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Explores data augmentation as a key regularization method in deep learning, covering techniques like translations, rotations, and artistic style transfer.
Explores the variability of spike trains in computational neuroscience, covering experiments, sources of variability, and stochastic spike arrival and firing.
Explores the quality of Integrate-and-Fire models in computational neuroscience through comparisons with experimental data and mathematical predictions.
Explores the application of computational neuroscience in neuroprosthetics, focusing on predicting intended arm movements based on spike times and the importance of systematic parameter optimization.
