  Perceptual learning enables humans to recognize and represent stimuli
invariant to various transformations and build a consistent representation of
the self and physical world. Such representations preserve the invariant
physical relations among the multiple perceived sensory cues. This work is an
attempt to exploit these principles in an engineered system. We design a novel
neural network architecture capable of learning, in an unsupervised manner,
relations among multiple sensory cues. The system combines computational
principles, such as competition, cooperation, and correlation, in a neurally
plausible computational substrate. It achieves that through a parallel and
distributed processing architecture in which the relations among the multiple
sensory quantities are extracted from time-sequenced data. We describe the core
system functionality when learning arbitrary non-linear relations in
low-dimensional sensory data. Here, an initial benefit rises from the fact that
such a network can be engineered in a relatively straightforward way without
prior information about the sensors and their interactions. Moreover,
alleviating the need for tedious modelling and parametrization, the network
converges to a consistent description of any arbitrary high-dimensional
multisensory setup. We demonstrate this through a real-world learning problem,
where, from standard RGB camera frames, the network learns the relations
between physical quantities such as light intensity, spatial gradient, and
optical flow, describing a visual scene. Overall, the benefits of such a
framework lie in the capability to learn non-linear pairwise relations among
sensory streams in an architecture that is stable under noise and missing
sensor input.
