Learning of Associations

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Related lectures (36)

Associative Memory: Magnetic Materials MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores the dynamics of associative memory in networks of neurons and includes a detour into magnetic materials.

Hopfield Model: Memory and Dynamics MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores the Hopfield Model for associative memory and its dynamics.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Attractor Networks and Spiking Neurons MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Neuromorphic Computing: Concepts and Hardware Implementations

Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.

Storage Capacity: Prototypes and Neuronal Dynamics MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores the storage capacity of associative memory in networks of neurons and the impact of multiple prototypes on error rates.

Neural Networks: Training and Activation

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

Deep Neural Networks: Training and Optimization

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

Neural Networks: Perceptron

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

Nonlinear Supervised Learning

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

Synaptic Plasticity: Online Memory Learning MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

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

AdEx model: Firing patterns and phase plane analysis MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Engineering Neurons: Optogenetics

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

Neural Model: Assemblies of Neurons and Language Acquisition

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

Modeling Electrophysiology: Different Scales MOOC: Neuroscience Reconstructed: Genetics and Brain Development

Covers modeling electrophysiology at different scales, discussing ion channels, single neurons, and microcircuits.

Attractor Networks and Generalizations MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores attractor networks, Hopfield model generalizations, and memory dynamics in computational neuroscience.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Neuronal Dynamics of Cognition: Associative Memory MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores associative memory in neuronal networks, neuronal structure, and information processing.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

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