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5 DISCUSSION AND FUTURE
WORK
The spiking neural network presented in this paper is
constructed using a hierarchical structure that is
composed of spiking neurons with various receptive
fields. The input image is converted to retinal
ganglion cell output spike trains by convolving with
DoG filters. The spike trains are presented to the
network and the various receptive fields process the
image, performing edge detection and corner
detection. The spiking neuron models provide
powerful functionality for integration of inputs and
generation of spikes. Synapses are able to perform
different complicated computations. This paper
demonstrates how a spiking neural network can
detect edge and corners in an image. The
performance illustrates that the proposed detector is
currently only capable of detecting simple edges at
specific orientations and similarly only particular
corner types. However, the current results appear
promising when compared with the standard Harris
approach to corner detection. Future work will
involve the incorporation of biologically plausible
unsupervised learning algorithms (STDP) to set the
synaptic weights, automatic development of
receptive fields to deal with different edge and
corner types.
ACKNOWLEDGEMENTS
This work was supported by the Centre of
Excellence in Intelligent Systems project, funded by
InvestNI and the Integrated Development Fund.
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