  Predictive coding (PDC) has recently attracted attention in the neuroscience
and computing community as a candidate unifying paradigm for neuronal studies
and artificial neural network implementations particularly targeted at
unsupervised learning systems. The Mismatch Negativity (MMN) has also recently
been studied in relation to PC and found to be a useful ingredient in neural
predictive coding systems. Backed by the behavior of living organisms, such
networks are particularly useful in forming spatio-temporal transitions and
invariant representations of the input world. However, most neural systems
still do not account for large number of synapses even though this has been
shown by a few machine learning researchers as an effective and very important
component of any neural system if such a system is to behave properly. Our
major point here is that PDC systems with the MMN effect in addition to a large
number of synapses can greatly improve any neural learning system's performance
and ability to make decisions in the machine world. In this paper, we propose a
novel bio-mimetic computational intelligence algorithm -- the Deviant Learning
Algorithm, inspired by these key ideas and functional properties of recent
brain-cognitive discoveries and theories. We also show by numerical experiments
guided by theoretical insights, how our invented bio-mimetic algorithm can
achieve competitive predictions even with very small problem specific data.
