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Introduction to computational neuroscience

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Level
Beginner

How to gain the recommended background knowledge for success in computational neuroscience

Most who enter the field of computational neuroscience have a prior background in either mathematics, physics, computer science, or (neuro)biology. Since computational neuroscience requires a bit of knowledge from all these fields, with some basic knowledge of neurons and a familiarity with certain types of equations and mathematical concepts, we recommend two different "starting tracks" depending on the student's background before you begin the lectures listed below:

Intro to computational neuroscience for a computer sci/math background
The student should learn basic concepts and equations for how neurons generate signals, either a more through introduction via the Cellular Mechanisms of Brain Function course or a quick reminder via the Basic mathematics for computational neuroscience tutorials.

Intro to computational neuroscience for a biology background
Here the student is assumed to already have basic knowledge of neurons. We recommend some orientation in mathematics (differential equations, linear algebra, dynamical systems) and computer science. There are a number of possible online courses openly available, for instance the MIT OpenCourseware course on Differential Equations. After that, we recommend a quick orientation on how these mathematics apply to neuroscience by viewing the Basic mathematics for computational neuroscience tutorials.

 

Modelling the Brain
Simple abstract models of neurons
Simple spiking neuron models
Modelling of chemical computation in the brain
Stability analysis of neural models
Oscillations and bursting
Firing rate models
Role of models in theoretical neuroscience
Lessons of this Course
1
Duration:
1:23:01

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

2
Duration:
8:23

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

3
Duration:
48 Slides
Speaker:

Introduction to simple spiking neuron models.

4
Duration:
28:29

The ionic basis of the action potential, including the Hodgkin Huxley model. 

4
Duration:
1:11:29
Speaker:

Forms of plasticity on many levels - short-term, long-term, metaplasticity, structural plasticity. With examples related to modelling of biochemical networks. 

[NB: The sound uptake is a bit noisy the first few minutes, but gets better from about 5 mins in]

 

6
Duration:
1:00:11
Speaker:

Introduction to modelling of chemical computation in the brain

7
Duration:
15:44

Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level

8
Duration:
1:26:06

Introduction to stability analysis of neural models

9
Duration:
1:25:38

Introduction to stability analysis of neural models

10
Duration:
1:24:30

Oscillations and bursting

11
Duration:
1:31:57

Oscillations and bursting

12
Duration:
1:26:02

Weakly coupled oscillators

13
Duration:
1:24:44

Continuation of coupled oscillators

14
Duration:
1:26:42

Firing rate models.

15
Duration:
1:20:42

Pattern generation in visual system hallucinations.

16
Duration:
19:26
Speaker:

Introduction to the role of models in theoretical neuroscience

17
Duration:
39:09
Speaker:

Different types of models, model complexity, and how to choose an appropriate model.

18
Duration:
1:22:11

Balanced E-I networks, stability and gain modulation

19
Duration:
1:16:47
Speaker:

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

20
Duration:
1:39:32

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

21
Duration:
1:24:22

Spiking neuron networks and linear response models.

22
Duration:
1:12:38
Speaker:

Bayesian neuron models and parameter estimation.

23
Duration:
1:33:34

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

24
Duration:
1:34:42

Constraints can help us understand how the brain works.

25
Duration:
1:29:38

Approaching neural systems from an evolutionary perspective