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

This lesson continues from the previous lecture, giving an overview of various neural phenomena such as oscillations and bursting. 

This lesson provides more context around weakly coupled oscillators.

This lesson builds upon previous lectures in this series, providing an overview of coupled oscillators.

In this lesson, you will learn about neuronal models based on their spike rate. 

In this lesson, you will learn about neural activity pattern generation in visual system hallucinations.

This lesson provides an introduction to the role of models in theoretical neuroscience.

This lesson introduces different types of models, model complexity, and how to choose an appropriate model.

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

In this lesson, you will learn about methods for dimensionality reduction of data, with a focus on factor analysis.

This lesson gives an in-depth look into various types of neuronal networks, as well properties, parameters, and phenomena which characterize them. 

In this lesson, you will learn about spiking neuron networks and linear response models.

This lesson discusses Bayesian neuron models and parameter estimation.

This lesson gives an overview of Bayesian memory and learning, as well as how to go from observations to latent variables.

In this lesson, you will learn about how constraints can help us understand how the brain works.

This lesson discusses how to approach neural systems from an evolutionary perspective.

This lecture covers computational principles that growth cones employ to detect and respond to environmental chemotactic gradients, focusing particularly on growth-cone shape dynamics.

In this lecture you will learn that in developing mouse somatosensory cortex, endogenous Btbd3 translocate to the cell nucleus in response to neuronal activity and oriente primary dendrites toward active axons in the barrel hollow.

In this presentation, the speaker describes some of their recent efforts to characterize the transcriptome of the developing human brain, and and introduction to the BrainSpan project.

This talk introduces Bayes' theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event.