  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 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.
