Frequency response analysis of an artificial neural network modeling the lamina ganglionaris of musca domestica

Author

Haines, Karen G. ; Moya, Javier A. ; Caudell, Thomas P.

Author_Institution

Dept. of Comput. Sci., Western Australia Univ., Nedlands, WA, Australia

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

118

Lastpage

123

Abstract

The following investigated the frequency response of a biologically motivated artificial neural network (ANN). This analysis will reveal nonlinearities present in the ANN, as well as classify the network´s filtering characteristics. This work will demonstrate that that photoreceptor processing acts as a nonlinear low pass filter. Discussions will also demonstrate that ALMC processing acts as an inverting nonlinear low pass filter in the frequency domain. These characteristics are similar to their biological counterparts

Keywords

filtering theory; frequency response; low-pass filters; neural nets; neurophysiology; nonlinear filters; physiological models; vision; ANN; biologically motivated artificial neural network; filtering characteristics; first optic ganglion; frequency response; frequency response analysis; housefly; inverting nonlinear low pass filter; lamina ganglionaris; musca domestica; photoreceptor processing; Artificial neural networks; Extracellular; Filtering; Frequency response; Low pass filters; Nerve fibers; Neurons; Photoreceptors; Retina; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005454

Filename

1005454