF, page 607-612. IEEE, (2022)Coalescence and Fragmentation of Cortical Networks during Focal Seizures, , , , , and . The Journal of Neuroscience, 30 (30): 10076--10085 (2010)Decoding Movement Trajectories Through a T-Maze Using Point Process Filters Applied to Place Field Data from Rat Hippocampal Region CA1., , , , and . Neural Comput., 21 (12): 3305-3334 (2009)