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Introduction to computational neuroscience
Purpose of the study track
This study track is intended for those with either a background in neurobiology or informatics looking to gain a basic understanding of computational neuroscience.

Computational neuroscience is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system.
 

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:
 

Lectures in this study track
1

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

2

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

3

Introduction to simple spiking neuron models.

4

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

5

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

6

Introduction to modelling of chemical computation in the brain

7

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

8

Introduction to stability analysis of neural models

9

Introduction to stability analysis of neural models

13

Continuation of coupled oscillators

15

Pattern generation in visual system hallucinations.

16

Introduction to the role of models in theoretical neuroscience

17

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

18

Balanced E-I networks, stability and gain modulation

19

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

20

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

21

Spiking neuron networks and linear response models.

22

Bayesian neuron models and parameter estimation.

23

Constraints can help us understand how the brain works.

24

Approaching neural systems from an evolutionary perspective

Courses in this study track
1

Introductory lectures on different aspects of biochemical models

2

Introductory lectures on different aspects of Biophysical models.

3

The CAJAL computational neuroscience courses teaches the central ideas, methods, and practice of modern computational neuroscience through a…

5

Introductory lectures on different aspects of biochemical models.

6

Introductory lectures on different aspects of statistical models.