Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics

Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.

A systematic approach to brain dynamics: cognitive evolution theory of consciousness

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.

Thinking, holograms, and the quantum brain

This article continues the development of the idea that all human behavior and thinking are innate. A model of thinking and functioning of the brain has been constructed, which is capable of explaining both the accuracy of molecular processes and the innateness of behaviors. The focus of the model is the phase of the wave function of the particle, which is an additional (free) parameter. It should also be emphasized that the phase of the wave function of a particle is inextricably linked with the quantum action S in the Feynman's formulation of quantum mechanics (path integrals). A hypothesis is proposed: the set of particles that make up neurons and the brain is controlled by changing the phases from the outside (by a higher order system). Such a control system must be outside our world because our measurement methods do not allow us to determine the phase of an elementary particle. In a sense, it can be viewed as an extension of Bohm's ideas about the holographic brain and the holographic universe. Experiments are proposed that could confirm or disprove this model.

Quantum spin models for numerosity perception

Humans share with animals, both vertebrates and invertebrates, the capacity to sense the number of items in their environment already at birth. The pervasiveness of this skill across the animal kingdom suggests that it should emerge in very simple populations of neurons. Current modelling literature, however, has struggled to provide a simple architecture carrying out this task, with most proposals suggesting the emergence of number sense in multi-layered complex neural networks, and typically requiring supervised learning; while simple accumulator models fail to predict Weber's Law, a common trait of human and animal numerosity processing. We present a simple quantum spin model with all-to-all connectivity, where numerosity is encoded in the spectrum after stimulation with a number of transient signals occurring in a random or orderly temporal sequence. We use a paradigmatic simulational approach borrowed from the theory and methods of open quantum systems out of equilibrium, as a possible way to describe information processing in neural systems. Our method is able to capture many of the perceptual characteristics of numerosity in such systems. The frequency components of the magnetization spectra at harmonics of the system's tunneling frequency increase with the number of stimuli presented. The amplitude decoding of each spectrum, performed with an ideal-observer model, reveals that the system follows Weber's law. This contrasts with the well-known failure to reproduce Weber's law with linear system or accumulators models.

Wave-like patterns in parameter space interpreted as evidence for macroscopic effects resulting from quantum or quantum-like processes in the brain

Data from eight numerosity estimation experiments reliably exhibit wave-like patterns in plots of the standard deviations of the response times along the abstract parameter of the magnitude of the error in the numerosity estimation. An explanation for this phenomenon is proposed in terms of an analogy between response times and error magnitude on one hand, and energy and position of quantum particles on the other, constructed using an argument for an overlap between the mathematical apparatus describing Hopfield-type neural networks and quantum systems, established by some researchers. Alternative explanations are presented within the traditional explanatory framework of oscillations due to neural firing, involving hypothetical mechanisms for converting oscillation patterns in time to oscillation patterns in the space of an abstract parameter, such as the magnitude of the error during numerosity estimation. The viability of the proposal of causal influences propagating from the microscale of quantum phenomena to the macroscale of human behavior, needed for the first type of explanation, is exemplified by the phenomenon of magnetoreception in some species of birds, which is allegedly quantum in nature.

Complexity analysis of heartbeat-related signals in brain MRI time series as a potential biomarker for ageing and cognitive performance

Getting older affects both the structure of the brain and some cognitive capabilities. Until now, magnetic resonance imaging (MRI) approaches have been unable to give a coherent reflection of the cognitive declines. It shows the limitation of the contrast mechanisms used in most MRI investigations, which are indirect measures of brain activities depending on multiple physiological and cognitive variables. However, MRI signals may contain information of brain activity beyond these commonly used signals caused by the neurovascular response. Here, we apply a zero-spin echo (ZSE) weighted MRI sequence, which can detect heartbeat-evoked signals (HES). Remarkably, these MRI signals have properties only known from electrophysiology. We investigated the complexity of the HES arising from this sequence in two age groups; young (18-29 years) and old (over 65 years). While comparing young and old participants, we show that the complexity of the HES decreases with age, where the stability and chaoticity of these HES are particularly sensitive to age. However, we also found individual differences which were independent of age. Complexity measures were related to scores from different cognitive batteries and showed that higher complexity may be related to better cognitive performance. These findings underpin the affinity of the HES to electrophysiological signals. The profound sensitivity of these changes in complexity shows the potential of HES for understanding brain dynamics that need to be tested in more extensive and diverse populations with clinical relevance for all neurovascular diseases.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjs/s11734-022-00696-2.

Self-organized criticality as a framework for consciousness: A review study

Objective

No current model of consciousness is univocally accepted on either theoretical or empirical grounds, and the need for a solid unifying framework is evident. Special attention has been given to the premise that self-organized criticality (SOC) is a fundamental property of neural system. SOC provides a competitive model to describe the physical mechanisms underlying spontaneous brain activity, and thus, critical dynamics were proposed as general gauges of information processing representing a strong candidate for a surrogate measure of consciousness. As SOC could be a neurodynamical framework, which may be able to bring together existing theories and experimental evidence, the purpose of this work was to provide a comprehensive overview of progress of research on SOC in association with consciousness.

Methods

A comprehensive search of publications on consciousness and SOC published between 1998 and 2021 was conducted. The Web of Science database was searched, and annual number of publications and citations, type of articles, and applied methods were determined.

Results

A total of 71 publications were identified. The annual number of citations steadily increased over the years. Original articles comprised 50.7% and reviews/theoretical articles 43.6%. Sixteen studies reported on human data and in seven studies data were recorded in animals. Computational models were utilized in n = 12 studies. EcoG data were assessed in n = 4 articles, fMRI in n = 4 studies, and EEG/MEG in n = 10 studies. Notably, different analytical tools were applied in the EEG/MEG studies to assess a surrogate measure of criticality such as the detrended fluctuation analysis, the pair correlation function, parameters from the neuronal avalanche analysis and the spectral exponent.

Conclusion

Recent studies pointed out agreements of critical dynamics with the current most influencing theories in the field of consciousness research, the global workspace theory and the integrated information theory. Thus, the framework of SOC as a neurodynamical parameter for consciousness seems promising. However, identified experimental work was small in numbers, and a heterogeneity of applied analytical tools as a surrogate measure of criticality was observable, which limits the generalizability of findings.

Dynamics of Excitons in Conjugated Molecules and Organic Semiconductor Systems

The exciton, an excited electron-hole pair bound by Coulomb attraction, plays a key role in photophysics of organic molecules and drives practically important phenomena such as photoinduced mechanical motions of a molecule, photochemical conversions, energy transfer, generation of free charge carriers, etc. Its behavior in extended π-conjugated molecules and disordered organic films is very different and very rich compared with exciton behavior in inorganic semiconductor crystals. Due to the high degree of variability of organic systems themselves, the exciton not only exerts changes on molecules that carry it but undergoes its own changes during all phases of its lifetime, that is, birth, conversion and transport, and decay. The goal of this review is to give a systematic and comprehensive view on exciton behavior in π-conjugated molecules and molecular assemblies at all phases of exciton evolution with emphasis on rates typical for this dynamic picture and various consequences of the above dynamics. To uncover the rich variety of exciton behavior, details of exciton formation, exciton transport, exciton energy conversion, direct and reverse intersystem crossing, and radiative and nonradiative decay are considered in different systems, where these processes lead to or are influenced by static and dynamic disorder, charge distribution symmetry breaking, photoinduced reactions, electron and proton transfer, structural rearrangements, exciton coupling with vibrations and intermediate particles, and exciton dissociation and annihilation as well.

Toward a More General Understanding of Bohr's Complementarity: Insights from Modeling of Ion Channels

Some contemporary theorists such as Mazzocchi, Theise and Kafatos are convinced that the reformed complementarity may redefine how we might exploit the complexity theory in 21st-century life sciences research. However, the motives behind the profound re-invention of "biological complementarity" need to be substantiated with concrete shreds of evidence about this principle's applicability in real-life science experimentation, which we found missing in the literature. This paper discusses such pieces of evidence by confronting Bohr's complementarity and ion channel modeling practice. We examine whether and to what extent this principle might assist in developing ion channel models incorporating both deterministic and stochastic solutions. According to the "mutual exclusiveness of experimental setups" version of Bohr's complementarity, this principle is needed when two mutually exclusive perspectives or approaches are right, necessary in a particular context, and are not contradictory as they arise in mutually exclusive conditions (mutually exclusive experimental or modeling setups). A detailed examination of the modeling practice reveals that both solutions are often used simultaneously in a single ion channel model, suggesting that the opposite conceptual frameworks can coexist in the same modeling setup. We concluded that Bohr's complementarity might find applications in these complex modeling setups but only through its realistic phenomenological interpretation that allows applying different modes of description regardless of the nature of the underlying ion channel opening process. Also, we propose the combined use of complementarity and Complex thinking in building the multifaceted ion channel models. Overall, this paper's results support the efforts to establish a more general form of complementarity to meet today's complexity theory-inspired life sciences modeling demands.

Consciousness and cognition in plants

Unlike animal behavior, behavior in plants is traditionally assumed to be completely determined either genetically or environmentally. Under this assumption, plants are usually considered to be noncognitive organisms. This view nonetheless clashes with a growing body of empirical research that shows that many sophisticated cognitive capabilities traditionally assumed to be exclusive to animals are exhibited by plants too. Yet, if plants can be considered cognitive, even in a minimal sense, can they also be considered conscious? Some authors defend that the quest for plant consciousness is worth pursuing, under the premise that sentience can play a role in facilitating plant's sophisticated behavior. The goal of this article is not to provide a positive argument for plant cognition and consciousness, but to invite a constructive, empirically informed debate about it. After reviewing the empirical literature concerning plant cognition, we introduce the reader to the emerging field of plant neurobiology. Research on plant electrical and chemical signaling can help shed light into the biological bases for plant sentience. To conclude, we shall present a series of approaches to scientifically investigate plant consciousness. In sum, we invite the reader to consider the idea that if consciousness boils down to some form of biological adaptation, we should not exclude a priori the possibility that plants have evolved their own phenomenal experience of the world. This article is categorized under: Cognitive Biology > Evolutionary Roots of Cognition Philosophy > Consciousness Neuroscience > Cognition.

Addiction as a brain disease revised: why it still matters, and the need for consilience

The view that substance addiction is a brain disease, although widely accepted in the neuroscience community, has become subject to acerbic criticism in recent years. These criticisms state that the brain disease view is deterministic, fails to account for heterogeneity in remission and recovery, places too much emphasis on a compulsive dimension of addiction, and that a specific neural signature of addiction has not been identified. We acknowledge that some of these criticisms have merit, but assert that the foundational premise that addiction has a neurobiological basis is fundamentally sound. We also emphasize that denying that addiction is a brain disease is a harmful standpoint since it contributes to reducing access to healthcare and treatment, the consequences of which are catastrophic. Here, we therefore address these criticisms, and in doing so provide a contemporary update of the brain disease view of addiction. We provide arguments to support this view, discuss why apparently spontaneous remission does not negate it, and how seemingly compulsive behaviors can co-exist with the sensitivity to alternative reinforcement in addiction. Most importantly, we argue that the brain is the biological substrate from which both addiction and the capacity for behavior change arise, arguing for an intensified neuroscientific study of recovery. More broadly, we propose that these disagreements reveal the need for multidisciplinary research that integrates neuroscientific, behavioral, clinical, and sociocultural perspectives.

Imprecise Visual Feedback About Hand Location Increases a Classically Conditioned Pain Expectancy Effect

We tested the hypotheses that rendering sensory input about hand location imprecise increases a classically conditioned pain expectancy effect, increases generalization of the effect to novel locations and reduces extinction of the effect. Forty healthy volunteers performed movements with their right hand along predefined paths. Each path passed through 2 locations that were defined as either i) the conditioned stimulus (CS+; paired with a painful unconditioned stimulus), or ii) unpaired (CS-). During acquisition phase, participants watched their hand as they moved it. Participants were randomly allocated to an Imprecise group, for whom visual feedback of the hand was offset 30 to 50 mm from its true location, or a Precise group, for whom vision was not disrupted. In the test phase, participants moved their hands to 5 locations-the CS+, CS-, and 3 locations that lay between the 2 ("generalization stimuli"). Our primary hypothesis was supported-pain expectancy was greater at the CS+ location in the Imprecise group than in the Precise group (6.9 [SD = 1.9] vs 5.4 [SD = 2.5], P= .02). Pain expectancies generalized to novel locations similarly in both groups and there was no difference in extinction between groups. Our primary hypothesis was supported but our subsequent hypotheses were not. PERSPECTIVE: We conditioned pain expectancy at a certain location of one hand, even though most participants were unaware of the contingency. Conditioned pain expectancy was greater when sensory information about location was less precise. This adds support to the possibility that associative learning may play a role in the progression of an acute pain episode to a more generalized pain disorder.

Mathematical Modeling of Ion Quantum Tunneling Reveals Novel Properties of Voltage-Gated Channels and Quantum Aspects of Their Pathophysiology in Excitability-Related Disorders

Voltage-gated channels are crucial in action potential initiation and propagation and there are many diseases and disorders related to them. Additionally, the classical mechanics are the main mechanics used to describe the function of the voltage-gated channels and their related abnormalities. However, the quantum mechanics should be considered to unravel new aspects in the voltage-gated channels and resolve the problems and challenges that classical mechanics cannot solve. In the present study, the aim is to mathematically show that quantum mechanics can exhibit a powerful tendency to unveil novel electrical features in voltage-gated channels and be used as a promising tool to solve the problems and challenges in the pathophysiology of excitability-related diseases. The model of quantum tunneling of ions through the intracellular hydrophobic gate is used to evaluate the influence of membrane potential and gating free energy on the tunneling probability, single channel conductance, and quantum membrane conductance. This evaluation is mainly based on graphing the mathematical relationships between these variables. The obtained mathematical graphs showed that ions can achieve significant quantum membrane conductance, which can affect the resting membrane potential and the excitability of cells. In the present work, quantum mechanics reveals original electrical properties associated with voltage-gated channels and introduces new insights and implications into the pathophysiology of excitability- related disorders. In addition, the present work sets a mathematical and theoretical framework that can be utilized to conduct experimental studies in order to explore the quantum aspects of voltage-gated channels and the quantum bioelectrical property of biological membranes.

Photons detected in the active nerve by photographic technique

The nervous system is one of the most complex expressions of biological evolution. Its high performance mostly relies on the basic principle of the action potential, a sequential activation of local ionic currents along the neural fiber. The implications of this essentially electrical phenomenon subsequently emerged in a more comprehensive electromagnetic perspective of neurotransmission. Several studies focused on the possible role of photons in neural communication and provided evidence of the transfer of photons through myelinated axons. A hypothesis is that myelin sheath would behave as an optical waveguide, although the source of photons is controversial. In a previous work, we proposed a model describing how photons would arise at the node of Ranvier. In this study we experimentally detected photons in the node of Ranvier by Ag+ photoreduction measurement technique, during electrically induced nerve activity. Our results suggest that in association to the action potential a photonic radiation takes place in the node.

From Quantum Physics to Quantum Hypnosis: A Quantum Mind Perspective

A novel, heuristic model based upon chaotic complex systems theory and quantum mechanics is proposed to overcome the dichotomy between mind and body. The mind-body interface represents a chaotic system, ruled by the probability principle, as shown in quantum mechanics. Neuronal activity shows many patterns of chaotic behavior, and applications of chaotic patterns seem to be relevant for research regarding the mind-body relationship and the process of trance. A quantum consciousness theory has been proposed, largely controversial, since quantum physics applies to subatomic world and not to macrostructures, such as the brain. Quantum cognition is an emerging field that applies the formalism of quantum theory to model cognitive phenomena such as information processing by the human brain; it overcomes limits and shortcomings of cartesian dualism as well as quantum general theory. As hypnosis is a state of consciousness, it applies to hypnotic cognitive functioning rather than hypnotic structure.

Consciousness as an Emergent Phenomenon: A Tale of Different Levels of Description

One of the biggest queries in cognitive sciences is the emergence of consciousness from matter. Modern neurobiological theories of consciousness propose that conscious experience is the result of interactions between large-scale neuronal networks in the brain, traditionally described within the realm of classical physics. Here, we propose a generalized connectionist framework in which the emergence of "conscious networks" is not exclusive of large brain areas, but can be identified in subcellular networks exhibiting nontrivial quantum phenomena. The essential feature of such networks is the existence of strong correlations in the system (classical or quantum coherence) and the presence of an optimal point at which the system's complexity and energy dissipation are maximized, whereas free-energy is minimized. This is expressed either by maximization of the information content in large scale functional networks or by achieving optimal efficiency through the quantum Goldilock effect.

Medical hypothesis: Neurodegenerative diseases arise from oxidative damage to electron tunneling proteins in mitochondria

Mitochondria likely arose from serial endosymbiosis by early eukaryotic cells and control electron flow to molecular oxygen to facilitate energy transformation. Mitochondria translate between the quantum and macroscopic worlds and utilize quantum tunneling of electrons to reduce activation energy barriers to electron flow. Electron tunneling has been extensively characterized in Complex I of the electron transport chain. Age-related increases in oxidative damage to these electron tunneling systems may account for decreased energy storage found in aged and neurodegenerative disease tissues, such as those from sufferers of amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD) and Parkinson's disease (PD). This hypothesis is testable. If correct, this hypothesis supports pre-symptomatic, mitochondrially-directed oxygen free radical scavenging therapies.

Ferritin and neuromelanin "quantum dot" array structures in dopamine neurons of the substantia nigra pars compacta and norepinephrine neurons of the locus coeruleus

In this review, the author shows that ferritin has documented quantum dot material properties that have been reported in numerous independent studies, and can enable quantum mechanical electron transport over substantial distances. In addition, neuromelanin is a pi-conjugated polymer, and quantum dot/pi-conjugated polymer combinations have been reported in numerous independent studies to facilitate electron transport for solar photovoltaic and other applications. Both ferritin and neuromelanin are present in large quantities in the dopamine neurons of the substantia nigra pars compactaand the norepinephrine neurons of the locus coeruleus. The unique structure of subgroups of these neurons that have a large number of axon branches and synapses may have evolved to take advantage of this electron transport mechanism, if it is present, such as to coordinate conscious action, or for other purposes. Independent clinical and laboratory studies are also reviewed that corroborate this theory of coordinated action in these neuron groups. Research to validate the theory using charge transport measurements, materials characterization, existing fluorescent probe material and reaction time testing is proposed.

Quantum theory of mass potentials

Probabilistic formalism of quantum mechanics is used to quantitatively link the global scale mass potential with the underlying electrical activity of excitable cells. Previous approaches implemented methods of classical physics to reconstruct the mass potential in terms of explicit physical models of participating cells and the volume conductor. However, the multiplicity of cellular processes with extremely intricate mixtures of deterministic and random factors prevents the creation of consistent biophysical parameter sets. To avoid the uncertainty inherent in physical attributes of cell ensembles, we undertake here a radical departure from deterministic equations of classical physics, instead applying the probabilistic reasoning of quantum mechanics. Crucial steps include: (1) the relocation of the elementary bioelectric sources from a cellular to a molecular level; (2) the creation of microscale particle models in terms of a non-homogenous birth-and-death process. To link the microscale processes with macroscale potentials, time-frequency analysis was applied for estimation of the empirical characteristic functions for component waveforms of electroencephalogram (EEG), eye-blink electromyogram (EMG), and electrocardiogram (ECG). We describe universal models for the amplitude spectra and phase functions of functional components of mass potentials. The corresponding time domain relationships disclose the dynamics of mass potential components as limit distribution functions produced by specific microscale transients. The probabilistic laws governing the microscale machinery, founded on an empirical basis, are presented. Computer simulations of particle populations with time dependent transition probabilities reveal that hidden deterministic chaos underlies development of the components of mass potentials. We label this kind of behaviour “transient deterministic chaos”.