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A fundamental problem in the field of unsupervised machine learning is thedetection of anomalies corresponding to rare and unusual observations ofinterest; reasons include for their rejection, accommodation or furtherinvestigation. Anomalies are intuitively understood to be something unusual orinconsistent, whose occurrence sparks immediate attention. More formallyanomalies are those observations-under appropriate random variablemodelling-whose expectation of occurrence with respect to a grouping of priorinterest is less than one; such a definition and understanding has been used todevelop the parameter-free perception anomaly detection algorithm. The presentwork seeks to establish important and practical connections between theapproach used by the perception algorithm and prior decades of research inneurophysiology and computational neuroscience; particularly that ofinformation processing in the retina and visual cortex. The algorithm isconceptualised as a neuron model which forms the kernel of an unsupervisedneural network that learns to signal unexpected observations as anomalies. Boththe network and neuron display properties observed in biological processesincluding: immediate intelligence; parallel processing; redundancy; globaldegradation; contrast invariance; parameter-free computation, dynamicthresholds and non-linear processing. A robust and accurate model for anomalydetection in univariate and multivariate data is built using this network as aconcrete application.

A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data