  The representation of arbitrary data in a biological system is one of the
most elusive elements of biological information processing. The often
logarithmic nature of information in amplitude and frequency presented to
biosystems prevents simple encapsulation of the information contained in the
input. Criticality Analysis (CA) is a bio-inspired method of information
representation within a controlled self-organised critical system that allows
scale-free representation. This is based on the concept of a reservoir of
dynamic behaviour in which self-similar data will create dynamic nonlinear
representations. This unique projection of data preserves the similarity of
data within a multidimensional neighbourhood. The input can be reduced
dimensionally to a projection output that retains the features of the overall
data, yet has much simpler dynamic response. The method depends only on the
rate control of chaos applied to the underlying controlled models, that allows
the encoding of arbitrary data, and promises optimal encoding of data given
biological relevant networks of oscillators. The CA method allows for a
biologically relevant encoding mechanism of arbitrary input to biosystems,
creating a suitable model for information processing in varying complexity of
organisms and scale-free data representation for machine learning.
