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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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CiteScore 2017: 0.33

SCImago Journal Rank (SJR) 2017: 0.104

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2081-4836
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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

Abstract

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

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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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© by Fernanda da C. e C. Faria, published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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