Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA

Background

In our previous work

Methods

The main goal of this work is to use sequences of images and to design an attention model including conjunction search based on unsupervised self-learning. First, Independent Component Analysis algorithm is used to determine an initial set of basis functions from the first image (Figure

Initial basis images

**Initial basis images**. a) the original image; b) a set of basis functions received by ICA from several patches from one image; c) and d) the convolution results of two functions with the original image.

Components for unsupervised self-organizing learning

Components for unsupervised self-organizing learning.

Conclusion

It is shown that performing sparse learning codes on video sequences of natural scenes produces results with qualitatively similar spatio-temporal properties of simple receptive field of neurons. The basic functions are similar to those obtained by sparse learning, but in our model they have a particular order (Figure

Ordered resulting basis functions

Ordered resulting basis functions.

Acknowledgements

The project described was supported by NIH Grant Number P20 RR-16460 from the IDeA Networks of Biomedical Research Excellence (INBRE) Program of the National Center for Research Resources.