Percolation Model of Sensory Transmission and Loss of Consciousness Under General Anesthesia

Eight challenges in developing theory of intelligence

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.

EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia

The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15-45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth.

Network location and clustering of genetic mutations determine chronicity in a stylized model of genetic diseases

In a highly simplified view, a disease can be seen as the phenotype emerging from the interplay of genetic predisposition and fluctuating environmental stimuli. We formalize this situation in a minimal model, where a network (representing cellular regulation) serves as an interface between an input layer (representing environment) and an output layer (representing functional phenotype). Genetic predisposition for a disease is represented as a loss of function of some network nodes. Reduced, but non-zero, output indicates disease. The simplicity of this genetic disease model and its deep relationship to percolation theory allows us to understand the interplay between disease, network topology and the location and clusters of affected network nodes. We find that our model generates two different characteristics of diseases, which can be interpreted as chronic and acute diseases. In its stylized form, our model provides a new view on the relationship between genetic mutations and the type and severity of a disease.

Percolation in a simple cubic lattice with distortion

Site percolation in a distorted simple cubic lattice is characterized numerically employing the Newman-Ziff algorithm. Distortion is administered in the lattice by systematically and randomly dislocating its sites from their regular positions. The amount of distortion is tunable by a parameter called the distortion parameter. In this model, two occupied neighboring sites are considered connected only if the distance between them is less than a predefined value called the connection threshold. It is observed that the percolation threshold always increases with distortion if the connection threshold is equal to or greater than the lattice constant of the regular lattice. On the other hand, if the connection threshold is less than the lattice constant, the percolation threshold first decreases and then increases steadily as distortion is increased. It is shown that the variation of the percolation threshold can be well explained by the change in the fraction of occupied bonds with distortion. The values of the relevant critical exponents of the transition strongly indicate that percolation in regular and distorted simple cubic lattices belong to the same universality class. It is also demonstrated that this model is intrinsically distinct from the site-bond percolation model.

Percolation may explain efficiency, robustness, and economy of the brain

The brain consists of billions of neurons connected by ultra-dense synapses, showing remarkable efficiency, robust flexibility, and economy in information processing. It is generally believed that these advantageous properties are rooted in brain connectivity; however, direct evidence remains absent owing to technical limitations or theoretical vacancy. This research explores the origins of these properties in the largest yet brain connectome of the fruit fly. We reveal that functional connectivity formation in the brain can be explained by a percolation process controlled by synaptic excitation-inhibition (E/I) balance. By increasing the E/I balance gradually, we discover the emergence of these properties as byproducts of percolation transition when the E/I balance arrives at 3:7. As the E/I balance keeps increase, an optimal E/I balance 1:1 is unveiled to ensure these three properties simultaneously, consistent with previous in vitro experimental predictions. Once the E/I balance reaches over 3:2, an intrinsic limitation of these properties determined by static (anatomical) brain connectivity can be observed. Our work demonstrates that percolation, a universal characterization of critical phenomena and phase transitions, may serve as a window toward understanding the emergence of various brain properties.

Regulation and drug modulation of a voltage-gated sodium channel: Pivotal role of the S4-S5 linker in activation and slow inactivation

Voltage-gated sodium channels initiate electric signals in cell communications. The S4–S5 linker between the voltage-sensing and pore modules transmits depolarization signals to trigger channel activation. The mechanisms of this action, however, remain elusive. By combining biophysical and computational approaches, we identify a critical residue, T140, in the S4–S5 linker of the bacterial sodium channel NaChBac, which plays a pivotal role in channel activation and drug modulation of slow inactivation. Specifically, we discovered conformation-dependent drug binding at this site and unveiled a toggling mode of action by T140, which switches interaction partners with different S6 residues to regulate channel activation and slow inactivation. These observations suggest the possibility of conformation-specific drugs targeting the gating machinery of voltage-gated ion channels.

Voltage-gated sodium (Na_V) channels control excitable cell functions. While structural investigations have revealed conformation details of different functional states, the mechanisms of both activation and slow inactivation remain unclear. Here, we identify residue T140 in the S4–S5 linker of the bacterial voltage-gated sodium channel NaChBac as critical for channel activation and drug effects on inactivation. Mutations at T140 either attenuate activation or render the channel nonfunctional. Propofol, a clinical anesthetic known to inhibit NaChBac by promoting slow inactivation, binds to a pocket between the S4–S5 linker and S6 helix in a conformation-dependent manner. Using ¹⁹F-NMR to quantify site-specific binding by saturation transfer differences (STDs), we found strong STDs in inactivated, but not activated, NaChBac. Molecular dynamics simulations show a highly dynamic pocket in the activated conformation, limiting STD buildup. In contrast, drug binding to this pocket promotes and stabilizes the inactivated states. Our results provide direct experimental evidence showing distinctly different associations between the S4–S5 linker and S6 helix in activated and inactivated states. Specifically, an exchange occurs between interaction partners T140 and N234 of the same subunit in activation, and T140 and N225 of the domain-swapped subunit in slow inactivation. The drug action on slow inactivation of prokaryotic Na_V channels seems to have a mechanism similar to the recently proposed “door-wedge” action of the isoleucine-phenylalanine-methionine (IFM) motif on the fast inactivation of eukaryotic Na_V channels. Elucidating this gating mechanism points to a possible direction for conformation-dependent drug development.

Critical and Ictal Phases in Simulated EEG Signals on a Small-World Network

Healthy brain function is marked by neuronal network dynamics at or near the critical phase, which separates regimes of instability and stasis. A failure to remain at this critical point can lead to neurological disorders such as epilepsy, which is associated with pathological synchronization of neuronal oscillations. Using full Hodgkin-Huxley (HH) simulations on a Small-World Network, we are able to generate synthetic electroencephalogram (EEG) signals with intervals corresponding to seizure (ictal) or non-seizure (interictal) states that can occur based on the hyperexcitability of the artificial neurons and the strength and topology of the synaptic connections between them. These interictal simulations can be further classified into scale-free critical phases and disjoint subcritical exponential phases. By changing the HH parameters, we can model seizures due to a variety of causes, including traumatic brain injury (TBI), congenital channelopathies, and idiopathic etiologies, as well as the effects of anticonvulsant drugs. The results of this work may be used to help identify parameters from actual patient EEG or electrocorticographic (ECoG) data associated with ictogenesis, as well as generating simulated data for training machine-learning seizure prediction algorithms.

A Potential Mechanism of Sodium Channel Mediating the General Anesthesia Induced by Propofol

General anesthesia has revolutionized healthcare over the past 200 years and continues to show advancements. However, many phenomena induced by general anesthetics including paradoxical excitation are still poorly understood. Voltage-gated sodium channels (Na_V) were believed to be one of the proteins targeted during general anesthesia. Based on electrophysiological measurements before and after propofol treatments of different concentrations, we mathematically modified the Hodgkin–Huxley sodium channel formulations and constructed a thalamocortical model to investigate the potential roles of Na_V. The ion channels of individual neurons were modeled using the Hodgkin–Huxley type equations. The enhancement of propofol-induced GABAa current was simulated by increasing the maximal conductance and the time-constant of decay. Electroencephalogram (EEG) was evaluated as the post-synaptic potential from pyramidal (PY) cells. We found that a left shift in activation of Na_V was induced primarily by a low concentration of propofol (0.3–10 μM), while a left shift in inactivation of Na_V was induced by an increasing concentration (0.3–30 μM). Mathematical simulation indicated that a left shift of Na_V activation produced a Hopf bifurcation, leading to cell oscillations. Left shift of Na_V activation around a value of 5.5 mV in the thalamocortical models suppressed normal bursting of thalamocortical (TC) cells by triggering its chaotic oscillations. This led to irregular spiking of PY cells and an increased frequency in EEG readings. This observation suggests a mechanism leading to paradoxical excitation during general anesthesia. While a left shift in inactivation led to light hyperpolarization in individual cells, it inhibited the activity of the thalamocortical model after a certain depth of anesthesia. This finding implies that high doses of propofol inhibit the network partly by accelerating Na_V toward inactivation. Additionally, this result explains why the application of sodium channel blockers decreases the requirement for general anesthetics. Our study provides an insight into the roles that Na_V plays in the mechanism of general anesthesia. Since the activation and inactivation of Na_V are structurally independent, it should be possible to avoid side effects by state-dependent binding to the Na_V to achieve precision medicine in the future.

Effects of transient subordinators on the firing statistics of a neuron model driven by dichotomous noise

The behavior of a stochastic perfect integrate-and-fire (PIF) model of neurons is considered. The effect of temporally correlated random activity of synaptic inputs is modeled as a combination of an asymmetric dichotomous noise and a random operation time in the form of an inverse strictly increasing Lévy-type subordinator. Using a first-passage-time formulation, we find exact expressions for the output interspike interval (ISI) statistics. Particularly, it is shown that at some parameter regimes the ISI density exhibits a multimodal structure. Moreover, it is demonstrated that the coefficient of variation, the serial correlation coefficient, and the Fano factor display a nonmonotonic dependence on the mean input current μ, i.e., the ISI's regularity is maximized at an intermediate value of μ. The features of spike statistics, analytically revealed in our study, are compared with previously obtained results for a perfect integrate-and-fire neuron model driven by dichotomous noise (without subordination).

Spiral Wave Propagation in Communities with Spatially Correlated Heterogeneity

Many multicellular communities propagate signals in a directed manner via excitable waves. Cell-to-cell heterogeneity is a ubiquitous feature of multicellular communities, but the effects of heterogeneity on wave propagation are still unclear. Here, we use a minimal FitzHugh-Nagumo-type model to investigate excitable wave propagation in a two-dimensional heterogeneous community. The model shows three dynamic regimes in which waves either propagate directionally, die out, or spiral indefinitely, and we characterize how these regimes depend on the heterogeneity parameters. We find that in some parameter regimes, spatial correlations in the heterogeneity enhance directional propagation and suppress spiraling. However, in other regimes, spatial correlations promote spiraling, a surprising feature that we explain by demonstrating that these spirals form by a second, distinct mechanism. Finally, we characterize the dynamics using techniques from percolation theory. Despite the fact that percolation theory does not completely describe the dynamics quantitatively because it neglects the details of the excitable propagation, we find that it accounts for the transitions between the dynamic regimes and the general dependency of the spiral period on the heterogeneity and thus provides important insights. Our results reveal that the spatial structure of cell-to-cell heterogeneity can have important consequences for signal propagation in cellular communities.

Statistics of correlated percolation in a bacterial community

Signal propagation over long distances is a ubiquitous feature of multicellular communities, but cell-to-cell variability can cause propagation to be highly heterogeneous. Simple models of signal propagation in heterogenous media, such as percolation theory, can potentially provide a quantitative understanding of these processes, but it is unclear whether these simple models properly capture the complexities of multicellular systems. We recently discovered that in biofilms of the bacterium Bacillus subtilis, the propagation of an electrical signal is statistically consistent with percolation theory, and yet it is reasonable to suspect that key features of this system go beyond the simple assumptions of basic percolation theory. Indeed, we find here that the probability for a cell to signal is not independent from other cells as assumed in percolation theory, but instead is correlated with its nearby neighbors. We develop a mechanistic model, in which correlated signaling emerges from cell division, phenotypic inheritance, and cell displacement, that reproduces the experimentally observed correlations. We find that the correlations do not significantly affect the spatial statistics, which we rationalize using a renormalization argument. Moreover, the fraction of signaling cells is not constant in space, as assumed in percolation theory, but instead varies within and across biofilms. We find that this feature lowers the fraction of signaling cells at which one observes the characteristic power-law statistics of cluster sizes, consistent with our experimental results. We validate the model using a mutant biofilm whose signaling probability decays along the propagation direction. Our results reveal key statistical features of a correlated signaling process in a multicellular community. More broadly, our results identify extensions to percolation theory that do or do not alter its predictions and may be more appropriate for biological systems.

Percolation in a distorted square lattice

This paper presents a Monte Carlo study of percolation in a distorted square lattice, in which the adjacent sites are not equidistant. Starting with an undistorted lattice, the position of the lattice sites are shifted through a tunable parameter α to create a distorted empty lattice. In this model, two occupied neighboring sites are considered to be connected to each other in order to belong to the same cluster, if the distance between them is less than or equal to a certain value, called connection threshold d. While spanning becomes difficult in distorted lattices as is manifested by the increment of the percolation threshold p_{c} with α, an increased connection threshold d makes it easier for the system to percolate. The scaling behavior of the order parameter studied through relevant critical exponents, and the fractal dimension d_{f} of the percolating cluster at p_{c} suggest that this new type of percolation may belong to the same universality class as ordinary percolation. This model can be very useful in various realistic applications since it is almost impossible to find a natural system that is perfectly ordered.

Signal Percolation within a Bacterial Community

Signal transmission among cells enables long-range coordination in biological systems. However, the scarcity of quantitative measurements hinders the development of theories that relate signal propagation to cellular heterogeneity and spatial organization. We address this problem in a bacterial community that employs electrochemical cell-to-cell communication. We developed a model based on percolation theory, which describes how signals propagate through a heterogeneous medium. Our model predicts that signal transmission becomes possible when the community is organized near a critical phase transition between a disconnected and a fully connected conduit of signaling cells. By measuring population-level signal transmission with single-cell resolution in wild-type and genetically modified communities, we confirm that the spatial distribution of signaling cells is organized at the predicted phase transition. Our findings suggest that at this critical point, the population-level benefit of signal transmission outweighs the single-cell level cost. The bacterial community thus appears to be organized according to a theoretically predicted spatial heterogeneity that promotes efficient signal transmission.

Exact analytical solution of irreversible binary dynamics on networks

In binary cascade dynamics, the nodes of a graph are in one of two possible states (inactive, active), and nodes in the inactive state make an irreversible transition to the active state, as soon as their precursors satisfy a predetermined condition. We introduce a set of recursive equations to compute the probability of reaching any final state, given an initial state, and a specification of the transition probability function of each node. Because the naive recursive approach for solving these equations takes factorial time in the number of nodes, we also introduce an accelerated algorithm, built around a breath-first search procedure. This algorithm solves the equations as efficiently as possible in exponential time.

Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent α_{c}≈0.402, which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

Consciousness as a global property of brain dynamic activity

We seek general principles of the structure of the cellular collective activity associated with conscious awareness. Can we obtain evidence for features of the optimal brain organization that allows for adequate processing of stimuli and that may guide the emergence of cognition and consciousness? Analyzing brain recordings in conscious and unconscious states, we followed initially the classic approach in physics when it comes to understanding collective behaviours of systems composed of a myriad of units: the assessment of the number of possible configurations (microstates) that the system can adopt, for which we use a global entropic measure associated with the number of connected brain regions. Having found maximal entropy in conscious states, we then inspected the microscopic nature of the configurations of connections using an adequate complexity measure and found higher complexity in states characterized not only by conscious awareness but also by subconscious cognitive processing, such as sleep stages. Our observations indicate that conscious awareness is associated with maximal global (macroscopic) entropy and with the short time scale (microscopic) complexity of the configurations of connected brain networks in pathological unconscious states (seizures and coma), but the microscopic view captures the high complexity in physiological unconscious states (sleep) where there is information processing. As such, our results support the global nature of conscious awareness, as advocated by several theories of cognition. We thus hope that our studies represent preliminary steps to reveal aspects of the structure of cognition that leads to conscious awareness.

Biological conservation law as an emerging functionality in dynamical neuronal networks

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Disconnecting Consciousness: Is There a Common Anesthetic End Point?

A quest for a systems-level neuroscientific basis of anesthetic-induced loss and return of consciousness has been in the forefront of research for the past 2 decades. Recent advances toward the discovery of underlying mechanisms have been achieved using experimental electrophysiology, multichannel electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. By the careful dosing of various volatile and IV anesthetic agents to the level of behavioral unresponsiveness, both specific and common changes in functional and effective connectivity across large-scale brain networks have been discovered and interpreted in the context of how the synthesis of neural information might be affected during anesthesia. The results of most investigations to date converge toward the conclusion that a common neural correlate of anesthetic-induced unresponsiveness is a consistent depression or functional disconnection of lateral frontoparietal networks, which are thought to be critical for consciousness of the environment. A reduction in the repertoire of brain states may contribute to the anesthetic disruption of large-scale information integration leading to unconsciousness. In future investigations, a systematic delineation of connectivity changes with multiple anesthetics using the same experimental design, and the same analytical method will be desirable. The critical neural events that account for the transition between responsive and unresponsive states should be assessed at similar anesthetic doses just below and above the loss or return of responsiveness. There will also be a need to identify a robust, sensitive, and reliable measure of information transfer. Ultimately, finding a behavior-independent measure of subjective experience that can track covert cognition in unresponsive subjects and a delineation of causal factors versus correlated events will be essential to understand the neuronal basis of human consciousness and unconsciousness.

Applied analysis of ultrasound-guided ilioinguinal and iliohypogastric nerve blocks in the radical surgery of aged cervical cancer

In the present study, we evaluated the effects of ultrasound-guided ilioinguinal and iliohypogastric nerve blocks and intratracheal general anesthesia in the radical surgery of aged cervical cancer. From June 2014 to December 2014, 62 patients diagnosed with aged cervical cancer were enrolled in this study. The patients conformed to the indication of cervical cancer radical surgery. Patients were randomly divided into the test group and the control group according to the random number table with 31 individuals in each group. General anesthesia and compound ultrasound-guided ilioinguinal and iliohypogastric nerve blocks were applied in the test group, while intratracheal general anesthesia was used in the control group. The results showed that the operative time, awake time and the total dosage of propofol and sufentanil in the test group were significantly lower than those in the control group. The postoperative VAS score, the times to press the automatic control bump and continuous application time of the automatic control bump in the test group were significantly less than those in the control group. The occurrence rate of anesthesia-related adverse reactions in the test group during the perioperative period was significantly lower than that in the control group. Differences were statistically significant (P<0.05). We concluded that ultrasound-guided ilioinguinal and iliohypogastric nerve blocks significantly improved the analgesic effects during the perioperative and postoperative period in cervical cancer radical surgeries.

Fluorine-19 NMR and computational quantification of isoflurane binding to the voltage-gated sodium channel NaChBac

Voltage-gated sodium channels (Na_V) play an important role in general anesthesia. Electrophysiology measurements suggest that volatile anesthetics such as isoflurane inhibit Na_V by stabilizing the inactivated state or altering the inactivation kinetics. Recent computational studies suggested the existence of multiple isoflurane binding sites in Na_V, but experimental binding data are lacking. Here we use site-directed placement of ¹⁹F probes in NMR experiments to quantify isoflurane binding to the bacterial voltage-gated sodium channel NaChBac. ¹⁹F probes were introduced individually to S129 and L150 near the S4-S5 linker, L179 and S208 at the extracellular surface, T189 in the ion selectivity filter, and all phenylalanine residues. Quantitative analyses of ¹⁹F NMR saturation transfer difference (STD) spectroscopy showed a strong interaction of isoflurane with S129, T189, and S208; relatively weakly with L150; and almost undetectable with L179 and phenylalanine residues. An orientation preference was observed for isoflurane bound to T189 and S208, but not to S129 and L150. We conclude that isoflurane inhibits NaChBac by two distinct mechanisms: (i) as a channel blocker at the base of the selectivity filter, and (ii) as a modulator to restrict the pivot motion at the S4-S5 linker and at a critical hinge that controls the gating and inactivation motion of S6.

Statistical mechanics of consciousness: Maximization of information content of network is associated with conscious awareness

It is said that complexity lies between order and disorder. In the case of brain activity and physiology in general, complexity issues are being considered with increased emphasis. We sought to identify features of brain organization that are optimal for sensory processing, and that may guide the emergence of cognition and consciousness, by analyzing neurophysiological recordings in conscious and unconscious states. We find a surprisingly simple result: Normal wakeful states are characterized by the greatest number of possible configurations of interactions between brain networks, representing highest entropy values. Therefore, the information content is larger in the network associated to conscious states, suggesting that consciousness could be the result of an optimization of information processing. These findings help to guide in a more formal sense inquiry into how consciousness arises from the organization of matter.

Effects of Propofol General Anesthesia on Olfactory Relearning

How general anesthesia interferes with sensory processing to cause amnesia remains unclear. Here, we show that activation of a learning-associated immediate early gene in rat olfactory cortices is uninterrupted by propofol, an intravenous general anesthetic with putative actions on the inhibitory GABA_A receptors. Once learned under anesthesia, a novel odor can no longer re-activate the same high-level transcription programming during subsequent conscious relearning. Behavioral tests indicate that the animals’ ability to consciously relearn a pure odorant, first experienced under general anesthesia, is indeed compromised. In contrast, when a mixture of two novel odorants is first experienced under anesthesia and then relearned consciously in pairs with one of the components, the animals show a deficit in relearning only the component but not the mixture. Our results reveal a previously unknown mechanism of unconscious memory due to irreplaceable neuronal commitment under general anesthesia and support the notion that general anesthesia acts at stages beyond cellular coding to disrupt sensory integration for higher-order association.

Statistical mechanics of neocortical interactions: Large-scale EEG influences on molecular processes

Calculations further support the premise that large-scale synchronous firings of neurons may affect molecular processes. The context is scalp electroencephalography (EEG) during short-term memory (STM) tasks. The mechanism considered is Π=p+qA (SI units) coupling, where p is the momenta of free Ca(2+) waves, q the charge of Ca(2+) in units of the electron charge, and A the magnetic vector potential of current I from neuronal minicolumnar firings considered as wires, giving rise to EEG. Data has processed using multiple graphs to identify sections of data to which spline-Laplacian transformations are applied, to fit the statistical mechanics of neocortical interactions (SMNI) model to EEG data, sensitive to synaptic interactions subject to modification by Ca(2+) waves.