  Hierarchical feature discovery using non-spiking convolutional neural
networks (CNNs) has attracted much recent interest in machine learning and
computer vision. However, it is still not well understood how to create a
biologically plausible network of brain-like, spiking neurons with multi-layer,
unsupervised learning. This paper explores a novel bio-inspired spiking CNN
that is trained in a greedy, layer-wise fashion. The proposed network consists
of a spiking convolutional-pooling layer followed by a feature discovery layer
extracting independent visual features. Kernels for the convolutional layer are
trained using local learning. The learning is implemented using a sparse,
spiking auto-encoder representing primary visual features. The feature
discovery layer extracts independent features by probabilistic, leaky
integrate-and-fire (LIF) neurons that are sparsely active in response to
stimuli. The layer of the probabilistic, LIF neurons implicitly provides
lateral inhibitions to extract sparse and independent features. Experimental
results show that the convolutional layer is stack-admissible, enabling it to
support a multi-layer learning. The visual features obtained from the proposed
probabilistic LIF neurons in the feature discovery layer are utilized for
training a classifier. Classification results contribute to the independent and
informative visual features extracted in a hierarchy of convolutional and
feature discovery layers. The proposed model is evaluated on the MNIST digit
dataset using clean and noisy images. The recognition performance for clean
images is above 98%. The performance loss for recognizing the noisy images is
in the range 0.1% to 8.5% depending on noise types and densities. This level of
performance loss indicates that the network is robust to additive noise.
