Function Of Neural Systems Research Articles

Nonlinearities compensator based on SORBFNN in ultra-long-haul high-capacity CO-OFDM systems

Radial basis function neural network is recognized as being the most useful type of neural network for a nonlinear equalizer in the CO-OFDM system. In the history of nonlinear equalizers, the focus has always been on mitigation of nonlinearity. Despite this interest, no one to the best of our knowledge has studied ultra-long-haul transmission length. Previous work has been limited to normal transmission length, i.e., up to 1400 km. In ultra-long-haul optical fiber communication systems, the optical signal becomes very much complex at longer distance, i.e., around over 8000 km. It has not yet been established whether radial basis function neural network can do the same improvement in performance at ultra-long-haul transmission length. A basic issue of a customary radial basis function neural system is its low performance at such a long distance. This paper presents a new approach to compensating nonlinearities in ultra-long-haul optical fiber communication CO-OFDM systems. In this new approach, a radial basis function with nonlinear equalizer (first hidden layer with automatically adjusted structure, i.e., number of neuron nodes in the hidden layer and the different parameters of the structure through learning, and second hidden layer with fixed structure) based on two hidden layers has been recommended. The aim of this study is to examine the performance of a self-organizing radial basis function neural network-based nonlinear equalizer (SORBFNN-NLE) for the mitigation of nonlinearities in ultra-long-haul CO-OFDM systems. Better performance of the SORBFNN is attributed due to self-organizing structure and training of network. From this study, it has been observed that the proposed SORBFNN-NLE performs well regarding Q-factor and OSNR at an ultra-long distance.

Concurrence of form and function in developing networks and its role in synaptic pruning

A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network with an evolving mechanism for the birth and death of synapses. A feedback loop then arises leading to two qualitatively different types of behaviour. In one, the network structure becomes heterogeneous and dissasortative, and the system displays good memory performance; furthermore, the structure is optimised for the particular memory patterns stored during the process. In the other, the structure remains homogeneous and incapable of pattern retrieval. These findings provide an inspiring picture of brain structure and dynamics that is compatible with experimental results on early brain development, and may help to explain synaptic pruning. Other evolving networks—such as those of protein interactions—might share the basic ingredients for this feedback loop and other questions, and indeed many of their structural features are as predicted by our model.

Statistics of Natural Communication Signals Observed in the Wild Identify Important Yet Neglected Stimulus Regimes in Weakly Electric Fish.

Sensory systems evolve in the ecological niches that each species is occupying. Accordingly, encoding of natural stimuli by sensory neurons is expected to be adapted to the statistics of these stimuli. For a direct quantification of sensory scenes, we tracked natural communication behavior of male and female weakly electric fish, Apteronotus rostratus, in their Neotropical rainforest habitat with high spatiotemporal resolution over several days. In the context of courtship, we observed large quantities of electrocommunication signals. Echo responses, acknowledgment signals, and their synchronizing role in spawning demonstrated the behavioral relevance of these signals. In both courtship and aggressive contexts, we observed robust behavioral responses in stimulus regimes that have so far been neglected in electrophysiological studies of this well characterized sensory system and that are well beyond the range of known best frequency and amplitude tuning of the electroreceptor afferents' firing rate modulation. Our results emphasize the importance of quantifying sensory scenes derived from freely behaving animals in their natural habitats for understanding the function and evolution of neural systems.SIGNIFICANCE STATEMENT The processing mechanisms of sensory systems have evolved in the context of the natural lives of organisms. To understand the functioning of sensory systems therefore requires probing them in the stimulus regimes in which they evolved. We took advantage of the continuously generated electric fields of weakly electric fish to explore electrosensory stimulus statistics in their natural Neotropical habitat. Unexpectedly, many of the electrocommunication signals recorded during courtship, spawning, and aggression had much smaller amplitudes or higher frequencies than stimuli used so far in neurophysiological characterizations of the electrosensory system. Our results demonstrate that quantifying sensory scenes derived from freely behaving animals in their natural habitats is essential to avoid biases in the choice of stimuli used to probe brain function.

Robust generalized Mittag-Leffler synchronization of fractional order neural networks with discontinuous activation and impulses

Fractional order system is playing an increasingly important role in terms of both theory and applications. In this paper we investigate the global existence of Filippov solutions and the robust generalized Mittag-Leffler synchronization of fractional order neural networks with discontinuous activation and impulses. By means of growth conditions, differential inclusions and generalized Gronwall inequality, a sufficient condition for the existence of Filippov solution is obtained. Then, sufficient criteria are given for the robust generalized Mittag-Leffler synchronization between discontinuous activation function of impulsive fractional order neural network systems with (or without) parameter uncertainties, via a delayed feedback controller and M-Matrix theory. Finally, four numerical simulations demonstrate the effectiveness of our main results.

Viscosity prediction for six pure refrigerants using different artificial neural networks

The viscosities of six environmentally friendly pure refrigerants with low GWP are predicted based on three artificial neural network (ANN) models: back propagation neural network (BPNN), radial biased function neural network (RBFNN) and adaptive neuro fuzzy interface system (ANFIS). A total of 1089 experimental data are used to train and test the models. Temperature, pressure and density are considered as input variables of networks. The optimal parameters are obtained through the stepwise searching method. The predicted values using the three optimized ANN models with values of experimental data are compared. Moreover, the viscosity of the six refrigerants in saturated liquid state are predicted using all three models in a wide temperature range. The results show that the deviations of almost all data are less than 5.0% and the ANFIS has the best performance.

Development of a prediction model for estimating tractor engine torque based on soft computing and low cost sensors

Torque estimation needs intensive efforts and costly sensors. In this research, a model was proposed based on soft computing to estimate the ITM285 tractor engine torque using some low cost sensors. To this end, two models including the radial basis function (RBF) neural network and adaptive neuro fuzzy inference system (ANFIS) were used. Thirteen training algorithms were examined to train the RBF. These algorithms were compared using three statistical methods, namely k-fold cross validation, completely randomized design (CRD) and least significant difference (LSD). Moreover, three methods, namely grid partitioning (GP), sub-clustering (SC) and fuzzy c-means (FCM), were used to construct the fuzzy inference system (FIS). However, the FCM was the most suitable method. The sensitivity analysis showed that only measuring engine speed, fuel mass flow and exhaust gas temperature was sufficient for proper engine torque estimation. The RBF had a better performance (R2 = 0.99, RMSE = 0.5 and EF = 0.99) than the ANFIS and hence, was suggested for estimating the engine torque.

The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders.

Neuroeconomics is providing insights into the neural bases of decision-making in normal and pathological conditions. In the neuropsychiatric domain, this discipline investigates how abnormal functioning of neural systems associated with reward processing and cognitive control promotes different disorders, and whether such evidence may inform treatments. This endeavor is crucial when studying different types of addiction, which share a core promoting mechanism in the imbalance between impulsive subcortical neural signals associated with immediate pleasurable outcomes and inhibitory signals mediated by a prefrontal reflective system. The resulting impairment in behavioral control represents a hallmark of alcohol use disorders (AUDs), a chronic relapsing disorder characterized by excessive alcohol consumption despite devastating consequences. This review aims to summarize available magnetic resonance imaging (MRI) evidence on reward-related decision-making alterations in AUDs, and to envision possible future research directions. We review functional MRI (fMRI) studies using tasks involving monetary rewards, as well as MRI studies relating decision-making parameters to neurostructural gray- or white-matter metrics. The available data suggest that excessive alcohol exposure affects neural signaling within brain networks underlying adaptive behavioral learning via the implementation of prediction errors. Namely, weaker ventromedial prefrontal cortex activity and altered connectivity between ventral striatum and dorsolateral prefrontal cortex likely underpin a shift from goal-directed to habitual actions which, in turn, might underpin compulsive alcohol consumption and relapsing episodes despite adverse consequences. Overall, these data highlight abnormal fronto-striatal connectivity as a candidate neurobiological marker of impaired choice in AUDs. Further studies are needed, however, to unveil its implications in the multiple facets of decision-making.

Does Psychosocial Stress Impact Cognitive Reappraisal? Behavioral and Neural Evidence.

Cognitive reappraisal (CR) is regarded as an effective emotion regulation strategy. Acute stress, however, is believed to impair the functioning of prefrontal-based neural systems, which could result in lessened effectiveness of CR under stress. This study tested the behavioral and neurobiological impact of acute stress on CR. While undergoing fMRI, adult participants ( n = 54) passively viewed or used CR to regulate their response to negative and neutral pictures and provided ratings of their negative affect in response to each picture. Half of the participants experienced an fMRI-adapted acute psychosocial stress manipulation similar to the Trier Social Stress Test, and a control group received parallel manipulations without the stressful components. Relative to the control group, the stress group exhibited heightened stress as indexed by self-report, heart rate, and salivary cortisol throughout the scan. Contrary to our hypothesis, we found that reappraisal success was equivalent in the control and stress groups, as was electrodermal response to the pictures. Heart rate deceleration, a physiological response typically evoked by aversive pictures, was blunted in response to negative pictures and heightened in response to neutral pictures in the stress group. In the brain, we found weak evidence of stress-induced increases of reappraisal-related activity in parts of the PFC and left amygdala, but these relationships were statistically fragile. Together, these findings suggest that both the self-reported and neural effects of CR may be robust to at least moderate levels of stress, informing theoretical models of stress effects on cognition and emotion.

The effect of dietary DHA and taurine on rotifer capture success, growth, survival and vision in the larvae of Atlantic bluefin tuna (Thunnus thynnus)

The severe to complete mortality that occurs during the larviculture of Atlantic bluefin tuna (ABFT; Thunnus thynnus) may be due, in part, to sub-optimal neural and eye development. The adult and larval ABFT eyes are rich in docosahexaenoic acid (DHA; 22:6n-3), which facilitates key intra-membrane reactions in the photoreceptors of the retina. Another critical nutrient is taurine, which plays vital roles that include bile salt conjugation as well as development and function of visual, neural and muscular systems. The objectives of the present study were to (1) determine the pattern of conservation and loss of fatty acid groups and their constituent fatty acids during egg and pre-larval development as well as in food deprivation. (2) Determine the effect of rotifer (Brachionus rotundiformis) DHA on hunting success, growth, and retinal opsin abundance in 2–14dph ABFT larvae. (3) Evaluate the effect of supplemented taurine in rotifers enriched on the most effective DHA level from objective (2) on larval survival and growth.During the egg and yolk sac larval stages, there was a significant decrease (P<0.05) in SAT, MONO and PUFA that can be expressed as 46.7%, 50.3% and 57.1%, respectively. Similarly, the levels of DHA, EPA, and ArA were markedly (P<0.05) reduced that can be expressed as 59.8%, 52.5% and 59.5%, respectively. In the DHA study, there was a rotifer DHA dose dependent (P<0.05) effect on prey consumption by 3–7dph ABFT larvae where the highest DHA level (11mgg−1 DW rotifer) elicited significantly (P<0.05) higher rotifer consumption compared to the control and moderate DHA diets (2mgg−1 DW and 5mgg−1 DW rotifer, respectively). Moreover, larvae with the highest DHA level (7.01mgg−1 DW) exhibited a significantly (P<0.05) higher opsin protein concentration (25.27 unit area−1) compared to the 2.83mgDHAg−1 DW and 1.26mgDHAg−1 DW fish (20.32 and 16.33 opsin protein unit area−1, respectively). Although there was a significant (P<0.05) taurine modulated increase in larval length in 10dph fish, there was a non-significant (P>0.05) growth advantage, in terms of dry weight, as a result of moderate dietary taurine supplementation at the end of the study. Nevertheless, the moderate 6.44mgtaurineg−1 DW larvae exhibited markedly (P=0.024) better survival and >4 times higher (P=0.0018) average tank biomass (273.6mg) than the low (1.97mgg−1 DW) and high (12.62mgg−1 DW) taurine fish (62.14 and 56.90mg, respectively). Overall, the data suggests that supplementing effective levels of DHA and taurine contributes to an array of physiological processes resulting in enhanced vision and prey acquisition to markedly improve ABFT larval performance during early development.

“Loser” or “Popular”?: Neural response to social status words in adolescents with major depressive disorder

Concerns about social status are ubiquitous during adolescence, with information about social status often conveyed in text formats. Depressed adolescents may show alterations in the functioning of neural systems supporting processing of social status information. We examined whether depressed youth exhibited altered neural activation to social status words in temporal and prefrontal cortical regions thought to be involved in social cognitive processing, and whether this response was associated with development. Forty-nine adolescents (ages 10–18; 35 female), including 20 with major depressive disorder and 29 controls, were scanned while identifying the valence of words that connoted positive and negative social status. Results indicated that depressed youth showed reduced late activation to social status (vs neutral) words in the superior temporal cortex (STC) and medial prefrontal cortex (MPFC); whereas healthy youth did not show any significant differences between word types. Depressed youth also showed reduced late activation in the dorsolateral prefrontal cortex and fusiform gyrus to negative (vs positive) social status words; whereas healthy youth showed the opposite pattern. Finally, age was positively associated with MPFC activation to social status words. Findings suggest that hypoactivation in the “social cognitive brain network” might be implicated in altered interpersonal functioning in adolescent depression.

Processing of earthquake catalog data of Western Turkey with artificial neural networks and adaptive neuro-fuzzy inference system

Turkey is one of several countries frequently facing significant earthquakes because of its geological and tectonic position on earth. Especially, graben systems of Western Turkey occur as a result of seismically quite active tensional tectonics. The prediction of earthquakes has been one of the most important subjects concerning scientists for a long time. Although different methods have already been developed for this task, there is currently no reliable technique for finding the exact time and location of an earthquake epicenter. Recently artificial intelligence (AI) methods have been used for earthquake studies in addition to their successful application in a broad spectrum of data intensive applications from stock market prediction to process control. In this study, earthquake data from one part of Western Turkey (37–39.30° N latitude and 26°–29.30° E longitude) were obtained from 1975 to 2009 with a magnitude greater than M ≥ 3. To test the performance of AI in time series, the monthly earthquake frequencies of Western Turkey were calculated using catalog data from the region and then the obtained data set was evaluated with two neural networks namely as the multilayer perceptron neural networks (MLPNNs) and radial basis function neural networks (RBFNNs) and adaptive neuro-fuzzy inference system (ANFIS). The results show that for monthly earthquake frequency data prediction, the proposed RBFNN provides higher correlation coefficients with real data and smaller error values.

Neurophysiology and neural engineering: a review.

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.

Neural Oscillations and a Nascent Corticohippocampal Theory of Reference.

The ability to use words to refer to the world is vital to the communicative power of human language. In particular, the anaphoric use of words to refer to previously mentioned concepts (antecedents) allows dialogue to be coherent and meaningful. Psycholinguistic theory posits that anaphor comprehension involves reactivating a memory representation of the antecedent. Whereas this implies the involvement of recognition memory or the mnemonic subroutines by which people distinguish old from new, the neural processes for reference resolution are largely unknown. Here, we report time-frequency analysis of four EEG experiments to reveal the increased coupling of functional neural systems associated with referentially coherent expressions compared with referentially problematic expressions. Despite varying in modality, language, and type of referential expression, all experiments showed larger gamma-band power for referentially coherent expressions compared with referentially problematic expressions. Beamformer analysis in high-density Experiment 4 localized the gamma-band increase to posterior parietal cortex around 400-600 msec after anaphor onset and to frontotemporal cortex around 500-1000 msec. We argue that the observed gamma-band power increases reflect successful referential binding and resolution, which links incoming information to antecedents through an interaction between the brain's recognition memory networks and frontotemporal language network. We integrate these findings with previous results from patient and neuroimaging studies, and we outline a nascent corticohippocampal theory of reference.

Analysis of miRNAs Involved in Mouse Brain Damage upon Enterovirus 71 Infection.

Enterovirus 71 (EV71) infects the central nervous system (CNS) and causes brainstem encephalitis in children. MiRNAs have been found to play various functions in EV71 infection in human cell lines. To identify potential miRNAs involved in the inflammatory injury in CNS, our study, for the first time, performed a miRNA microarray assay in vivo using EV71 infected mice brains. Twenty differentially expressed miRNAs were identified (four up- and 16 down-regulated) and confirmed by qRT-PCR. The target genes of these miRNAs were analyzed using KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, revealing that the miRNAs were mainly involved in the regulation of inflammation and neural system function. MiR-150-5p, -3082-5p, -3473a, -468-3p, -669n, -721, -709, and -5107-5p that regulate MAPK and chemokine signaling were all down-regulated, which might result in increased cytokine production. In addition, miR-3473a could also regulate focal adhesion and leukocyte trans-endothelial migration, suggesting a role in virus-induced blood-brain barrier disruption. The miRNAs and pathways identified in this study could help to understand the intricate interactions between EV71 and the brain injury, offering new insight for the future research of the molecular mechanism of EV71 induced brainstem encephalitis.

Synaptic Plasticity Selectively Activated by Polarization-Dependent Energy-Efficient Ion Migration in an Ultrathin Ferroelectric Tunnel Junction.

Selectively activated inorganic synaptic devices, showing a high on/off ratio, ultrasmall dimensions, low power consumption, and short programming time, are required to emulate the functions of high-capacity and energy-efficient reconfigurable human neural systems combining information storage and processing ( Li et al. Sci. Rep. 2014 , 4 , 4096 ). Here, we demonstrate that such a synaptic device is realized using a Ag/PbZr0.52Ti0.48O3 (PZT)/La0.8Sr0.2MnO3 (LSMO) ferroelectric tunnel junction (FTJ) with ultrathin PZT (thickness of ∼4 nm). Ag ion migration through the very thin FTJ enables a large on/off ratio (107) and low energy consumption (potentiation energy consumption = ∼22 aJ and depression energy consumption = ∼2.5 pJ). In addition, the simple alignment of the downward polarization in PZT selectively activates the synaptic plasticity of the FTJ and the transition from short-term plasticity to long-term potentiation.

Understanding the broad influence of sex hormones and sex differences in the brain.

Sex hormones act throughout the entire brain of both males and females via both genomic and nongenomic receptors. Sex hormones can act through many cellular and molecular processes that alter structure and function of neural systems and influence behavior as well as providing neuroprotection. Within neurons, sex hormone receptors are found in nuclei and are also located near membranes, where they are associated with presynaptic terminals, mitochondria, spine apparatus, and postsynaptic densities. Sex hormone receptors also are found in glial cells. Hormonal regulation of a variety of signaling pathways as well as direct and indirect effects on gene expression induce spine synapses, up- or downregulate and alter the distribution of neurotransmitter receptors, and regulate neuropeptide expression and cholinergic and GABAergic activity as well as calcium sequestration and oxidative stress. Many neural and behavioral functions are affected, including mood, cognitive function, blood pressure regulation, motor coordination, pain, and opioid sensitivity. Subtle sex differences exist for many of these functions that are developmentally programmed by hormones and by not yet precisely defined genetic factors, including the mitochondrial genome. These sex differences and responses to sex hormones in brain regions, which influence functions not previously regarded as subject to such differences, indicate that we are entering a new era of our ability to understand and appreciate the diversity of gender-related behaviors and brain functions. © 2016 Wiley Periodicals, Inc.

Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS.

In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS) to the Multi-Simulator Coordinator (MUSIC). This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

Adult Neurogenesis and Psychiatric Disorders

Psychiatric disorders continue to be among the most challenging disorders to diagnose and treat because there is no single genetic or anatomical locus that is causative for the disease. Current treatments are often blunt tools used to ameliorate the most severe symptoms, at the risk of disrupting functional neural systems. There is a critical need to develop new therapeutic strategies that can target circumscribed functional or anatomical domains of pathology. Adult hippocampal neurogenesis may be one such domain. Here, we review the evidence suggesting that adult hippocampal neurogenesis plays a role in emotional regulation and forms of learning and memory that include temporal and spatial memory encoding and context discrimination, and that its dysregulation is associated with psychiatric disorders, such as affective disorders, schizophrenia, and drug addiction. Further, adult neurogenesis has proven to be an effective model to investigate basic processes of neuronal development and converging evidence suggests that aberrant neural development may be an etiological factor, even in late-onset diseases. Constitutive neurogenesis in the hippocampus of the mature brain reflects large-scale plasticity unique to this region and could be a potential hub for modulation of a subset of cognitive and affective behaviors that are affected by multiple psychiatric disorders.

Auditory processing and morphological anomalies in medial geniculate nucleus of Cntnap2 mutant mice.

Genetic epidemiological studies support a role for CNTNAP2 in developmental language disorders such as autism spectrum disorder, specific language impairment, and dyslexia. Atypical language development and function represent a core symptom of autism spectrum disorder (ASD), with evidence suggesting that aberrant auditory processing-including impaired spectrotemporal processing and enhanced pitch perception-may both contribute to an anomalous language phenotype. Investigation of gene-brain-behavior relationships in social and repetitive ASD symptomatology have benefited from experimentation on the Cntnap2 knockout (KO) mouse. However, auditory-processing behavior and effects on neural structures within the central auditory pathway have not been assessed in this model. Thus, this study examined whether auditory-processing abnormalities were associated with mutation of the Cntnap2 gene in mice. Cntnap2 KO mice were assessed on auditory-processing tasks including silent gap detection, embedded tone detection, and pitch discrimination. Cntnap2 knockout mice showed deficits in silent gap detection but a surprising superiority in pitch-related discrimination as compared with controls. Stereological analysis revealed a reduction in the number and density of neurons, as well as a shift in neuronal size distribution toward smaller neurons, in the medial geniculate nucleus of mutant mice. These findings are consistent with a central role for CNTNAP2 in the ontogeny and function of neural systems subserving auditory processing and suggest that developmental disruption of these neural systems could contribute to the atypical language phenotype seen in autism spectrum disorder.

Neuroscience of resilience and vulnerability for addiction medicine: From genes to behavior.

Addiction is a complex behavioral disorder arising from roughly equal contributions of genetic and environmental factors. Behavioral traits such as novelty-seeking, impulsivity, and cue-reactivity have been associated with vulnerability to addiction. These traits, at least in part, arise from individual variation in functional neural systems, such as increased striatal dopaminergic activity and decreased prefrontal cortical control over subcortical emotional and motivational responses. With a few exceptions, genetic studies have largely failed to consistently identify specific alleles that affect addiction liability. This may be due to the multifactorial nature of addiction, with different genes becoming more significant in certain environments or in certain subsets of the population. Epigenetic mechanisms may also be an important source of risk. Adolescence is a particularly critical time period in the development of addiction, and environmental factors at this stage of life can have a large influence on whether inherited risk factors are actually translated into addictive behaviors. Knowledge of how individual differences affect addiction liability at the level of genes, neural systems, behavioral traits, and sociodevelopmental trajectories can help to inform and improve clinical practice.