Title

Inhibitory Connections in the Assembly Neural Network for Texture Segmentation

Abstract

A neural network with assembly organization is described. This assembly network is applied to the problem of texture segmentation in natural scenes. The network is partitioned into several subnetworks: one for each texture class. Hebb's assemblies are formed in the subnetworks during the process of training the excitatory connections. Also, a structure of the inhibitory connections is formed in the assembly network during a separate training process. The inhibitory connections result in inhibitory interactions between different subnetworks. Computer simulation of the network has been performed. Experiments show that an adequately trained assembly network with inhibitory connections is more efficient than without them.
A neural network with assembly organization is described. This assembly network is applied to the problem of texture segmentation in natural scenes. The network is partitioned into several subnetworks: one for each texture class. Hebb's assemblies are formed in the subnetworks during the process of training the excitatory connections. Also, a structure of the inhibitory connections is formed in the assembly network during a separate training process. The inhibitory connections result in inhibitory interactions between different subnetworks. Computer simulation of the network has been performed. Experiments show that an adequately trained assembly network with inhibitory connections is more efficient than without them.

Department(s)

Electrical and Computer Engineering

International Standard Serial Number (ISSN)

0893-6080

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1998 Elsevier, All rights reserved.


Share

 
COinS