Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer\nvision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced\ncomputation complexity. SNN have been successfully used for image classification. They provide a model for the\nmammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical\nmodels exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a\nnovel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time\ndependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data\nrepresentation; images are encoded by the network and processed in a similar manner as the primary layers in visual\ncortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple\nobject classifier. Results show that the model can successfully learn and classify black and white images with added\nnoise or partially obscured samples with up to Ã?â??20 computing speed-up at an equivalent classification ratio when\ncompared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network\ntopology, neuron membrane model and STDP learning.
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