Lateral inhibition in V1 controls neural & perceptual contrast sensitivity.

Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

Amplification of olfactory signals by Anoctamin 9 is important for mammalian olfaction.

Sensing smells of foods, prey, or predators determines animal survival. Olfactory sensory neurons in the olfactory epithelium (OE) detect odorants, where cAMP and Ca2+ play a significant role in transducing odorant inputs to electrical activity. Here we show Anoctamin 9, a cation channel activated by cAMP/PKA pathway, is expressed in the OE and amplifies olfactory signals. Ano9-deficient mice had reduced olfactory behavioral sensitivity, electro-olfactogram signals, and neural activity in the olfactory bulb. In line with the difference in olfaction between birds and other vertebrates, chick ANO9 failed to respond to odorants, whereas chick CNGA2, a major transduction channel, showed greater responses to cAMP. Thus, we concluded that the signal amplification by ANO9 is important for mammalian olfactory transduction.

Enhanced decomposition of caffeine by water plasma combined with mist generator: Effect of operational parameter and decomposition pathway.

Water plasma coupled with mist generator was introduced to perform the decomposition of caffeine (CAF) wastewater. The mist-shaped water molecule was directly used for plasma-forming gas with no additional gas. The influence of arc current on the decomposition of CAF was elucidated in detail. With the increase of input power from 0.8 to 1.1 kW according to arc current, the removal efficiency of total organic carbon (TOC) and CAF increased, reaching 91.1 and 99.8% at 9.5 A, respectively. H2, CO, CO2, and N2 were major effluent gaseous species, of which the H2 generation was more than 40% for all conditions. The concentration of nitrate in the effluent liquids was the highest at 9.5 A due to a higher oxidation environment. The H, O, and OH as reactive species formed via the dissociation of water molecules were demonstrated, and the plasma temperatures were at over 5000 K. The detailed decomposition pathway was deduced based on eleven intermediate products identified in this process. Electron impact and hydroxyl radical were found to take leading roles in the decomposition of CAF.

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.

Design principles of PI(4,5)P2 clustering under protein-free conditions: Specific cation effects and calcium-potassium synergy.

Phosphatidylinositol 4,5-bisphosphate (PIP2) clustering is a key component in cell signaling, yet little is known about the atomic-level features of this phenomenon. Network-theoretic analysis of multimicrosecond atomistic simulations of PIP2 containing asymmetric bilayers under protein-free conditions, presented here, reveals how design principles of PIP2 clustering are determined by the specific cation effects. Ca2+ generates large clusters (6% are pentamer or larger) by adding existing PIP2 dimers formed by strong O‒Ca2+‒O bridging interactions of unprotonated P4/P5 phosphates. In contrast, monovalent cations (Na+ and K+) form smaller and less-stable clusters by preferentially adding PIP2 monomers. Despite having the same net charge, the affinity to P4/P5 is higher for Na+, while affinity toward glycerol P1 is higher for K+. Consequently, a mixture of K+ and Ca2+ (as would be produced by Ca2+ influx) synergistically yields larger and more stable clusters than Ca2+ alone due to the different binding preferences of these cations.

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.

Drinking coffee enhances neurocognitive function by reorganizing brain functional connectivity.

The purpose of this study was to identify the mechanisms underlying effects of coffee on cognition in the context of brain networks. Here we investigated functional connectivity before and after drinking coffee using graph-theoretic analysis of electroencephalography (EEG). Twenty-one healthy adults voluntarily participated in this study. The resting-state EEG data and results of neuropsychological tests were consecutively acquired before and 30 min after coffee consumption. Graph analyses were performed and compared before and after coffee consumption. Correlation analyses were conducted to assess the relationship between changes in graph measures and those in cognitive function tests. Functional connectivity (FC) was reorganized toward more efficient network properties after coffee consumption. Performance in Digit Span tests and Trail Making Test Part B improved after coffee consumption, and the improved performance in executive function was correlated with changes in graph measures, reflecting a shift toward efficient network properties. The beneficial effects of coffee on cognitive function might be attributed to the reorganization of FC toward more efficient network properties. Based on our findings, the patterns of network reorganization could be used as quantitative markers to elucidate the mechanisms underlying the beneficial effects of coffee on cognition, especially executive function.

Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models.

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5'-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer's disease, amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

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.

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant's speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

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.

Predicting the Size of Benign Thyroid Nodules and Analysis of Associated Factors That Affect Nodule Size.

This study aimed to identify factors that affect the size of benign thyroid nodules and to predict nodule size by using a newly developed model. Because most thyroid nodules are benign, they are commonly only monitored. Only a few studies have evaluated the natural progression or regression of benign thyroid nodules. Large-scale studies on the subject are nonexistent. Between January 2001 and December 2011, our study subjects were selected from among 1,564 patients with benign thyroid nodules (2,469 nodules) in a retrospective analysis. We measured nodule size and volume and attempted to predict nodule size by using a newly developed model. Nodules were considered to have increased in size if the total volume increased by >15%. Nodules that increased in size over time required a longer follow-up period than nodules that decreased in size. The proportion of females and the cystic proportion of the nodules were relatively high in our study sample. For thyroid nodules that increased in size, we analyzed potential predictive factors. Larger nodule volume, extended follow-up period, and high cystic proportion were positively associated with increased nodule size. According to the model we developed in our study, the nodules in the group with an increase in size grew at an approximate rate of 0.034 cm(3) per year when controlled for other factors. Percutaneous ethanol injection or radiofrequency ablation is performed for cosmetic purposes and proper functioning if or when nodules reach a certain size. The model used in our study may offer helpful insight in determining an optimal treatment schedule for benign thyroid nodules.