Assignments for the course "Computational Neuroscience" (A.Y. 2022/2023) from the Computer Science Master's Degree of University of Pisa.
Assignments are done in Python, specifically Jupyter Notebooks.
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LAB1 Implementation of the Izhikevich’s model. Includes the 20 neuro-computational features of biological neurons using the model, each with plots of the resulting membrane potential’s time courses and the phase portraits.
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LAB2_1 Implementation of Hebbian Learning (basic Hebbian Learning, Oja Rule and Subtractive Normalization).
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LAB2_2 Implementation of an Hopfield Network that learns 3 numbers (from MNIST) and is able to recall them starting from their noisy variant.
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LAB3_1 Implementation of an RNN and TDNN in PyTorch to solve NARMA10 time series task.
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LAB3_2 implementation of an Echo State Network in NumPy to solve NARMA10 time series task.