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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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CiteScore 2018: 2.17

SCImago Journal Rank (SJR) 2018: 0.336
Source Normalized Impact per Paper (SNIP) 2018: 1.707

ICV 2018: 120.52

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Biologically inspired computational modeling of motion based on middle temporal area

Fernanda da C. e C. Faria / Jorge Batista / Helder Araújo
Published Online: 2018-04-13 | DOI: https://doi.org/10.1515/pjbr-2018-0005


This paper describes a bio-inspired algorithm for motion computation based on V1 (Primary Visual Cortex) andMT (Middle Temporal Area) cells. The behavior of neurons in V1 and MT areas contain significant information to understand the perception of motion. From a computational perspective, the neurons are treated as two dimensional filters to represent the receptive fields of simple cells that compose the complex cells. A modified elaborated Reichardt detector, adding an output exponent before the last stage followed by a re-entry stage of modulating feedback from MT, (reciprocal connections of V1 and MT) in a hierarchical framework, is proposed. The endstopped units, where the receptive fields of cells are surrounded by suppressive regions, are modeled as a divisive operation. MT cells play an important role for integrating and interpreting inputs from earlier-level (V1).We fit a normalization and a pooling to find the most active neurons for motion detection. All steps employed are physiologically inspired processing schemes and need some degree of simplification and abstraction. The results suggest that our proposed algorithm can achieve better performance than recent state-of-the-art bio-inspired approaches for real world images.

Keywords: Motion Direction; Neural Computational Model; Area MT


  • [1] F. d. C. e. C. Faria, J. Batista, H. Araújo, Stereoscopic Depth Perception Using a Model Based on the Primary Visual Cortex, PLoS ONE, 2013, 8(12), e80745Google Scholar

  • [2] M. Antonelli, A. Gibaldi, F. Beuth, A. J. Duran, A. Canessa, M. Chessa, F. Solari, A. P. del Pobil, F. Hamker, E. Chinellato, S. P. Sabatini, A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot, IEEE Transactions on Autonomous Mental Development, 2014, 6, 259-273Google Scholar

  • [3] J. H. Maunsell, D. C. van Essen, The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey, The Journal of Neuroscience, 1983, 3, 2563-2586CrossrefGoogle Scholar

  • [4] J. H. R. Maunsell, D. C. van Essen, Functional Properties of Neurons in Middle Temporal Visual Area of the Macaque Monkey. I. Selectivity for Stimulus Direction, Speed, and Orientation, Journal of Neurophysiology, 1983, 49, 1127-1147Google Scholar

  • [5] J. A. Movshon, W. T. Newsome, Visual Response Properties of Striate Cortical Neurons Projecting to AreaMT inMacaque Monkeys, The Journal of Neuroscience, 1996, 16, 7733-7741CrossrefGoogle Scholar

  • [6] J. H. R. Maunsell, D. C. van Essen, Functional Properties of Neurons in Middle Temporal Visual Area of the Macaque Monkey. II. Binocular Interactions and Sensitivity to Binocular Disparity, Journal of Neurophysiology, 1983, 49, 1148-1167Google Scholar

  • [7] J. Allman, F. Miezin, E. McGuinness, Direction- and velocityspecific responses from beyond the classical receptive field in the middle temporal visual area (MT), Perception, 1985, 14, 105- 126CrossrefGoogle Scholar

  • [8] R. T. Born, Center-Surround Interactions in the Middle Temporal Visual Area of the Owl Monkey, Journal of Neurophysiology, 2000, 84, 2658-2669Google Scholar

  • [9] L. L. Lui, J. A. Bourne, M. G. P. Rosa, Spatial Summation, End Inhibition and Side Inhibition in the Middle Temporal Visual Area (MT), Journal of Neurophysiology, 2007, 97, 1135-1148CrossrefGoogle Scholar

  • [10] G. C. DeAngelis, R. D. Freeman, I. Ohzawa, Length and Width Tuning of Neurons in the Cat’s Primary Visual Cortex, Journal of Neurophysiology, 1994, 71, 347-374Google Scholar

  • [11] R. T. Born, D. C. Bradley, Structure and Function of Visual Area MT, Annual Review Neuroscience, 2005, 28, 157-189 CrossrefGoogle Scholar

  • [12] D. G. Albrecht,W. S. Geisler, Motion selectivity and the contrastresponse function of simple cells in the visual cortex, Visual Neuroscience, 1991, 7, 531-546CrossrefGoogle Scholar

  • [13] T. Poggio,W. Reichardt, Considerations on Models of Movement Detection, Kybernetik, 1973, 13, 223-227CrossrefGoogle Scholar

  • [14] J. P. H. van Santen, G. Sperling, Elaborated Reichardt detectors, Journal of the Optical Society of America A, 1985, 2(2), 300-321CrossrefGoogle Scholar

  • [15] W. Reichardt, Evaluation of optical motion information by movement detectors, Journal of Comparative Physiology A, 1987, 161, 533-547Google Scholar

  • [16] E. H. Adelson, J. R. Bergen, Spatiotemporal energy models for the perception of motion, Journal of the Optical Society of America A, 1985, 2(2), 284-299Google Scholar

  • [17] A. B.Watson, A. J. A. Jr., Model of human visual-motion sensing, Journal of the Optical Society of America A, 1985, 2(2), 322-342 Google Scholar

  • [18] N. C. Rust, V.Mante, E. P. Simoncelli, J. A. Movshon, HowMT cells analyze the motion of visual patterns, Nature Neuroscience, 2006, 9(11), 1421-1431Google Scholar

  • [19] M. Silies, D. M. Gohl, T. R. Clandinin, Motion-Detecting Circuits in Flies: Coming into View, Annual Review of Neuroscience, 2014, 37, 307-327Google Scholar

  • [20] A. Borst, M. Egelhaaf, Detection Visual Motion: Theory and Models, in F. A. Miles, J. Wallman (Ed.), Visual Motion and Its Role in the Stabilization of Gaze, Elsevier Science Publishers B.V., 1993Google Scholar

  • [21] B. Krekelberg, Motion Detection Mechanisms, in A. Basbaumet al (Ed.), The Senses: A Comprehensive Reference, Elsevier Inc, Oxford, 2008Google Scholar

  • [22] A. Borst, T. Euler, Seeing Things in Motion: Models, Circuits, and Mechanisms, Neuron, 2011, 71, 974-994Google Scholar

  • [23] B. Hassenstein, W. Reichardt, Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus., Z Naturforsch, 1956, 11b, 513-524Google Scholar

  • [24] N. Petkov, E. Subramanian, Motion detection, noise reduction, texture suppression and contour enhancement by spatiotemporal Gabor filters with surround inhibition, Biological Cybernetics, 2007, 97, 423-439CrossrefGoogle Scholar

  • [25] F. Raudies, H. Neumann, A Bio-Inspired, Motion-Based Analysis of Crowd Behavior Attributes Relevance to Motion Transparency, Velocity Gradients, and Motion Patterns, PLos ONE, 2012, 7(12), e53456Google Scholar

  • [26] S. Jain, Performance Characterization of Watson Ahumada Motion Detector Using Random Dot Rotary Motion Stimuli, PLos ONE, 2009, 4(2), e4536Google Scholar

  • [27] A. Pavan, A. Contillo, G. Mather, Modelling adaptation to directional motion using the Adelson-Bergen energy sensor, PLos ONE, 2013, 8(3), e59298Google Scholar

  • [28] M.-J. Escobar, P. Kornprobst, Action recognition via bio-inspired features: The richness of center-surround interaction, Computer Vision and Image Understanding, 2012, 116, 593-605Google Scholar

  • [29] R. M. Haefner, B. G. Cumming, Adaptation to natural binocular disparities in primate V1 explained by a generalized energy model, Neuron, 2008, 57, 147-158Google Scholar

  • [30] A. Borst, J. Haag, D. F. Reiff, Fly Motion Vision, Annual Review of Neuroscience, 2010, 33, 49-70Google Scholar

  • [31] N. C. Rust, O. Schwartz, J. A. Movshon, E. P. Simoncelli, Spatiotemporal Elements of Macaque V1 Receptive Fields, Neuron, 2005, 46, 945-956Google Scholar

  • [32] J. R. Cavanaugh, W. Bair, A. Movshon, Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons, Journal of Neurophysiology, 2002, 88, 2530-2546Google Scholar

  • [33] D. J. Heeger, Normalization of cell responses in cat striate cortex, Visual Neuroscience, 1992, 9, 181-197 CrossrefGoogle Scholar

  • [34] E. P. Simoncelli, D. J. Heeger, A model of neuronal responses in visual area MT, Vision Research, 1998, 38, 743-761CrossrefGoogle Scholar

  • [35] J. M. G. Tsui, J. N. Hunter, R. T. Born, C. C. Pack, The Role of V1 Surround Suppression inMT Motion Integration, Journal of Neurophysiology, 2010, 103, 3123-3138Google Scholar

  • [36] C. C. Pack, M. S. Livingstone, K. R. Duffy, R. T. Born, End- Stopping and the Aperture Problem: Two-Dimensional Motion Signals in Macaque V1, Neuron, 2003, 39, 671-680Google Scholar

  • [37] D. J. Field, Relations between the statistics of natural images and the response properties of cortical cells, Journal of the Optical Society of America A, 1987, 4, 2379-2394Google Scholar

  • [38] P. Kovesi, Image features from phase congruency, Journal of Computer Vision Research, 1999, 1, 1-26Google Scholar

  • [39] F. Raudies, E. Mingolla, H. Neumann, A Model of Motion Transparency Processing with Local Center-Surround Interactions and Feedback, Neural Computation, 2011, 23, 2868-2914CrossrefGoogle Scholar

  • [40] F. Mechler, D. S. Reich, J. D. Victor, Detection and Discrimination of Relative Spatial Phase by V1 Neurons, The Journal of Neuroscience, 2002, 22(14), 6129-6157CrossrefGoogle Scholar

  • [41] M. Carandini, D. J. Heeger, Summation and division by neurons in primate visual cortex, Science, 1994, 264(5163), 1333-1336Google Scholar

  • [42] C. Koch, Biophysics of Computation: Information Processing in Single Neurons, 2004Google Scholar

  • [43] A. L. Hodgkin, A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, 1952, 117(4), 500-544Google Scholar

  • [44] P. Bayerl, H. Neumann, Disambiguating Visual Motion Through Contextual Feedback Modulation, Neural Computation, 2004, 16, 2041-2066CrossrefGoogle Scholar

  • [45] T. Brosch, H. Neumann, Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback, Neural Computation, 2014, 26, 2735-2789 CrossrefGoogle Scholar

  • [46] C. Beck, H. Neumann, Combining Feature Selection and Integration - A Neural Model forMT Motion Selectivity, PLoS ONE, 2011, 6(7), e21254Google Scholar

  • [47] F. Raudies, H. Neumann, Developing and Applying Biologically- Inspired Vision Systems: Interdisciplinary Concepts., 2012, 121-153Google Scholar

  • [48] J. D. Bouecke, E. Tlapale, P. Kornprobst, H. Neumann, Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems, EURASIP Journal on Advances in Signal Processing 2011, 2011CrossrefGoogle Scholar

  • [49] A. Borst, M. Egelhaaf, J. Haag, Mechanisms of Dendritic Integration Underlying Gain Control in Fly Motion-Sensitive Interneurons, Journal of Computational Neuroscience, 1995, 2, 5-18 Google Scholar

  • [50] M.Maruyama, D. D. Palomo, A. A. Ioannides, Stimulus-Contrast- Induced Biases in Activation Order Reveal Interaction Between V1/V2 and Human MT+, Human Brain Mapping, 2009, 30, 147-162CrossrefGoogle Scholar

  • [51] A. M. Sillito, J. Cudeiro, H. E. Jones, Always Returning: Feedback and Sensory Processing in Visual Cortex and Thalamus, Trends in Neurosciences, 2006, 29(6), 307-316CrossrefGoogle Scholar

  • [52] J.-M. Hupé, A. C. James, P. Girard, S. G. Lomber, B. R. Payne, J. Bullier, Feedback Connections Act on the Early Part of the Responses in Monkey Visual Cortex, Journal of Neurophysiology, 2001, 85, 134-145Google Scholar

  • [53] A. Angelucci, J. B. Levitt, E. J. S.Walton, J.-M. Hupé, J. Bullier, J. S. Lund, Circuits for Local and Global Signal Integration in Primary Visual Cortex, The Journal of Neuroscience, 2002, 22(19), 8633- 8646Google Scholar

  • [54] J. M. G. Tsui, C. C. Pack, Contrast sensitivity ofMT receptive field centers and surrounds, Journal of Neurophysiology, 2011, 106, 1888-1900Google Scholar

  • [55] S. Deneve, P. E. Latham, A. Pouget, Reading population codes: a neural implementation of ideal observers, Nature Neuroscience, 1999, 2(8), 740-745CrossrefGoogle Scholar

  • [56] S. Treue, K. Hol, H.-J. Rauber, Seeing multiple directions of motion - physiology and psychophysics, Nature Neuroscience, 2000, 3(3), 270-276Google Scholar

  • [57] A. Pouget, P. Dayan, R. Zemel, Information processing with population codes, Nature Reviews Neuroscience, 2000, 1, 125-132Google Scholar

  • [58] S. Baker, D. Scharstein, J. P. Lewis, IEEE International Conference on Computer Vision, 2007Google Scholar

  • [59] S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, R. Szeliski, A database and evaluation methodology for optical flow, International Journal of Computer Vision, 2011, 92, 1-31Google Scholar

  • [60] J. L. Barron, D. J. Fleet, S. S. Beauchemin, Performance of optical flow techniques, International Journal of Computer Vision, 1994, 12, 43-77Google Scholar

  • [61] M. Otte, H.-H. Nagel, European Conference on Computer Vision, 1994, 49-60Google Scholar

  • [62] D. J. Fleet, A. D. Jepson, Computation of component image velocity from local phase information, International Journal of Computer Vision, 1990, 5, 77-104CrossrefGoogle Scholar

  • [63] F. Solari, M. Chessa, S. P. Sabatini, An integrated neuromimetic architecture for direct motion interpretation in the log-polar domain, Computer Vision and Image Understanding, 2014, 125, 37-54Google Scholar

  • [64] B. Dellen, F. Wörgötter, A local algorithm for the computation of image velocity via constructive interference of global Fourier components, International Journal of Computer Vision, 2011, 92, 53-70CrossrefGoogle Scholar

  • [65] F. Solari, M. Chessa, N. V. K. Medathati, P. Kornprobst, What can we expect from a V1-MT feedforward architecture for optical flow estimation?, Signal Processing: Image Communication, 2015Google Scholar

  • [66] P. Lennie, Single units and visual cortical organization, Perception, 1998, 27, 889-935CrossrefGoogle Scholar

About the article

Received: 2017-11-30

Accepted: 2018-03-03

Published Online: 2018-04-13

Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 9, Issue 1, Pages 60–71, ISSN (Online) 2081-4836, DOI: https://doi.org/10.1515/pjbr-2018-0005.

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© 2018 Fernanda da C. e C. Faria et al. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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