Chiral conformity emerges from the least-time free energy consumption

The prevalence of chirally pure biological polymers is often assumed to stem from some slight preference for one chiral form at the origin of life. Likewise, the predominance of matter over antimatter is presumed to follow from some subtle bias for matter at the dawn of the universe. However, rather than being imposed from the start, handedness standards in societies emerged to make things work. Since work is the universal measure of transferred energy, it is reasoned that standards at all scales and scopes emerge to consume free energy. Free energy minimization, equal to entropy maximization, turns out to be the second law of thermodynamics when derived from statistical physics of open systems. This many-body theory is based on the atomistic axiom that everything comprises the same fundamental elements known as quanta of action; hence, everything follows the same law. According to the thermodynamic principle, the flows of energy naturally select standard structures over less-fit functional forms to consume free energy in the least time. Thermodynamics making no distinction between animate and inanimate renders the question of life's handedness meaningless and deems the search for an intrinsic difference between matter and antimatter pointless.

Information is non-physical: The rules connecting representation and meaning do not obey the laws of physics

The proposition that information is physical is widely accepted in the scientific community. Information is composed of physical representation, abstract meaning and rules, which interpret representation to meaning. In this article, I demonstrated that the rules connecting representation with meaning cannot be the laws of physics, because all quantities appearing in the laws of physics are physical, observable and measurable, and the meaning, however, is abstract, unobservable and unmeasurable. For linguistic information, the rules that determine how the ordered symbol sequence of language corresponds to its meaning are language vocabulary and grammar, not the laws of physics. This characteristic of information – its rules that link representation with meaning do not obey the laws of physics – ontologically distinguishes information from physical substances that obey the laws of physics, and inspires me to define entities that do not obey the laws of physics (such as information) as non-physical.

Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction

The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon’s noisy-channel coding theory, which adds into the “formula” for rule induction the crucial dimension of time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission.

Irreversible work and Maxwell demon in terms of quantum thermodynamic force

The second law of classical equilibrium thermodynamics, based on the positivity of entropy production, asserts that any process occurs only in a direction that some information may be lost (flow out of the system) due to the irreversibility inside the system. However, any thermodynamic system can exhibit fluctuations in which negative entropy production may be observed. In particular, in stochastic quantum processes due to quantum correlations and also memory effects we may see the reversal energy flow (heat flow from the cold system to the hot system) and the backflow of information into the system that leads to the negativity of the entropy production which is an apparent violation of the Second Law. In order to resolve this apparent violation, we will try to properly extend the Second Law to quantum processes by incorporating information explicitly into the Second Law. We will also provide a thermodynamic operational meaning for the flow and backflow of information. Finally, it is shown that negative and positive entropy production can be described by a quantum thermodynamic force.

An organisational systems-biology view of viruses explains why they are not alive

Whether or not viruses are alive remains unsettled. Discoveries of giant viruses with translational genes and large genomes have kept the debate active. Here, a fresh approach is introduced, based on the organisational definition of life from within systems biology. It views living as a circular process of self-organisation and self-construction which is 'closed to efficient causation'. How information combines with force to fabricate and organise environmentally obtained materials, given an energy source, is here explained as a physical embodiment of informational constraint. Comparing a general virus replication cycle with Rosen's (M,R)-system shows it to be linear, rather than closed. Some viruses contribute considerable organisational information, but so far none is known to supply all required, nor the material nor energy necessary to complete their replication cycle. As a result, no known virus replication cycle is closed to efficient causation: unlike cellular obligate parasites, viruses do not match the causal structure of an (M,R)-system. Analysis based in identifying a Markov blanket in causal structure proved inconclusive, but using Integrated Information Theory on a Boolean representation, it was possible to show that the causal structure of a virocell is not different from that of the host cell.

Prolegomena to an Operator Theory of Computation

Defining computation as information processing (information dynamics) with information as a relational property of data structures (the difference in one system that makes a difference in another system) makes it very suitable to use operator formulation, with similarities to category theory. The concept of the operator is exceedingly important in many knowledge areas as a tool of theoretical studies and practical applications. Here we introduce the operator theory of computing, opening new opportunities for the exploration of computing devices, processes, and their networks.

Hierarchical Structure of Generalized Thermodynamic and Informational Entropy

The present research aimed at discussing the thermodynamic and informational aspects of entropy concept to propose a unitary perspective of its definitions as an inherent property of any system in any state. The dualism and the relation between physical nature of information and the informational content of physical states of matter and phenomena play a fundamental role in the description of multi-scale systems characterized by hierarchical configurations. A method is proposed to generalize thermodynamic and informational entropy property and characterize the hierarchical structure of its canonical definition at macroscopic and microscopic levels of a system described in the domain of classical and quantum physics. The conceptual schema is based on dualisms and symmetries inherent to the geometric and kinematic configurations and interactions occurring in many-particle and few-particle thermodynamic systems. The hierarchical configuration of particles and sub-particles, representing the constitutive elements of physical systems, breaks down into levels characterized by particle masses subdivision, implying positions and velocities degrees of freedom multiplication. This hierarchy accommodates the allocation of phenomena and processes from higher to lower levels in the respect of the equipartition theorem of energy. However, the opposite and reversible process, from lower to higher level, is impossible by virtue of the Second Law, expressed as impossibility of Perpetual Motion Machine of the Second Kind (PMM2) remaining valid at all hierarchical levels, and the non-existence of Maxwell’s demon. Based on the generalized definition of entropy property, the hierarchical structure of entropy contribution and production balance, determined by degrees of freedom and constraints of systems configuration, is established. Moreover, as a consequence of the Second Law, the non-equipartition theorem of entropy is enunciated, which would be complementary to the equipartition theorem of energy derived from the First Law.

A Measure of Information Available for Inference

The mutual information between the state of a neural network and the state of the external world represents the amount of information stored in the neural network that is associated with the external world. In contrast, the surprise of the sensory input indicates the unpredictability of the current input. In other words, this is a measure of inference ability, and an upper bound of the surprise is known as the variational free energy. According to the free-energy principle (FEP), a neural network continuously minimizes the free energy to perceive the external world. For the survival of animals, inference ability is considered to be more important than simply memorized information. In this study, the free energy is shown to represent the gap between the amount of information stored in the neural network and that available for inference. This concept involves both the FEP and the infomax principle, and will be a useful measure for quantifying the amount of information available for inference.

Discourse on order vs. disorder

The second law of thermodynamics is on one hand understood to account for irrevocable flow of energy from the top down, on the other hand it is seen to imply irreversible increase of disorder. This tension between the 2 stances is resolved in favor of the free energy consumption when entropy is derived from the statistical mechanics of open systems. The change in entropy is shown to map directly to the decrease in free energy without any connotation attached to disorder. Increase of disorder, just as order, is found to be merely a consequence of free energy consumption. The erroneous association of disorder with entropy stems from an unwarranted assumption that a system could undergo changes of state without concomitant dissipation, i.e., a change in energy.

On the Character of Consciousness

The human brain is a particularly demanding system to infer its nature from observations. Thus, there is on one hand plenty of room for theorizing and on the other hand a pressing need for a rigorous theory. We apply statistical mechanics of open systems to describe the brain as a hierarchical system in consuming free energy in least time. This holistic tenet accounts for cellular metabolism, neuronal signaling, cognitive processes all together, or any other process by a formal equation of motion that extends down to the ultimate precision of one quantum of action. According to this general thermodynamic theory cognitive processes are no different by their operational and organizational principle from other natural processes. Cognition too will emerge and evolve along path-dependent and non-determinate trajectories by consuming free energy in least time to attain thermodynamic balance within the nervous system itself and with its surrounding systems. Specifically, consciousness can be ascribed to a natural process that integrates various neural networks for coherent consumption of free energy, i.e., for meaningful deeds. The whole hierarchy of integrated systems can be formally summed up to thermodynamic entropy. The holistic tenet provides insight to the character of consciousness also by acknowledging awareness in other systems at other levels of nature's hierarchy.

Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies

In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.

The concepts of representation and information in explanatory theories of human behavior

Focusing in experimental study of human behavior, this article discusses the concepts of information and mental representation aiming the integration of their biological, computational, and semantic aspects. Assuming that the objective of any communication process is ultimately to modify the receiver's state, the term correlational information is proposed as a measure of how changes occurring in external world correlate with changes occurring inside an individual. Mental representations are conceptualized as a special case of information processing in which correlational information is received, recorded, but also modified by a complex emergent process of associating new elements. In humans, the acquisition of information and creation of mental representations occurs in a two-step process. First, a sufficiently complex brain structure is necessary to establishing internal states capable to co-vary with external events. Second, the validity or meaning of these representations must be gradually achieved by confronting them with the environment. This contextualization can be considered as part of the process of ascribing meaning to information and representations. The hypothesis introduced here is that the sophisticated psychological constructs classically associated with the concept of mental representation are essentially of the same nature of simple interactive behaviors. The capacity of generating elaborated mental phenomena like beliefs and desires emerges gradually during evolution and, in a given individual, by learning and social interaction.

Noise enhances information transfer in hierarchical networks

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

The role of information in cell regulation

The organised state of living cells must derive from information internal to the system; however, there are strong reasons, based on sound evidence, to reject the base sequence information encoded in the genomic DNA as being directly relevant to the regulation of cellular phenotype. Rather, it is argued here that highly specific relational information, encoded on the gene products, mainly proteins, is responsible for phenotype. This regulatory information emerges as the peptide folds into a tertiary structure in much the same way as enzymic activity emerges under the same circumstances. The DNA coding sequence serves as a "data base" in which a second category of relational information is stored to enable accurate reproduction of the cellular peptides. In the context of the cell, therefore, information is physical in character and contributes, through its ability to dissipate free energy, to the maximisation of the entropy of the cell according to the 2nd law of thermodynamics.

The capricious character of nature

The on-going whole genome sequencing and whole cell assays of metabolites and proteins imply that complex systems could ultimately be mastered by perfecting knowledge into great detail. However, courses of nature are inherently intractable because flows of energy and their driving forces depend on each other. Thus no data will suffice to predict precisely the outcomes of e.g., engineering experiments. All path-dependent processes, most notably evolution in its entirety, display this capricious character of nature.

Looking for the Last Universal Common Ancestor (LUCA)

Genomic sequences across diverse species seem to align towards a common ancestry, eventually implying that eons ago some universal antecedent organism would have lived on the face of Earth. However, when evolution is understood not only as a biological process but as a general thermodynamic process, it becomes apparent that the quest for the last universal common ancestor is unattainable. Ambiguities in alignments are unavoidable because the driving forces and paths of evolution cannot be separated from each other. Thus tracking down life’s origin is by its nature a non-computable task. The thermodynamic tenet clarifies that evolution is a path-dependent process of least-time consumption of free energy. The natural process is without a demarcation line between animate and inanimate.

Identifying the 'inorganic gene' for high-temperature piezoelectric perovskites through statistical learning

This paper develops a statistical learning approach to identify potentially new high-temperature ferroelectric piezoelectric perovskite compounds. Unlike most computational studies on crystal chemistry, where the starting point is some form of electronic structure calculation, we use a data-driven approach to initiate our search. This is accomplished by identifying patterns of behaviour between discrete scalar descriptors associated with crystal and electronic structure and the reported Curie temperature (T_C) of known compounds; extracting design rules that govern critical structure–property relationships; and discovering in a quantitative fashion the exact role of these materials descriptors. Our approach applies linear manifold methods for data dimensionality reduction to discover the dominant descriptors governing structure–property correlations (the ‘genes’) and Shannon entropy metrics coupled to recursive partitioning methods to quantitatively assess the specific combination of descriptors that govern the link between crystal chemistry and T_C (their ‘sequencing’). We use this information to develop predictive models that can suggest new structure/chemistries and/or properties. In this manner, BiTmO₃–PbTiO₃ and BiLuO₃–PbTiO₃ are predicted to have a T_C of 730^°C and 705^°C, respectively. A quantitative structure–property relationship model similar to those used in biology and drug discovery not only predicts our new chemistries but also validates published reports.

Ecological succession as an energy dispersal process

Ecological succession is described by the 2nd law of thermodynamics. According to the universal law of the maximal energy dispersal, an ecosystem evolves toward a stationary state in its surroundings by consuming free energy via diverse mechanisms. Species are the mechanisms that conduct energy down along gradients between repositories of energy which consist of populations at various thermodynamic levels. The salient characteristics of succession, growing biomass production, increasing species richness and shifting distributions of species are found as consequences of the universal quest to diminish energy density differences in least time. The analysis reveals that during succession the ecosystem's energy transduction network, i.e., the food web organizes increasingly more effective in the free energy reduction by acquiring new, more effective and abandoning old, less effective species of energy transduction. The number of species does not necessarily peak at the climax state that corresponds to the maximum-entropy partition of species maximizing consumption of free energy. According to the theory of evolution by natural selection founded on statistical physics of open systems, ecological succession is one among many other evolutionary processes.