  Biological image processing is performed by complex neural networks composed
of thousands of neurons interconnected via thousands of synapses, some of which
are excitatory and others inhibitory. Spiking neural models are distinguished
from classical neurons by being biological plausible and exhibiting the same
dynamics as those observed in biological neurons. This paper proposes a Natural
Convolutional Neural Network (NatCSNN) which is a 3-layer bio-inspired
Convolutional Spiking Neural Network (CSNN), for classifying objects extracted
from natural images. A two-stage training algorithm is proposed using
unsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) and
ReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on the
CIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is an
improvement over the 2-layer neural networks previously applied to this
dataset.
