What are we trying to model

Related lectures (32)

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Explores the Generalized Integrate-and-Fire Model and the Nonlinear Integrate-and-Fire Model.

Explores the logic of neuronal function, the Perceptron model, deep learning applications, and levels of abstraction in neural models.

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

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

Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.

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

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.