  The increasing need for intelligent sensors in a wide range of everyday
objects requires the existence of low power information processing systems
which can operate autonomously in their environment. In particular, merging and
processing the outputs of different sensors efficiently is a necessary
requirement for mobile agents with cognitive abilities. In this work, we
present a multi-layer spiking neural network for inference of relations between
stimuli patterns in dedicated neuromorphic systems. The system is trained with
a new version of the backpropagation algorithm adapted to on-chip learning in
neuromorphic hardware: Error gradients are encoded as spike signals which are
propagated through symmetric synapses, using the same integrate-and-fire
hardware infrastructure as used during forward propagation. We demonstrate the
strength of the approach on an arithmetic relation inference task and on visual
XOR on the MNIST dataset. Compared to previous, biologically-inspired
implementations of networks for learning and inference of relations, our
approach is able to achieve better performance with less neurons. Our
architecture is the first spiking neural network architecture with on-chip
learning capabilities, which is able to perform relational inference on complex
visual stimuli. These features make our system interesting for sensor fusion
applications and embedded learning in autonomous neuromorphic agents.
