Introduction to computational neuroscience

Related Study tracks

Introduction to Neuroinformatics

Introduction to computational neuroscience

By
INCF
Category
Computational neuroscience
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.

 

Course Features
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
1
Introduction to modeling the brain
Duration:
1:23:01
Speaker:
Gaute Einevoll

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

2
2
Some simple models of neurons
Duration:
8:23
Speaker:
Geoffrey Hinton

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

3
3
Integrate and fire neuron modeling
Duration:
48 Slides
Speaker:
Zubin Bhuyan

Introduction to simple spiking neuron models.

4
4
The action potential
Duration:
28:29
Speaker:
Carl Petersen

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

5
4
Modelling across scales of analysis
Duration:
1:11:29
Speaker:
Upi Bhalla

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
6
Principles of intracellular modelling and computation
Duration:
1:00:11
Speaker:
Upi Bhalla

Introduction to modelling of chemical computation in the brain

7
7
Simulating the long time scales and large molecule numbers involved in synaptic plasticity
Duration:
15:44
Speaker:
Kim "Avrama" Blackwell

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

8
8
Single neuron models - I
Duration:
1:26:06
Speaker:
Bard Ermentrout

Introduction to stability analysis of neural models

9
9
Single Neuron Models - II
Duration:
1:25:38
Speaker:
Bard Ermentrout

Introduction to stability analysis of neural models

10
10
Neural oscillations, weak coupling and networks I
Duration:
1:24:30
Speaker:
Bard Ermentrout

Oscillations and bursting

11
11
Neural oscillations and networks II
Duration:
1:31:57
Speaker:
Bard Ermentrout

Oscillations and bursting

12
12
Oscillatory networks and the Ott Antonsen theory I
Duration:
1:26:02
Speaker:
Bard Ermentrout

Weakly coupled oscillators

13
13
Oscillatory networks II
Duration:
1:24:44
Speaker:
Bard Ermentrout

Continuation of coupled oscillators

14
14
Firing rate models - networks
Duration:
1:26:42
Speaker:
Bard Ermentrout

Firing rate models.

15
15
Spatiotemporal dynamics, waves, pattern formation
Duration:
1:20:42
Speaker:
Bard Ermentrout

Pattern generation in visual system hallucinations.

16
16
Models and theory
Duration:
19:26
Speaker:
Jakob Macke

Introduction to the role of models in theoretical neuroscience

17
17
Biophysical models of single neurons
Duration:
39:09
Speaker:
Astrid Prinz

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

18
18
Tightly and loosely coupled networks
Duration:
1:22:11
Speaker:
Kenneth Miller

Balanced E-I networks, stability and gain modulation

19
19
Dimensionality reduction of large-scale neural recordings
Duration:
1:16:47
Speaker:
Byron Yu

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

20
20
Dynamics of rate-based and spiking balanced random networks
Duration:
1:39:32
Speaker:
Julijana Gjorgjieva

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

21
21
Theory of network dynamics
Duration:
1:24:22
Speaker:
Tatjana Tchumatchenko

Spiking neuron networks and linear response models.

22
22
Neural data analysis: The Bayesics
Duration:
1:12:38
Speaker:
Jakob Macke

Bayesian neuron models and parameter estimation.

23
23
Bayesian models of perception, cognition and learning
Duration:
1:33:34
Speaker:
Máté Lengyel

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

24
24
Constraints on information processing
Duration:
1:34:42
Speaker:
Simon Laughlin

Constraints can help us understand how the brain works.

25
25
Evolution and Brain Computation
Duration:
1:29:38
Speaker:
Gilles Laurent

Approaching neural systems from an evolutionary perspective

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