Artificial Neural Network (ANN) based techniques have dominated
state-of-the-art results in most problems related to computer vision, audio
recognition, and natural language processing in the past few years, resulting
in strong industrial adoption from all leading technology companies worldwide.
One of the major obstacles that have historically delayed large scale adoption
of ANNs is the huge computational and power costs associated with training and
testing (deploying) them. In the mean-time, Neuromorphic Computing platforms
have recently achieved remarkable performance running more bio-realistic
Spiking Neural Networks at high throughput and very low power consumption
making them a natural alternative to ANNs. Here, we propose using the Random
Neural Network (RNN), a spiking neural network with both theoretical and
practical appealing properties, as a general purpose classifier that can match
the classification power of ANNs on a number of tasks while enjoying all the
features of a spiking neural network. This is demonstrated on a number of
real-world classification datasets.