Computational Modeling of Neuronal Plasticity

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Introduction to computational neuroscience

Computational Modeling of Neuronal Plasticity

By
Florence I. Kleberg and Jochen Triesch
Category
Computational neuroscience
Level
Intermediate

In this course, you will learn how computational neuroscientists use mathematical models and computer simulations to study different plasticity phenomena in the brain. During the course, you will program your own neuron model, a so-called leaky-integrate-and-fire (LIF) neuron model, and simulate it with a computer. You will also learn how to add various neuronal properties and plasticity mechanisms to the model and study how they operate. This course will deepen your understanding of neural plasticity and prepare you for studying plasticity and learning in larger models such as neural networks.

About this course:

  • This course provides users with a brief video introduction to the concepts, lecture notes, and solution figures.
  • The main idea is to model a neuron and its plasticity mechanisms from scratch, without the use of specialised neuronal modelling software such as Brian, NEURON, or NEST. Of course, the use of python packages such as numpy, scipy, ipython and so on is allowed.
  • It is advised to be familiar with the basics of the Python programming language before commencing the course.
Course Features
Videos
Lectures
Example Code
Exercises and Solutions
Lessons of this Course
1
1
Computational Modeling of Neuronal Plasticity: Introduction
Duration:
0:40
Speaker:
Florence I. Kleberg

This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.

2
2
1.1 Leaky Integrate-and-Fire Model
Duration:
1:23
Speaker:
Florence I. Kleberg

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

3
3
1.2 Synaptic Inputs
Duration:
1:20
Speaker:
Florence I. Kleberg

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

4
4
1.3 Integration Time Step
Duration:
1:08
Speaker:
Florence I. Kleberg

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

5
5
1.4 Poisson Spike Trains
Duration:
1:18
Speaker:
Florence I. Kleberg

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

6
6
2.1 Spike-Rate Adaptation
Duration:
1:26
Speaker:
Florence I. Kleberg

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.

7
7
2.2 Refractory Period
Duration:
0:42
Speaker:
Florence I. Kleberg

This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.

8
8
3.1 Spike-Timing Dependent Plasticity (STDP)
Duration:
2:40
Speaker:
Florence I. Kleberg

In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).

9
9
3.2 Correlated Spike Trains
Duration:
2:54
Speaker:
Florence I. Kleberg

This lesson reviews theoretical and mathematical descriptions of correlated spike trains.

10
10
3.3 Correlations and STDP
Duration:
1:43
Speaker:
Florence I. Kleberg

This lesson investigates the effect of correlated spike trains on spike-timing dependent plasticity (STDP).

11
11
4.1 Synaptic Normalisation
Duration:
2:58
Speaker:
Florence I. Kleberg

This lesson goes over synaptic normalisation, the homeostatic process by which groups of weighted inputs scale up or down their biases.

12
12
4.2 Intrinsic Plasticity
Duration:
2:08
Speaker:
Florence I. Kleberg

In this lesson, you will learn about the intrinsic plasticity of single neurons.

13
13
5.1 Short-Term Facilitation
Duration:
1:58
Speaker:
Florence I. Kleberg

This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.

14
14
5.2 Short-Term Depression
Duration:
1:40
Speaker:
Florence I. Kleberg

This lesson describes short-term depression, a reduction of synaptic information transfer between neurons.

15
15
Computational Modeling of Neuronal Plasticity: Epilogue
Duration:
0:37
Speaker:
Florence I. Kleberg

This lesson briefly wraps up the course on Computational Modeling of Neuronal Plasticity.

 

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