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Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.
Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.
