Neural Information Processing and Computations of Two-Input Synapses.

Information processing in artificial neural networks is largely dependent on the nature of neuron models. While commonly used models are designed for linear integration of synaptic inputs, accumulating experimental evidence suggests that biological neurons are capable of nonlinear computations for many converging synaptic inputs via homo- and heterosynaptic mechanisms. This nonlinear neuronal computation may play an important role in complex information processing at the neural circuit level. Here we characterize the dynamics and coding properties of neuron models on synaptic transmissions delivered from two hidden states. The neuronal information processing is influenced by the cooperative and competitive interactions among synapses and the coherence of the hidden states. Furthermore, we demonstrate that neuronal information processing under two-input synaptic transmission can be mapped to linearly nonseparable XOR as well as basic AND/OR operations. In particular, the mixtures of linear and nonlinear neuron models outperform the fashion-MNIST test compared to the neural networks consisting of only one type. This study provides a computational framework for assessing information processing of neuron and synapse models that may be beneficial for the design of brain-inspired artificial intelligence algorithms and neuromorphic systems.

The structural aspects of neural dynamics and information flow.

BACKGROUND: Neurons have specialized structures that facilitate information transfer using electrical and chemical signals. Within the perspective of neural computation, the neuronal structure is an important prerequisite for the versatile computational capabilities of neurons resulting from the integration of diverse synaptic input patterns, complex interactions among the passive and active dendritic local currents, and the interplay between dendrite and soma to generate action potential output. For this, characterization of the relationship between the structure and neuronal spike dynamics could provide essential information about the cellular-level mechanism supporting neural computations. RESULTS: This work describes simulations and an information-theoretic analysis to investigate how specific neuronal structure affects neural dynamics and information processing. Correlation analysis on the Allen Cell Types Database reveals biologically relevant structural features that determine neural dynamics-eight highly correlated structural features are selected as the primary set for characterizing neuronal structures. These features are used to characterize biophysically realistic multi-compartment mathematical models for primary neurons in the direct and indirect hippocampal pathways consisting of the pyramidal cells of Cornu Ammonis 1 (CA1) and CA3 and the granule cell in the dentate gyrus (DG). Simulations reveal that the dynamics of these neurons vary depending on their specialized structures and are highly sensitive to structural modifications. Information-theoretic analysis confirms that structural factors are critical for versatile neural information processing at a single-cell and a neural circuit level; not only basic AND/OR but also linearly non-separable XOR functions can be explained within the information-theoretic framework. CONCLUSIONS: Providing quantitative information on the relationship between the structure and the dynamics/information flow of neurons, this work would help us understand the design and coding principles of biological neurons and may be beneficial for designing biologically plausible neuron models for artificial intelligence (AI) systems.

Characterization of multiscale logic operations in the neural circuits.

Background: Ever since the seminal work by McCulloch and Pitts, the theory of neural computation and its philosophical foundation known as 'computationalism' have been central to brain-inspired artificial intelligence (AI) technologies. The present study describes neural dynamics and neural coding approaches to understand the mechanisms of neural computation. The primary focus is to characterize the multiscale nature of logic computations in the brain, which might occur at a single neuron level, between neighboring neurons via synaptic transmission, and at the neural circuit level. Results: For this, we begin the analysis with simple neuron models to account for basic Boolean logic operations at a single neuron level and then move on to the phenomenological neuron models to explain the neural computation from the viewpoints of neural dynamics and neural coding. The roles of synaptic transmission in neural computation are investigated using biologically realistic multi-compartment neuron models: two representative computational entities, CA1 pyramidal neuron in the hippocampus and Purkinje fiber in the cerebellum, are analyzed in the information-theoretic framework. We then construct two-dimensional mutual information maps, which demonstrate that the synaptic transmission can process not only basic AND/OR Boolean logic operations but also the linearly non-separable XOR function. Finally, we provide an overview of the evolutionary algorithm and discuss its benefits in automated neural circuit design for logic operations. Conclusions: This study provides a comprehensive perspective on the multiscale logic operations in the brain from both neural dynamics and neural coding viewpoints. It should thus be beneficial for understanding computational principles of the brain and may help design biologically plausible neuron models for AI devices.

A least action principle for interceptive walking.

The principle of least effort has been widely used to explain phenomena related to human behavior ranging from topics in language to those in social systems. It has precedence in the principle of least action from the Lagrangian formulation of classical mechanics. In this study, we present a model for interceptive human walking based on the least action principle. Taking inspiration from Lagrangian mechanics, a Lagrangian is defined as effort minus security, with two different specific mathematical forms. The resulting Euler-Lagrange equations are then solved to obtain the equations of motion. The model is validated using experimental data from a virtual reality crossing simulation with human participants. We thus conclude that the least action principle provides a useful tool in the study of interceptive walking.

Computational modeling of the effects of autophagy on amyloid-ß peptide levels.

BACKGROUND: Autophagy is an evolutionarily conserved intracellular process that is used for delivering proteins and organelles to the lysosome for degradation. For decades, autophagy has been speculated to regulate amyloid-ß peptide (Aß) accumulation, which is involved in Alzheimer's disease (AD); however, specific autophagic effects on the Aß kinetics only have begun to be explored. RESULTS: We develop a mathematical model for autophagy with respect to Aß kinetics and perform simulations to understand the quantitative relationship between Aß levels and autophagy activity. In the case of an abnormal increase in the Aß generation, the degradation, secretion, and clearance rates of Aß are significantly changed, leading to increased levels of Aß. When the autophagic Aß degradation is defective in addition to the increased Aß generation, the Aß-regulation failure is accompanied by elevated concentrations of autophagosome and autolysosome, which may further clog neurons. CONCLUSIONS: The model predicts that modulations of different steps of the autophagy pathway (i.e., Aß sequestration, autophagosome maturation, and intralysosomal hydrolysis) have significant step-specific and combined effects on the Aß levels and thus suggests therapeutic and preventive implications of autophagy in AD.

Predicting Energy Expenditure During Gradient Walking With a Foot Monitoring Device: Model-Based Approach.

BACKGROUND: Many recent commercial devices aim at providing a practical way to measure energy expenditure. However, those devices are limited in accuracy. OBJECTIVE: This study aimed to build a model of energy consumption during walking applicable to a range of sloped surfaces, used in conjunction with a simple, wearable device. METHODS: We constructed a model of energy consumption during gradient walking by using arguments based in mechanics. We built a foot monitoring system that used pressure sensors on the foot insoles. We did experiments in which participants walked on a treadmill wearing the foot monitoring system, and indirect calorimetry was used for validation. We found the parameters of the model by fitting to the data. RESULTS: When walking at 1.5 m/s, we found that the model predicted a calorie consumption rate of 5.54 kcal/min for a woman with average height and weight and 6.89 kcal/min for an average man. With the obtained parameters, the model predicted the data with a root-mean-square deviation of 0.96 kcal/min and median percent error of 12.4%. CONCLUSIONS: Our model was found to be an accurate predictor of energy consumption when walking on a range of slopes. The model uses few variables; thus, it can be used in conjunction with a convenient wearable device.

Numerical study of entrainment of the human circadian system and recovery by light treatment.

BACKGROUND: While the effects of light as a zeitgeber are well known, the way the effects are modulated by features of the sleep-wake system still remains to be studied in detail. METHODS: A mathematical model for disturbance and recovery of the human circadian system is presented. The model combines a circadian oscillator and a sleep-wake switch that includes the effects of orexin. By means of simulations, we characterize the period-locking zone of the model, where a stable 24-hour circadian rhythm exists, and the occurrence of circadian disruption due to both insufficient light and imbalance in orexin. We also investigate how daily bright light treatments of short duration can recover the normal circadian rhythm. RESULTS: It is found that the system exhibits continuous phase advance/delay at lower/higher orexin levels. Bright light treatment simulations disclose two optimal time windows, corresponding to morning and evening light treatments. Among the two, the morning light treatment is found effective in a wider range of parameter values, with shorter recovery time. CONCLUSIONS: This approach offers a systematic way to determine the conditions under which circadian disruption occurs, and to evaluate the effects of light treatment. In particular, it could potentially offer a way to optimize light treatments for patients with circadian disruption, e.g., sleep and mood disorders, in clinical settings.

Failure of Arm Movement Control in Stroke Patients, Characterized by Loss of Complexity.

We study the mechanism of human arm-posture control by means of nonlinear dynamics and quantitative time series analysis methods. Utilizing linear and nonlinear measures in combination, we find that pathological tremors emerge in patient dynamics and serve as a main feature discriminating between normal and patient groups. The deterministic structure accompanied with loss of complexity inherent in the tremor dynamics is also revealed. To probe the underlying mechanism of the arm-posture dynamics, we further analyze the coupling patterns between joints and components, and discuss their roles in breaking of the organization structure. As a result, we elucidate the mechanisms in the arm-posture dynamics of normal subjects responding to the gravitational force and for the reduction of the dynamic degrees of freedom in the patient dynamics. This study provides an integrated framework for the origin of the loss of complexity in the dynamics of patients as well as the coupling structure in the arm-posture dynamics.

Synaptotagmin-1 binds to PIP(2)-containing membrane but not to SNAREs at physiological ionic strength.

The Ca(2+) sensor synaptotagmin-1 is thought to trigger membrane fusion by binding to acidic membrane lipids and SNARE proteins. Previous work has shown that binding is mediated by electrostatic interactions that are sensitive to the ionic environment. However, the influence of divalent or polyvalent ions, at physiological concentrations, on synaptotagmin's binding to membranes or SNAREs has not been explored. Here we show that binding of rat synaptotagmin-1 to membranes containing phosphatidylinositol 4,5-bisphosphate (PIP2) is regulated by charge shielding caused by the presence of divalent cations. Surprisingly, polyvalent ions such as ATP and Mg(2+) completely abrogate synaptotagmin-1 binding to SNAREs regardless of the presence of Ca(2+). Altogether, our data indicate that at physiological ion concentrations Ca(2+)-dependent synaptotagmin-1 binding is confined to PIP2-containing membrane patches in the plasma membrane, suggesting that membrane interaction of synaptotagmin-1 rather than SNARE binding triggers exocytosis of vesicles.

Autophagy mediates phase transitions from cell death to life.

Autophagy is a lysosomal degradation pathway, which is critical for maintaining normal cellular functions. Despite considerable advances in defining the specific molecular mechanism governing the autophagy pathway during the last decades, we are still far from understanding the underlying principle of the autophagy machinery and its complex role in human disease. As an alternative attempt to reinvigorate the search for the principle of the autophagy pathway, we in this study make use of the computer-aided analysis, complementing current molecular-level studies of autophagy. Specifically, we propose a hypothesis that autophagy mediates cellular phase transitions and demonstrate that the autophagic phase transitions are essential to the maintenance of normal cellular functions and critical in the fate of a cell, i.e., cell death or survival. This study should provide valuable insight into how interactions of sub-cellular components such as genes and protein modules/complexes regulate autophagy and then impact on the dynamic behaviors of living cells as a whole, bridging the microscopic molecular-level studies and the macroscopic cellular-level and physiological approaches.

Quantitative indices of autophagy activity from minimal models.

BACKGROUND: A number of cellular- and molecular-level studies of autophagy assessment have been carried out with the help of various biochemical and morphological indices. Still there exists ambiguity for the assessment of the autophagy status and of the causal relationship between autophagy and related cellular changes. To circumvent such difficulties, we probe new quantitative indices of autophagy which are important for defining autophagy activation and further assessing its roles associated with different physiopathological states. METHODS: Our approach is based on the minimal autophagy model that allows us to understand underlying dynamics of autophagy from biological experiments. Specifically, based on the model, we reconstruct the experimental context-specific autophagy profiles from the target autophagy system, and two quantitative indices are defined from the model-driven profiles. The indices are then applied to the simulation-based analysis, for the specific and quantitative interpretation of the system. RESULTS: Two quantitative indices measuring autophagy activities in the induction of sequestration fluxes and in the selective degradation are proposed, based on the model-driven autophagy profiles such as the time evolution of autophagy fluxes, levels of autophagosomes/autolysosomes, and corresponding cellular changes. Further, with the help of the indices, those biological experiments of the target autophagy system have been successfully analyzed, implying that the indices are useful not only for defining autophagy activation but also for assessing its role in a specific and quantitative manner. CONCLUSIONS: Such quantitative autophagy indices in conjunction with the computer-aided analysis should provide new opportunities to characterize the causal relationship between autophagy activity and the corresponding cellular change, based on the system-level understanding of the autophagic process at good time resolution, complementing the current in vivo and in vitro assays.

Simultaneous analysis and peroxynitrite-scavenging activity of galloylated flavonoid glycosides and ellagic acid in Euphorbia supina.

The herbs of Euphorbia supina (Euphorbiaceae) have been used to treat hemorrhage, chronic bronchitis, hepatitis, jaundice, diarrhea, gastritis, and hemorrhoids as a medicinal herb. This work is aimed to qualitatively and quantitatively analyze the polyphenols with peroxynitrite-scavenging activities. The eight compounds: gallic acid, methyl gallate, avicularin, astragalin, juglanin, isoquercitrin 6″-gallate, astragalin 6″-gallate, and ellagic acid, were isolated from E. supina and used for HPLC analysis and peroxynitrite (ONOO(-))-scavenging assay. Simultaneous analysis of the eight compounds was performed on MeOH extract and its fractions. The contents in MeOH extract and peroxynitrite-scavenging activities of the dimer of gallic acid, ellagic acid (15.64 mg/g; IC50 0.89 µM), and two galloylated flavonoid glycosides, astragalin 6″-gallate (13.72 mg/g; IC50 1.43 µM) and isoquercitrin 6″-gallate (16.99 mg/g; IC50 1.75 µM), were high, compared to other compounds. The legendary uses of E. supina could be attributed to the high content of polyphenols, particularly ellagic acid, isoquercitrin 6″-gallate, and astragalin 6″-gallate as active principles.

Mathematical model for glucose regulation in the whole-body system.

The human body needs continuous and stable glucose supply for maintaining its biological functions. Stable glucose supply comes from the homeostatic regulation of the blood glucose level, which is controlled by various glucose consuming or producing organs. Therefore, it is important to understand the whole-body glucose regulation mechanism. In this article, we describe various mathematical models proposed for glucose regulation in the human body, and discuss the difficulty and limitation in reproducing real processes of glucose regulation.

Mathematical models for insulin secretion in pancreatic ß-cells.

Insulin secretion is one of the most characteristic features of ß-cell physiology. As it plays a central role in glucose regulation, a number of experimental and theoretical studies have been performed since the discovery of the pancreatic ß-cell. This review article aims to give an overview of the mathematical approaches to insulin secretion. Beginning with the bursting electrical activity in pancreatic ß-cells, we describe effects of the gap-junction coupling between ß-cells on the dynamics of insulin secretion. Then, implications of paracrine interactions among such islet cells as α-, ß-, and δ-cells are discussed. Finally, we present mathematical models which incorporate effects of glycolysis and mitochondrial glucose metabolism on the control of insulin secretion.

Antinociceptive anti-inflammatory effect of Monotropein isolated from the root of Morinda officinalis.

The root of Morinda officinalis (Rubiaceae) is used to treat rheumatoid arthritis and impotence in the traditional Oriental medicine. To identify the antinociceptive anti-inflammatory components of this crude drug, we adopted an activity-directed fractionation approach. The active fraction of the BuOH extract of M. officinalis root was subjected to silica gel and ODS column chromatography to yield two diterpenes, compounds 1 and 2 and these were identified as monotropein and deacetylasperulosidic acid, respectively. The iridoid glycoside, monotropein, was tested for its anti-inflammatory antinociceptive effects using hot plate- and writhing antinociceptive assays and by using carrageenan-induced anti-inflammatory assays in mice and rats. Pretreatment with monotropein (at 20, 30 mg/kg/d, p.o.) significantly reduced stretching episodes and prolonged action time in mice. It also significantly reduced acute paw edema by carrageenan in rats. These results indicate that monotropein contributes to the antinociceptive and anti-inflammatory action of Morinda officinalis root.

Antimutagenic activity of flavonoids from the heartwood of Rhus verniciflua.

Pretreatment of the methanolic extract of the heartwood of Rhus verniciflua (Anacardiaceae) to rats prevented the activation of hepatic microsomal cytochrome p(450) enzymes, inhibition of hepatic glutathione S-transferase by bromobenzene treatment, respectively, and therefore significantly decreased malondialdehyde content in the rat. The Ames test showed that the addition of 1.0 mg/plate of the methanolic extract or the EtOAc fraction of the Rhus verniciflua heartwood extract potentially inhibited the mutagenicity by aflatoxin B(1). Column chromatography of the EtOAc fraction yielded four flavonoids, garbanzol (1), sulfuretin (2), fisetin (3), fustin (4), mollisacasidin (5). When these components were subjected to the Ames test, it was found that sulfuretin might effectively prevent the metabolic activation or scavenge electrophilic intermediates capable of causing mutation. In contrast, fustin showed a dose-independent antimutagenic activity and it has mutagenic/antimutagenic activity. However, a mixture of sulfuretin and fustin (1:1) exhibited dose-dependent antimutagenicity indicating that sulfuretin inhibited the mutagenicity of fustin. These results suggest that the extract of Rhus verniciflua heartwood containing flavonoid complex could be a potent anticarcinogen.