2.1 Spike rate adaptation

By

Florence I. Kleberg and Jochen Triesch

Difficulty level

Intermediate

Speaker

Florence I. Kleberg

Type

Duration

1:26

Topic

Computational neuroscience

Neuronal plasticity

This lecture (6/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures. Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Topics covered in this lesson

Spike rate adaptation

Documents

Solution figure 1

Solution figure 2

Solution figure 3

External Links

I cannot solve it on my own, please show me example code

Download Python

Prerequisites

Experience with Python programming language
Previous lectures in the series

Back to the course

Computational Modeling of Neuronal Plasticity

Lessons

1

1

Computational Modeling of Neuronal Plasticity: Introduction

2

2

1.1 Leaky integrate-and-fire model

3

3

1.2 Synaptic inputs

4

4

1.3 Integration time step

5

5

1.4 Poisson spike trains

6

6

2.1 Spike rate adaptation

7

7

2.2 Refractory period

8

8

3.1 Spike-timing dependent plasticity (STDP)

9

9

3.2 Correlated spike trains

10

10

3.3 Correlations and STDP

11

11

4.1 Synaptic normalisation

12

12

4.2 Intrinsic plasticity

13

13

5.1 Short-term facilitation

14

14

5.2 Short-term depression

15

15

Computational Modeling of Neuronal Plasticity: Epilogue