On classical and quantum error-correction in ciliate mate selection

Ownership psychology as a "cognitive cell" adaptation: A minimalist model of microbial goods theory

Microbes perfect social interactions with intuitive logics and goal-directed reciprocity. These multilevel, cognition-resembling adaptations in Dictyostelid cellular molds enable individual-to-group viability through public/private bacterial farming and dynamic marketspaces. Like humans and animals, Dictyostelid livestock-ownership depends on environmental sensing, cooperation, and competition. Moreover, social-norm policing of cosmopolitan colonies coordinates farmer decisions, phenotypes, and ownership identities with bacteria herding, privatization, and consumption.

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.

Unpredictable homeodynamic and ambient constraints on irrational decision making of aneural and neural foragers

Foraging for nutritional sustenance represents common significant learned/heritable survival strategies evolved for phylum-diverse cellular life on Earth. Unicellular aneural to multicellular neural foragers display conserved rational or irrational decision making depending on outcome predictions for noise-susceptible real/illusory homeodynamic and ambient dietary cues. Such context-dependent heuristic-guided foraging enables optimal, suboptimal, or fallacious decisions that drive organismal adaptation, health, longevity, and life history.

Evolution of affective and linguistic disambiguation under social eavesdropping pressures

Contradicting new dual-pathway models of language evolution, cortico-striatal-thalamic circuitry disambiguate uncertainties in affective prosody and propositional linguistic content of language production and comprehension, predictably setting limits on useful complexity of articulate phonic and/or signed speech. Such limits likely evolved to ensure public information is discriminated by intended communicants and safeguarded against the ecological pressures of social eavesdropping within and across phylogenetic boundaries.

Macromolecular networks and intelligence in microorganisms

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

Basis for a neuronal version of Grover's quantum algorithm

Grover's quantum (search) algorithm exploits principles of quantum information theory and computation to surpass the strong Church–Turing limit governing classical computers. The algorithm initializes a search field into superposed N (eigen)states to later execute nonclassical “subroutines” involving unitary phase shifts of measured states and to produce root-rate or quadratic gain in the algorithmic time (O(N^1/2)) needed to find some “target” solution m. Akin to this fast technological search algorithm, single eukaryotic cells, such as differentiated neurons, perform natural quadratic speed-up in the search for appropriate store-operated Ca²⁺ response regulation of, among other processes, protein and lipid biosynthesis, cell energetics, stress responses, cell fate and death, synaptic plasticity, and immunoprotection. Such speed-up in cellular decision making results from spatiotemporal dynamics of networked intracellular Ca²⁺-induced Ca²⁺ release and the search (or signaling) velocity of Ca²⁺ wave propagation. As chemical processes, such as the duration of Ca²⁺ mobilization, become rate-limiting over interstore distances, Ca²⁺ waves quadratically decrease interstore-travel time from slow saltatory to fast continuous gradients proportional to the square-root of the classical Ca²⁺ diffusion coefficient, D^1/2, matching the computing efficiency of Grover's quantum algorithm. In this Hypothesis and Theory article, I elaborate on these traits using a fire-diffuse-fire model of store-operated cytosolic Ca²⁺ signaling valid for glutamatergic neurons. Salient model features corresponding to Grover's quantum algorithm are parameterized to meet requirements for the Oracle Hadamard transform and Grover's iteration. A neuronal version of Grover's quantum algorithm figures to benefit signal coincidence detection and integration, bidirectional synaptic plasticity, and other vital cell functions by rapidly selecting, ordering, and/or counting optional response regulation choices.

Ciliates learn to diagnose and correct classical error syndromes in mating strategies

Preconjugal ciliates learn classical repetition error-correction codes to safeguard mating messages and replies from corruption by “rivals” and local ambient noise. Because individual cells behave as memory channels with Szilárd engine attributes, these coding schemes also might be used to limit, diagnose, and correct mating-signal errors due to noisy intracellular information processing. The present study, therefore, assessed whether heterotrich ciliates effect fault-tolerant signal planning and execution by modifying engine performance, and consequently entropy content of codes, during mock cell–cell communication. Socially meaningful serial vibrations emitted from an ambiguous artificial source initiated ciliate behavioral signaling performances known to advertise mating fitness with varying courtship strategies. Microbes, employing calcium-dependent Hebbian-like decision making, learned to diagnose then correct error syndromes by recursively matching Boltzmann entropies between signal planning and execution stages via “power” or “refrigeration” cycles. All eight serial contraction and reversal strategies incurred errors in entropy magnitude by the execution stage of processing. Absolute errors, however, subtended expected threshold values for single bit-flip errors in three-bit replies, indicating coding schemes protected information content throughout signal production. Ciliate preparedness for vibrations selectively and significantly affected the magnitude and valence of Szilárd engine performance during modal and non-modal strategy corrective cycles. But entropy fidelity for all replies mainly improved across learning trials as refinements in engine efficiency. Fidelity neared maximum levels for only modal signals coded in resilient three-bit repetition error-correction sequences. Together, these findings demonstrate microbes can elevate survival/reproductive success by learning to implement classical fault-tolerant information processing in social contexts.

On eukaryotic intelligence: signaling system's guidance in the evolution of multicellular organization

Communication with the environment is an essential characteristic of the living cell, even more when considering the origins and evolution of multicellularity. A number of changes and tinkering inventions were necessary in the evolutionary transition between prokaryotic and eukaryotic cells, which finally made possible the appearance of genuine multicellular organisms. In the study of this process, however, the transformations experimented by signaling systems themselves have been rarely object of analysis, obscured by other more conspicuous biological traits: incorporation of mitochondria, segregated nucleus, introns/exons, flagellum, membrane systems, etc. Herein a discussion of the main avenues of change from prokaryotic to eukaryotic signaling systems and a review of the signaling resources and strategies underlying multicellularity will be attempted. In the expansion of prokaryotic signaling systems, four main systemic resources were incorporated: molecular tools for detection of solutes, molecular tools for detection of solvent (Donnan effect), the apparatuses of cell-cycle control, and the combined system endocytosis/cytoskeleton. The multiple kinds of enlarged, mixed pathways that emerged made possible the eukaryotic revolution in morphological and physiological complexity. The massive incorporation of processing resources of electro-molecular nature, derived from the osmotic tools counteracting the Donnan effect, made also possible the organization of a computational tissue with huge information processing capabilities: the nervous system. In the central nervous systems of vertebrates, and particularly in humans, neurons have achieved both the highest level of molecular-signaling complexity and the highest degree of information-processing adaptability. Theoretically, it can be argued that there has been an accelerated pace of evolutionary change in eukaryotic signaling systems, beyond the other general novelties introduced by eukaryotic cells in their handling of DNA processes. Under signaling system's guidance, the whole processes of transcription, alternative splicing, mobile elements, and other elements of domain recombination have become closely intertwined and have propelled the differentiation capabilities of multicellular tissues and morphologies. An amazing variety of signaling and self-construction strategies have emerged out from the basic eukaryotic design of multicellular complexity, in millions and millions of new species evolved. This design can also be seen abstractly as a new kind of quasi-universal problem-solving 'engine' implemented at the biomolecular scale-providing the fundamentals of eukaryotic 'intelligence'. Analyzing in depth the problem-solving intelligence of eukaryotic cells would help to establish an integrative panorama of their information processing organization, and of their capability to handle the morphological and physiological complexity associated. Whether an informational updating of the venerable "cell theory" is feasible or not, becomes, at the time being - right in the middle of the massive data deluge/revolution from omic disciplines - a matter to careful consider.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present findings indicate sparseness performs a similar function in single aneural cells by tuning the size and density of encoded computational architectures useful for decision making in social contexts.

Arrhenius-kinetics evidence for quantum tunneling in microbial "social" decision rates

Social-like bacteria, fungi and protozoa communicate chemical and behavioral signals to coordinate their specializations into an ordered group of individuals capable of fitter ecological performance. Examples of microbial "social" behaviors include sporulation and dispersion, kin recognition and nonclonal or paired reproduction. Paired reproduction by ciliates is believed to involve intra- and intermate selection through pheromone-stimulated "courting" rituals. Such social maneuvering minimizes survival-reproduction tradeoffs while sorting superior mates from inferior ones, lowering the vertical spread of deleterious genes in geographically constricted populations and possibly promoting advantageous genetic innovations. In a previous article, I reported findings that the heterotrich Spirostomum ambiguum can out-complete mating rivals in simulated social trials by learning behavioral heuristics which it then employs to store and select sets of altruistic and deceptive signaling strategies. Frequencies of strategy use typically follow Maxwell-Boltzmann (MB), Fermi-Dirac (FD) or Bose-Einstein (BE) statistical distributions. For ciliates most adept at social decision making, a brief classical MB computational phase drives signaling behavior into a later quantum BE computational phase that condenses or favors the selection of a single fittest strategy. Appearance of the network analogue of BE condensation coincides with Hebbian-like trial-and-error learning and is consistent with the idea that cells behave as heat engines, where loss of energy associated with specific cellular machinery critical for mating decisions effectively reduces the temperature of intracellular enzymes cohering into weak Fröhlich superposition. I extend these findings by showing the rates at which ciliates switch serial behavioral strategies agree with principles of chemical reactions exhibiting linear and nonlinear Arrhenius kinetics during respective classical and quantum computations. Nonlinear Arrhenius kinetics in ciliate decision making suggest transitions from one signaling strategy to another result from a computational analogue of quantum tunneling in social information processing.