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
Course Features
Lectures
Videos
Tutorials
Lessons of this Course
1
1
Duration:
19:26
Speaker:
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.
2
2
Duration:
39:09
Speaker:
In this lesson, you will learn about different types of models, model complexity, and how to choose an appropriate model.
3
3
Duration:
1:22:11
Speaker:
This lesson provides an overview of balanced excitatory-inhibitory (E-I) networks, stability, and gain modulation.
4
4
Duration:
1:16:47
Speaker:
This lesson introduces methods for dimensionality reduction of data, with focus on factor analysis.
5
5
Duration:
1:39:32
Speaker:
This lecture delves into the dynamics of neural computation, from the spiking activity of single neurons to regional cortical population coding and network activity.
6
6
Duration:
1:24:22
Speaker:
This lesson provides an overview on spiking neuron networks and linear response models.
7
7
Duration:
1:12:38
Speaker:
In this lesson, you will learn about Bayesian neuron models and parameter estimation.
8
8
Duration:
1:33:34
Speaker:
This lecture describes Bayesian memory and learning; how to go from observations to latent variables.
9
9
Duration:
1:34:42
Speaker:
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.
10
10
Duration:
1:29:38
Speaker:
This lecture discusses approaching neural systems from an evolutionary perspective.
