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Cajal Course in Computational Neuroscience


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. 

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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

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

Methods for dimensionality reduction of data, with focus on factor analysis.

6
6
Duration:
1:24:22

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

Bayesian memory and learning, how to go from observations to latent variables.

9
9
Duration:
1:34:42

Constraints can help us understand how the brain works.

10
10
Duration:
1:29:38

Approaching neural systems from an evolutionary perspective