The CAJAL Course in Computational Neuroscience teaches the central ideas, methods, and practice of modern computational neuroscience through a combination of lectures and hands-on project work. This course is designed for graduate students and postdoctoral fellows from a variety of disciplines, including neuroscience, physics, electrical engineering, computer science, mathematics, and psychology.
Cajal Course in Computational Neuroscience
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.