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Related lectures (37)

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

Models and data MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Covers the optimization of neuron models for coding and decoding in computational neuroscience.

Neural Networks: Multilayer Perceptrons

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

In Silico Neuroscience: Ion Channels and Neuronal Models

Explores detailed modeling of ion channels and neuronal morphologies in in silico neuroscience, covering neuron classification, ion channel kinetics, and experimental observations.

Likelihood of Spike Train in GLM MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Covers the Generalized Linear Model (GLM) in computational neuroscience.

Modeling in vitro data MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores modeling in vitro data for computational neuroscience, including predicting subthreshold voltage and spike times.

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

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

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Likelihood Estimation and Least Squares

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

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.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Neuronal Dynamics: Random Networks MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

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

Firing Threshold in 2D Models MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores firing thresholds in 2D neuron models, pulse input, delayed spike initiation, and the FitzHugh-Nagumo model.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

MathDetour 1: Separation of time scales MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores the concept of separation of time scales in computational neuroscience and the reduction of detail in two-dimensional neuron models.

Interspike Intervals & Renewal Processes MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores interspike intervals, renewal processes, and escape noise experiments in neuronal dynamics.

Modeling Neuronal Activity

Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.

Understanding Neuronal Morphologies: NeuroM Basics MOOC: Simulation Neurocience

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

Spike Response Model (SRM)MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.

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