Negative Entropy and Inference in Living Organisms

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.