Attractor Networks and Generalizations

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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.

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

Explores attractor networks and Hopfield model generalizations in computational neuroscience, focusing on memory retrieval and dynamics convergence.

Scientific Computing in Neuroscience

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

Stochastic Hopfield model MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores the Stochastic Hopfield model, noisy neurons, firing probabilities, memory retrieval, and overlap equations in attractor networks.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

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.

Building Physical Neural Networks

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

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.

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.

Parameter estimation MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.

Decisions, actions, volition MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores decision models in computational neuroscience, focusing on competitive dynamics, perceptual decision making, and the problem of free will.

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.

Understanding Neuronal Morphologies: NeuroM Basics MOOC: Simulation Neurocience

Covers NeuroM basics, including checking neuron quality, extracting morphometrics, and visualizing neurons in different formats.

Generalized Integrate-and-Fire Models MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Neural Networks: Perceptron

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

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Explores the challenges and significance of classifying neurons based on morphology for accurate brain modeling and understanding brain function.

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Explores the dynamics of neuronal populations, emphasizing random networks and mean-field arguments for connectivity.

Learning of Associations MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Delves into associative memory, Hebbian learning, and hierarchical organization in neural networks.

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

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