Share Email Print
cover

Proceedings Paper

Modeling and investigation of some spatio-temporal aspects of visual information processing in the retinal neural network
Author(s): Alain Faure; Ilya A. Rybak; Natalia A. Shevtsova; Alexander V. Golovan; Olga Cachard; Valentina I. Gusakova; Lubov N. Podladchikova; Arkadi A. Klepatch
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

A simplified retinal neural network (RNN) model has been considered. The main properties of this model are as follows: (1) primary transform of input raster simulates a decrease of resolution from the fovea to the retinal periphery; (2) the RNN consists of two layers, i.e., excitatory and inhibitory ones, each of them being formed by elements with identical properties excluding input transform; (3) each element of the excitatory layer is inhibited by the retinotopically corresponding element of the inhibitory layer; and (4) receptive field size and time constant of inhibitory neurons are more than those of excitatory ones. Two versions of the RNN differing in several aspects from each other were developed. In the first model the Gauss transform was used as a primary transform of the input raster. In addition, a wide range of the RNN and visual stimulus parameters was tested by computer simulation. The primary transform in the second model was performed by brightness averaging on neuron receptive fields. In the last case, qualitative behavior of the RNN was studied analytically. It was shown that neuron dynamics in response to moving stimuli and the preferable velocity of motion depended on neuron position in the RNN. In particular, foveal neurons were tuned to lower velocity as compared with peripheral ones.

Paper Details

Date Published: 7 December 1994
PDF: 10 pages
Proc. SPIE 2430, Optical Memory & Neural Networks '94: Optical Neural Networks, (7 December 1994); doi: 10.1117/12.195588
Show Author Affiliations
Alain Faure, Univ. of Le Havre (France)
Ilya A. Rybak, Univ. of Pennsylvania (United States)
Natalia A. Shevtsova, Rostov State Univ. (Russia)
Alexander V. Golovan, Rostov State Univ. (Russia)
Olga Cachard, Univ. of Le Havre (France)
Valentina I. Gusakova, Rostov State Univ. (Russia)
Lubov N. Podladchikova, Rostov State Univ. (Russia)
Arkadi A. Klepatch, Rostov State Univ. (Russia)


Published in SPIE Proceedings Vol. 2430:
Optical Memory & Neural Networks '94: Optical Neural Networks
Andrei L. Mikaelian, Editor(s)

© SPIE. Terms of Use
Back to Top