The physiology of coordination: self-resolving diverse affinities via the sparse order in relevant noise.

Living systems at any given moment enact a very constrained set of end-directed and contextually appropriate actions that are self-initiated from among innumerable possible alternatives. However, these constrained actions are not necessarily because the system has reduced its sensitivities to themselves and their surroundings. Quite the contrary, living systems are continually open to novel and unanticipated stimulations that require a physiology of coordination. To address these competing demands, this paper offers a novel heuristic model informed by neuroscience, systems theory, biology and sign study to explain how organisms situated in diverse, complex and ever-changing environments might draw upon the sparse order made available by 'relevant noise'. This emergent order facilitates coordination, habituation and, ultimately, understanding of the world and its relevant affordances. Inspired by the burgeoning field of coordination dynamics and physiologist Denis Noble's concept of 'biological relativity', this model proposes a view of coordination on the neuronal level that is neither sequential nor stochastic, but instead implements a causal logic of phasic alignment, such that an organism's learned and inherited sets of diverse biological affinities and sympathies can be resolved into a continuous and complex range of patterns that will implement the kind of novel orientations and radical generativity required of such organisms to adaptively explore their environments and to learn from their experiences.

Living systems are smarter bots: Slime mold semiosis versus AI symbol manipulation.

Although machines may be good at mimicking, they are not currently able, as organisms are, to act creatively. We offer an understanding of the emergent qualities of biological sign processing in terms of generalization, association, and encryption. We use slime mold as a model of minimal cognition and compare it to deep-learning video game bots, which some claim have evolved beyond their merely quantitative algorithms. We find that these discrete Turing machine bots are not able to make productive, yet unanticipated, "errors"-necessary for biological learning-which, based on the physicality of signs, their relatively similar shapes, and relative physical positions spatially and temporally, lead to emergent effects and make learning and evolution possible. In organisms, stochastic resonance at the local level can be leveraged for self-organization at the global level. We contrast all this to the symbolic processing of today's machine learning, whereby each logic node and memory state is discrete. Computer codes are produced by external operators, whereas biological symbols are evolved through an internal encryption process.

The transition from constraint to regulation at the origin of life.

The origin of living dynamics required a local evasion of thermodynamic degradation by maintaining critical dynamical and structural constraints. Scenarios for life`s origin that fail to distinguish between constrained chemistry and regulated metabolism do not address the question of how living processes first emerge from simpler constraints on molecular interactions. We describe a molecular model system consisting of coupled reciprocal catalysis and self-assembly in which one of the catalytic bi-products tends to spontaneously self-assemble into a containing shell (analogous to a viral capsule). In this process, which we call autogenesis, self-repair/reconstitution and reproduction are made possible by the fact that each of these linked self-organizing processes generates boundary constraints that promote and limit the other, and because this synergy thereby becomes embodied as a persistent rate-independent substrate-transferrable constraint on the synergy of its component constraint-generating processes. It is proposed that this higher-order formal constraint is necessary and sufficient to constitute regulation as opposed to mere physico-chemical constraint. Two minor elaborations of this model system demonstrate how cybernetic and template-based regulation could emerge from this basic process.