Classical information theory ties information with physical representations.
Subcellular processes generate and store information, but only integrated cellular life
actually transforms these processes into biologically meaningful information. Living
organisms, as open systems, extract biological order from the predictably increased
disorder of their environment. They transform energy into a new form of energy with
higher thermodynamic value. The negative entropy results from the accumulation of
information within the living system. More information translates into greater
molecular complexity and functional dynamic behavior. Time-organized nonlinear
processes lead to the expansion of expectations. Natural selection necessarily results in
the buildup, within cellular processes, of information about environmental states and
their inferred causal architecture. Such information increases the likelihood that a given
organism will navigate and react more efficiently within its environment. The same
holds true for brain functions that organize adaptive global behaviors. Inferential brain
systems statistically minimize surprise, or “free-energy,” by generating sensory
samples, optimizing perception and/or improving adaptive action. Free-energy
minimization intrinsically connects perception and action, supporting an early origin
for top-down generative paradigms of brain function. Q consciousness-information
agrees with the concept of ensemble density, i.e. the probability of identifying hidden
causes in the environment.
Keywords: Biological Order, Bayesian Inference, Bayesian Sampler, Dissipative
Systems, Environmental States, Entropy, Ensemble Density, Free-Energy,
Generative Density, Hidden Causes, Information-Carrying, Information Theory,
Mass-Energy Transformation, Minimization of Surprise, Natural Selection, Open
Systems, Perception and Action, Predictive-Error, Singled-Celled Organisms,
Statistical Physics, Thermodynamic.
