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Biological image processing is performed by complex neural networks composedof thousands of neurons interconnected via thousands of synapses, some of whichare excitatory and others inhibitory. Spiking neural models are distinguishedfrom classical neurons by being biological plausible and exhibiting the samedynamics as those observed in biological neurons. This paper proposes a NaturalConvolutional Neural Network (NatCSNN) which is a 3-layer bio-inspiredConvolutional Spiking Neural Network (CSNN), for classifying objects extractedfrom natural images. A two-stage training algorithm is proposed usingunsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) andReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on theCIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is animprovement over the 2-layer neural networks previously applied to thisdataset.

NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images