Spiking neural networks´ model with spike frequency adaptation for e-nose

Author

Badiei, Shirin ; Abdel-Aty-Zohdy, Hoda

Author_Institution

Dept. of Electr. & Comput. Eng., Oakland Univ., Rochester Hills, MI, USA

fYear

2011

fDate

20-22 July 2011

Firstpage

62

Lastpage

64

Abstract

We create a spiking neural network of Integrate and Fire neurons with spike frequency adaption based on parameters adjusted for our e-nose device, and investigate the use of this model for odor classification. Addition of spike frequency adaptation term brings the model closer to the response of the olfactory system. Data from Cyranose 320, a polymer based 32-sensor array, is used to test the system and create unique dynamic spiking patterns. The results for four analytes are presented.

Keywords

chemioception; electronic engineering computing; electronic noses; neural nets; pattern classification; polymers; Cyranose 320; dynamic spiking pattern; e-nose device; fire neurons; integrate neurons; odor classification; olfactory system; polymer based 32-sensor array; spike frequency adaptation; spiking neural network model; Adaptation models; Biological neural networks; Biological system modeling; Computational modeling; Mathematical model; Neurons; Training; e-nose; spiking neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace and Electronics Conference (NAECON), Proceedings of the 2011 IEEE National

Conference_Location

Dayton, OH

ISSN

0547-3578

Print_ISBN

978-1-4577-1040-7

Type

conf

DOI

10.1109/NAECON.2011.6183078

Filename

6183078