  We introduce a novel spiking neural network model for learning distributed
internal representations from data in an unsupervised procedure. We achieved
this by transforming the non-spiking feedforward Bayesian Confidence
Propagation Neural Network (BCPNN) model, employing an online correlation-based
Hebbian-Bayesian learning and rewiring mechanism, shown previously to perform
representation learning, into a spiking neural network with Poisson statistics
and low firing rate comparable to in vivo cortical pyramidal neurons. We
evaluated the representations learned by our spiking model using a linear
classifier and show performance close to the non-spiking BCPNN, and competitive
with other Hebbian-based spiking networks when trained on MNIST and F-MNIST
machine learning benchmarks.
