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An insect-inspired model for visual binding II: functional analysis and visual attention

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Abstract

We have developed a neural network model capable of performing visual binding inspired by neuronal circuitry in the optic glomeruli of flies: a brain area that lies just downstream of the optic lobes where early visual processing is performed. This visual binding model is able to detect objects in dynamic image sequences and bind together their respective characteristic visual features—such as color, motion, and orientation—by taking advantage of their common temporal fluctuations. Visual binding is represented in the form of an inhibitory weight matrix which learns over time which features originate from a given visual object. In the present work, we show that information represented implicitly in this weight matrix can be used to explicitly count the number of objects present in the visual image, to enumerate their specific visual characteristics, and even to create an enhanced image in which one particular object is emphasized over others, thus implementing a simple form of visual attention. Further, we present a detailed analysis which reveals the function and theoretical limitations of the visual binding network and in this context describe a novel network learning rule which is optimized for visual binding.

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Acknowledgements

The authors would like to thank the Air Force Office of Scientific Research for early support of the modeling research on this project with Grant Number FA9550-07-1-0165, and the Air Force Research Laboratories for supporting this research to maturity with STTR Phase I Award Number FA8651-13-M-0085 and Phase II Award Number FA8651-14-C-0108, both in collaboration with Spectral Imaging Laboratory (Pasadena, CA). We would also like to thank the reviewers, whose input greatly enhanced this manuscript.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Arizona, 1230 E. Speedway Blvd., Tucson, AZ, 85721, USA

    Brandon D. Northcutt

  2. Departments of Neuroscience and Electrical/Computer Eng., University of Arizona, 1040 E. 4th St., Tucson, AZ, 85721, USA

    Charles M. Higgins

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  1. Brandon D. Northcutt
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  2. Charles M. Higgins
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Correspondence to Brandon D. Northcutt.

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Northcutt, B.D., Higgins, C.M. An insect-inspired model for visual binding II: functional analysis and visual attention. Biol Cybern 111, 207–227 (2017). https://doi.org/10.1007/s00422-017-0716-z

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  • DOI: https://doi.org/10.1007/s00422-017-0716-z

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