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This repository contains code from my Computational Neuroscience research internship at LMU Munich. The project focused on improving Population Vector Decoding by incorporating uncertainty through Bayesian inference, inspired by Kalman Filtering and Probability Theory. For the sake of publicity, data is simulated.

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klea0605/Neural_Decoding_public

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model_1.ipynb: comparison of 3 standard Neural Decoding techniques with respective visualizations and error estimations
pv_only_model.ipynb: comparison of Population Vector Decoding with or without dropouts
modified_pv_model.ipynb: Population Vector decoding modified to incorporate uncertainty of previous predictions (length of the PV), using extrapolation and Bayesian inference

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This repository contains code from my Computational Neuroscience research internship at LMU Munich. The project focused on improving Population Vector Decoding by incorporating uncertainty through Bayesian inference, inspired by Kalman Filtering and Probability Theory. For the sake of publicity, data is simulated.

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