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 fundamental neural phenomena such as oscillations and bursting, and the effects these have on cortical networks. 

This lesson continues discussing properties of neural oscillations and networks. 

In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.

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 description/analysis of the brain as a complex system: computation, algorithm & representation, and implementation.

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 and network activity.

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 lecture describes Bayesian memory and learning; how to go from observations to latent variables.

This lesson introduces the concept of constraints on information processing, and how studying these constraints can reveal valuable knowledge about how the brain and other systems function. 

This lecture discusses approaching neural systems from an evolutionary perspective.

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

This lecture provides 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.