top banner top banner

Search publications

Spike neural models (part I): The Hodgkin-Huxley model

Full text PDF
Bibliographic information: BibTEX format RIS format XML format APA style
Cited references information: BibTEX format APA style
Doi: 10.20982/tqmp.13.2.p105

Johnson, Melissa G. , Chartier, Sylvain
Keywords: Spiking neural networks , neural models , Hodkgin-Huxley model
Tools: Matlab
(no sample data)   (no appendix)

Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

Pages © TQMP;
Template last modified: 2017-27-09.
Page consulted on .
Be informed of the upcoming issues with RSS feed: RSS icon RSS