Derives an information geometry and free energy principle from Markov blanket statistical independencies, unifying mechanics across scales and interpreting internal states as inferences about external states for self-organizing systems.
Sentient Self-Organization: Minimal dynamics and circular causality
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abstract
Theoretical arguments and empirical evidence in neuroscience suggests that organisms represent or model their environment by minimizing a variational free-energy bound on the surprise associated with sensory signals from the environment. In this paper, we study phase transitions in coupled dissipative dynamical systems (complex Ginzburg-Landau equations) under a variety of coupling conditions to model the exchange of a system (agent) with its environment. We show that arbitrary coupling between sensory signals and the internal state of a system -- or those between its action and external (environmental) states -- do not guarantee synchronous dynamics between external and internal states: the spatial structure and the temporal dynamics of sensory signals and action (that comprise the system's Markov blanket) have to be pruned to produce synchrony. This synchrony is necessary for an agent to infer environmental states -- a pre-requisite for survival. Therefore, such sentient dynamics, relies primarily on approximate synchronization between the agent and its niche.
fields
q-bio.NC 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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A free energy principle for a particular physics
Derives an information geometry and free energy principle from Markov blanket statistical independencies, unifying mechanics across scales and interpreting internal states as inferences about external states for self-organizing systems.