Learning features and predictive transformation encoding based on a horizontal product model

Junpei Zhong, Cornelius Weber, Stefan Wermter

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


The visual system processes the features and movement of an object in separate pathways, called the ventral and dorsal streams. To integrate this principle in a functional model, a recurrent predictive network with a horizontal product is introduced. Learned in an unsupervised manner, two sets of hidden units represent cells in the ventral and dorsal pathways, respectively. Experiments show that the activity in the ventral-like units persists, given that the same feature appears in the receptive field, whilst the activity in the dorsal-like units shows a fluctuating pattern with different directions of object movements. Moreover, we show that the position information predicts the input's future position taking into account its moving direction due to the direction-selective responses of the dorsal-like units.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Number of pages8
EditionPART 1
Publication statusPublished - 2012
Externally publishedYes
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: 11 Sep 201214 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7552 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Artificial Neural Networks, ICANN 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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