This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

This lesson gives an introduction to simple spiking neuron models.

This lesson covers the ionic basis of the action potential, including the Hodgkin-Huxley model. 

This lecture describes forms of plasticity on many levels: short-term, long-term, metaplasticity, and structural plasticity. Included in this…

This lesson provides an introduction to modelling of chemical computation in the brain.

This lesson gives a presentation on computationally demanding studies of synaptic plasticity on the molecular level.

This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.

This lesson continues a thorough description of the concepts, theories, and methods involved in the modeling of single neurons. 

In this lesson, you will learn about phenomena of neural populations such as synchrony, oscillations, and bursting.

This lesson continues discussing properties of neural oscillations and networks. 

This lesson provides more context around weakly coupled oscillators.

This lesson provides a continued discussion and characterization of coupled oscillators. 

This lesson gives an overview of modeling neurons based on firing rate. 

This lesson characterizes the pattern generation observed in visual system hallucinations.

This lesson provides an introduction to the role of models in theoretical neuroscience, particularly focusing on David Marr's work on levels of…

In this lesson, you will learn about different types of models, model complexity, and how to choose an appropriate model.

This lesson provides an overview of balanced excitatory-inhibitory (E-I) networks, stability, and gain modulation. 

This lesson introduces methods for dimensionality reduction of data, with focus on factor analysis.

This lecture delves into the dynamics of neural computation, from the spiking activity of single neurons to regional cortical population coding…

This lesson provides an overview on spiking neuron networks and linear response models.

In this lesson, you will learn about Bayesian neuron models and parameter estimation.

This lesson introduces the concept of constraints on information processing, and how studying these constraints can reveal valuable knowledge…

This lecture discusses approaching neural systems from an evolutionary perspective.