Anthony N Burkitt

David B Grayden

Michael R Ibbotson

Parvin Zarei

Tatiana Kameneva

BMC Neuroscience

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POSTER PRESENTATION Open Access
The interaction between integration and
segmentation neurons for motion perception
Parvin Zarei1,2*, Tatiana Kameneva1, Michael R Ibbotson3, Anthony N Burkitt1,4, David B Grayden1,4
From 24th Annual Computational Neuroscience Meeting: CNS*2015
Prague, Czech Republic. 18-23 July 2015
The main challenge for understanding motion proces-
sing in the visual system is that local motion signals do
not accurately represent the direction of the whole
object. This is known as the “aperture problem”. The
small receptive fields of individual neurons can only
measure motion components orthogonal to their pre-
ferred direction. Neuro-physiological studies show that
neurons in brain area MT (V5) have a major role in
dealing with this problem [1]. We develop a model to
overcome the aperture problem through excitatory and
inhibitory interconnections between MT neurons.
The model contains two levels of rate-based neuron.
The first level is a model of V1 with two sets of neurons:
complex neurons and hyper-complex end-stopped neu-
rons. End-stopped neurons respond to the end-points of
the stimulus , thereby representing an unambiguous esti-
mation of the direction of motion [2]. The response of
V1 neurons is transmitted to the second level, the model
of MT neurons. MT neurons are categorized as integra-
tion and segmentation neurons. The role of integration
neurons is to integrate local motion signals to solve the
aperture problem; the role of segmentation neurons is to
detect discontinuities in the input stimulus to inhibit
integration neurons [3].
Excitatory recurrent connections between integration
neurons propagate end-stopped information from the
object terminators. As each spatial location contains
neurons selective to different directions, inhibitory con-
nections are added between the neurons such that there
will be only one neuron active at each spatial location.
Long-range inhibitory connections are also added
between more distant MT neurons selective to other
directions. Segmentation neurons send inhibitory
connections to integration neurons to prevent the pro-
pagation of motion signals outside the border of the
input stimulus. Inhibitory connections between segmen-
tation neurons are defined based on center-surround
interactions; this suppresses activity when the motion
signals in the center and surround are in the same
directions; ensuring that their activity will not prevent
the propagation of motion signals when there is no
discontinuity.
The results of simulations show that the activity of V1
neurons along the edges is much stronger than the activ-
ity of the neurons at the end-points. Activation of end-
stopped neurons in V1 suppresses ambiguous motion
signals along the edges of the stimulus. Therefore, unam-
biguous information at the end-points wins the competi-
tion over the neurons with ambiguous motion signals.
The final output of the model shows that MT neurons
achieve an accurate estimation of motion using the infor-
mation provided by the end-stopped neurons.
We developed a model of MT neurons for motion per-
ception based on the available neuro-physiological data.
These neurons receive their input from neurons in V1.
The results show the successful function of these neurons
in the perception of motion of a single moving bar. In this
model, we also investigated the role of end-stopped neu-
rons in overcoming the aperture problem. The results sug-
gest that MT neurons are not able to solve the aperture
problem without the vital role played by end-stopped cells.
Acknowledgements
The Bionics Institute acknowledges the support it receives from the Victorian
Government through its Operational Infrastructure Support Program. This
research was supported by the Australian Research Council through its
Special Research Initiative in Bionic Vision Science and Technology grant to
Bionic Vision Australia.
Authors’ details
1NeuroEngineering Lab, Dept Electrical & Electronic Engineering, University
of Melbourne, Melbourne, VIC 3010, Australia. 2NICTA, Sydney, Australia.
* Correspondence: parvinz@student.unimelb.edu.au
1NeuroEngineering Lab, Dept Electrical & Electronic Engineering, University
of Melbourne, Melbourne, VIC 3010, Australia
Full list of author information is available at the end of the article
Zarei et al. BMC Neuroscience 2015, 16(Suppl 1):P86
http://www.biomedcentral.com/1471-2202/16/S1/P86
© 2015 Zarei et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://
creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/
zero/1.0/) applies to the data made available in this article, unless otherwise stated.
3National Vision Research Institute, Australian College of Optometry,
Melbourne, VIC 3053, Australia. 4Bionics Institute, Melbourne, VIC 3002,
Australia.
Published: 18 December 2015
References
1. Born R, Bradley D: Structure and function of visual area MT. Ann. Rev.
Neurosci 2005, 28:157-189.
2. Tsui JMG, Hunter JN, Born R, Pack C: The role of V1 surround suppression
in MT motion integration. J. Neurophy 2010, 103(6):3123-3138.
3. Liden L, Pack C: The role of terminators and occlusion cues in motion
integration and segmentation: a neural network model. Vis. Res 1999,
39(19):3301-3320.
doi:10.1186/1471-2202-16-S1-P86
Cite this article as: Zarei et al.: The interaction between integration and
segmentation neurons for motion perception. BMC Neuroscience 2015
16(Suppl 1):P86.
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The interaction between integration and segmentation neurons for motion perception

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