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  • Research Article
  • Open Access

Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems

  • 1,
  • 2,
  • 2 and
  • 1Email author
EURASIP Journal on Advances in Signal Processing20102011:781561

  • Received: 15 June 2010
  • Accepted: 2 November 2010
  • Published:


Object motion can be measured locally by neurons at different stages of the visual hierarchy. Depending on the size of their receptive field apertures they measure either localized or more global configurationally spatiotemporal information. In the visual cortex information processing is based on the mutual interaction of neuronal activities at different levels of representation and scales. Here, we utilize such principles and propose a framework for modelling neural computational mechanisms of motion in primates using biologically inspired principles. In particular, we investigate motion detection and integration in cortical areas V1 and MT utilizing feedforward and modulating feedback processing and the automatic gain control through center-surround interaction and activity normalization. We demonstrate that the model framework is capable of reproducing challenging data from experimental investigations in psychophysics and physiology. Furthermore, the model is also demonstrated to successfully deal with realistic image sequences from benchmark databases and technical applications.


  • Visual Cortex
  • Receptive Field
  • Gain Control
  • Motion Detection
  • Realistic Image

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Authors’ Affiliations

Faculty of Engineering and Computer Sciences, Institute for Neural Information Processing, Ulm University, James-Franck-Ring, 89069 Ulm, Germany
Equipe Projet NeuroMathComp, Institut National de Recherche en Informatique et en Automatique (INRIA), Unité de recherche INRIA Sophia Antipolis, Sophia Antipolis Cedex, 06902, France


© Jan D. Bouecke et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.