Image mirror symmetry density enabled by delay and coincidence detection in spiking neural networks (Conference Presentation)

Volker J. Sorger

doi:10.1117/12.2559081

27 April 2020 Image mirror symmetry density enabled by delay and coincidence detection in spiking neural networks (Conference Presentation)

Volker J. Sorger

Author Affiliations +

Proceedings Volume 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX; 1142315 (2020) https://doi.org/10.1117/12.2559081
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Neuromorphic computing has made tremendous advances in computing efficiency by modeling computers after the brain primarily by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation.

Conference Presentation

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Volker J. Sorger "Image mirror symmetry density enabled by delay and coincidence detection in spiking neural networks (Conference Presentation)", Proc. SPIE 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 1142315 (27 April 2020); https://doi.org/10.1117/12.2559081

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KEYWORDS

Neural networks

Mirrors

Neurons

3D image processing

Artificial neural networks

Brain

Computing systems

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