  Spiking neural networks are motivated from principles of neural systems and
may possess unexplored advantages in the context of machine learning. A class
of \textit{convolutional spiking neural networks} is introduced, trained to
detect image features with an unsupervised, competitive learning mechanism.
Image features can be shared within subpopulations of neurons, or each may
evolve independently to capture different features in different regions of
input space. We analyze the time and memory requirements of learning with and
operating such networks. The MNIST dataset is used as an experimental testbed,
and comparisons are made between the performance and convergence speed of a
baseline spiking neural network.
