Criticality Analysis: Bio-inspired Nonlinear Data Representation
This provides a biologically relevant encoding mechanism for arbitrary input to biosystems, potentially modeling information processing across organisms and offering scale-free data representation for machine learning.
The paper tackles the problem of representing arbitrary data in biological systems by introducing Criticality Analysis (CA), a bio-inspired method that uses controlled self-organized criticality to achieve scale-free data representation, preserving data similarity in a multidimensional neighborhood while enabling dimensional reduction.
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.