Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.
Delves into simulating network dynamics in in silico neuroscience, covering spontaneous and evoked activity, in-vitro and in-vivo simulations, and sensitivity analysis.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
