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Identifying mirror symmetry density with delay in spiking neural networks (Conference Presentation)
Author(s): Jonathan K. George; Cesare Soci; Volker J. Sorger
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Paper Abstract

The ability to rapidly identify symmetry and anti-symmetry is an essential attribute of intelligence. Symmetry perception is a central process in human vision and may be key to human 3D visualization. While previous work in understanding neuron symmetry perception has concentrated on the neuron as an integrator, here we show how the coincidence detecting property of the spiking neuron can be used to reveal symmetry density in spatial data. We develop a method for synchronizing symmetry-identifying spiking artificial neural networks to enable layering and feedback in the network. We show a method for building a network capable of identifying symmetry density between sets of data and present a digital logic implementation demonstrating an 8x8 leaky-integrate-and-fire (LIF) symmetry detector in a field programmable gate array. Our results show that the efficiencies of spiking neural networks can be harnessed to rapidly identify symmetry in spatial data with applications in image processing, 3D computer vision, and robotics.In conclusion, we have presented a novel algorithm for finding a scalar field representing the symmetry of points in a multi-dimensional space. We have shown how time synchronization in the input values of spiking neural networks, with the appropriate choice of threshold and spike period, results in the identification of output neurons along points of high symmetry density to the network inputs. We have demonstrated an implementation of the symmetry selective LIF neural network in common hardware with a high speed, 2.8 MHz identification of symmetry points in an 8x8 Manhattan metric space. Our results show that utilizing only the delay and coincidence detecting properties of a single layer of neurons in spiking neural networks naturally lead to effective symmetry identification. A greater understanding of symmetry perception in artificial intelligences will lead to systems with more effective pattern visualization, compression, and goal setting processes.

Paper Details

Date Published: 17 September 2018
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Proc. SPIE 10751, Optics and Photonics for Information Processing XII, 107510L (17 September 2018); doi: 10.1117/12.2322083
Show Author Affiliations
Jonathan K. George, The George Washington Univ. (United States)
Cesare Soci, Nanyang Technological Univ. (Singapore)
Volker J. Sorger, The George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 10751:
Optics and Photonics for Information Processing XII
Abdul A. S. Awwal; Khan M. Iftekharuddin; Mireya García Vázquez, Editor(s)

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