Learning of Associations

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Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Explores synaptic plasticity, spike-timing models, and online memory learning challenges in computational neuroscience.

Explores the AdEx neuron model, analyzing firing patterns and phase planes.

Explores the engineering of neurons using light, chemicals, and sound to modulate neural activity and behavior.

Explores a neural model, assemblies of neurons, language acquisition, and the future of neuromorphic intelligent systems.