  A fundamental problem in the field of unsupervised machine learning is the
detection of anomalies corresponding to rare and unusual observations of
interest; reasons include for their rejection, accommodation or further
investigation. Anomalies are intuitively understood to be something unusual or
inconsistent, whose occurrence sparks immediate attention. More formally
anomalies are those observations-under appropriate random variable
modelling-whose expectation of occurrence with respect to a grouping of prior
interest is less than one; such a definition and understanding has been used to
develop the parameter-free perception anomaly detection algorithm. The present
work seeks to establish important and practical connections between the
approach used by the perception algorithm and prior decades of research in
neurophysiology and computational neuroscience; particularly that of
information processing in the retina and visual cortex. The algorithm is
conceptualised as a neuron model which forms the kernel of an unsupervised
neural network that learns to signal unexpected observations as anomalies. Both
the network and neuron display properties observed in biological processes
including: immediate intelligence; parallel processing; redundancy; global
degradation; contrast invariance; parameter-free computation, dynamic
thresholds and non-linear processing. A robust and accurate model for anomaly
detection in univariate and multivariate data is built using this network as a
concrete application.
