English
资源说明：Spiking neural networks (SNNs) have advantages
over traditional, non-spiking networks with respect to bio-
realism, potential for low-power hardware implementations, and
theoretical computing power. However, in practice, spiking net-
works with multi-layer learning have proven difficult to train.
This paper explores a novel, bio-inspired spiking convolutional
neural network (CNN) that is trained in a greedy, layer-wise
fashion. The spiking CNN consists of a convolutional/pooling
layer followed by a feature discovery layer, both of which
undergo bio-inspired learning. Kernels for the convolutional layer
are trained using a sparse, spiking auto-encoder representing
primary visual features. The feature discovery layer uses a
probabilistic spike-timing-dependent plasticity (STDP) learning
rule. This layer represents complex visual features using WTA-
thresholded, leaky, integrate-and-fire (LIF) neurons. The new
model is evaluated on the MNIST digit dataset using clean and
noisy images. Intermediate results show that the convolutional
layer is stack-admissible, enabling it to support a multi-layer
learning architecture. The recognition performance for clean
images is above 98%. This performance is accounted for by
the independent and informative visual features extracted in
a hierarchy of convolutional and feature discovery layers. The
performance loss for recognizing the noisy images is in the range
0.1% to 8.5%. This level of performance loss indicates that the
network is robust to additive noise
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