The CAJAL computational neuroscience courses teaches the central ideas, methods, and practice of modern computational neuroscience through a combination of lectures and hands-on project work. The 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
Models and theory
Rate and spiking Bayes populations
Spiking neuron networks and linear response models
Bayesian neuron models
Lessons of this Course
1
1
Duration:
19:26
Speaker:
Introduction to the role of models in theoretical neuroscience
2
2
Duration:
39:09
Speaker:
Different types of models, model complexity, and how to choose an appropriate model.
3
3
Duration:
1:22:11
Speaker:
Balanced E-I networks, stability and gain modulation
4
4
Duration:
1:16:47
Speaker:
Methods for dimensionality reduction of data, with focus on factor analysis.
5
5
Duration:
1:39:32
Speaker:
Methods for dimensionality reduction of data, with focus on factor analysis.
6
6
Duration:
1:24:22
Speaker:
Spiking neuron networks and linear response models.
7
7
Duration:
1:12:38
Speaker:
Bayesian neuron models and parameter estimation.
8
8
Duration:
1:33:34
Speaker:
Bayesian memory and learning, how to go from observations to latent variables.
9
9
Duration:
1:34:42
Speaker:
Constraints can help us understand how the brain works.
10
10
Duration:
1:29:38
Speaker:
Approaching neural systems from an evolutionary perspective
