Numerosity as a topological invariant.

PubMed

Kluth, Tobias; Zetzsche, Christoph

2016-01-01

The ability to quickly recognize the number of objects in our environment is a fundamental cognitive function. However, it is far from clear which computations and which actual neural processing mechanisms are used to provide us with such a skill. Here we try to provide a detailed and comprehensive analysis of this issue, which comprises both the basic mathematical foundations and the peculiarities imposed by the structure of the visual system and by the neural computations provided by the visual cortex. We suggest that numerosity should be considered as a mathematical invariant. Making use of concepts from mathematical topology--like connectedness, Betti numbers, and the Gauss-Bonnet theorem--we derive the basic computations suited for the computation of this invariant. We show that the computation of numerosity is possible in a neurophysiologically plausible fashion using only computational elements which are known to exist in the visual cortex. We further show that a fundamental feature of numerosity perception, its Weber property, arises naturally, assuming noise in the basic neural operations. The model is tested on an extended data set (made publicly available). It is hoped that our results can provide a general framework for future research on the invariance properties of the numerosity system.

‘Why should I care?’ Challenging free will attenuates neural reaction to errors

PubMed Central

Pourtois, Gilles; Brass, Marcel

2015-01-01

Whether human beings have free will has been a philosophical question for centuries. The debate about free will has recently entered the public arena through mass media and newspaper articles commenting on scientific findings that leave little to no room for free will. Previous research has shown that encouraging such a deterministic perspective influences behavior, namely by promoting cursory and antisocial behavior. Here we propose that such behavioral changes may, at least partly, stem from a more basic neurocognitive process related to response monitoring, namely a reduced error detection mechanism. Our results show that the error-related negativity, a neural marker of error detection, was reduced in individuals led to disbelieve in free will. This finding shows that reducing the belief in free will has a specific impact on error detection mechanisms. More generally, it suggests that abstract beliefs about intentional control can influence basic and automatic processes related to action control. PMID:24795441

Electronic device aspects of neural network memories

NASA Technical Reports Server (NTRS)

Lambe, J.; Moopenn, A.; Thakoor, A. P.

1985-01-01

The basic issues related to the electronic implementation of the neural network model (NNM) for content addressable memories are examined. A brief introduction to the principles of the NNM is followed by an analysis of the information storage of the neural network in the form of a binary connection matrix and the recall capability of such matrix memories based on a hardware simulation study. In addition, materials and device architecture issues involved in the future realization of such networks in VLSI-compatible ultrahigh-density memories are considered. A possible space application of such devices would be in the area of large-scale information storage without mechanical devices.

Neuromusculoskeletal models based on the muscle synergy hypothesis for the investigation of adaptive motor control in locomotion via sensory-motor coordination.

PubMed

Aoi, Shinya; Funato, Tetsuro

2016-03-01

Humans and animals walk adaptively in diverse situations by skillfully manipulating their complicated and redundant musculoskeletal systems. From an analysis of measured electromyographic (EMG) data, it appears that despite complicated spatiotemporal properties, muscle activation patterns can be explained by a low dimensional spatiotemporal structure. More specifically, they can be accounted for by the combination of a small number of basic activation patterns. The basic patterns and distribution weights indicate temporal and spatial structures, respectively, and the weights show the muscle sets that are activated synchronously. In addition, various locomotor behaviors have similar low dimensional structures and major differences appear in the basic patterns. These analysis results suggest that neural systems use muscle group combinations to solve motor control redundancy problems (muscle synergy hypothesis) and manipulate those basic patterns to create various locomotor functions. However, it remains unclear how the neural system controls such muscle groups and basic patterns through neuromechanical interactions in order to achieve adaptive locomotor behavior. This paper reviews simulation studies that explored adaptive motor control in locomotion via sensory-motor coordination using neuromusculoskeletal models based on the muscle synergy hypothesis. Herein, the neural mechanism in motor control related to the muscle synergy for adaptive locomotion and a potential muscle synergy analysis method including neuromusculoskeletal modeling for motor impairments and rehabilitation are discussed. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Applying Gradient Descent in Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

Transduction of NeuroD2 protein induced neural cell differentiation.

PubMed

Noda, Tomohide; Kawamura, Ryuzo; Funabashi, Hisakage; Mie, Masayasu; Kobatake, Eiry

2006-11-01

NeuroD2, one of the neurospecific basic helix-loop-helix transcription factors, has the ability to induce neural differentiation in undifferentiated cells. In this paper, we show that transduction of NeuroD2 protein induced mouse neuroblastoma cell line N1E-115 into neural differentiation. NeuroD2 has two basic-rich domains, one is nuclear localization signal (NLS) and the other is basic region of basic helix-loop-helix (basic). We constructed some mutants of NeuroD2, ND2(Delta100-115) (lack of NLS), ND2(Delta123-134) (lack of basic) and ND2(Delta100-134) (lack of both NLS and basic) for transduction experiments. Using these proteins, we have shown that NLS region of NeuroD2 plays a role of protein transduction. Continuous addition of NeuroD2 protein resulted in N1E-115 cells adopting neural morphology after 4 days and Tau mRNA expression was increased. These results suggest that neural differentiation can be induced by direct addition of NeuroD2 protein.

Social anhedonia is associated with neural abnormalities during face emotion processing.

PubMed

Germine, Laura T; Garrido, Lucia; Bruce, Lori; Hooker, Christine

2011-10-01

Human beings are social organisms with an intrinsic desire to seek and participate in social interactions. Social anhedonia is a personality trait characterized by a reduced desire for social affiliation and reduced pleasure derived from interpersonal interactions. Abnormally high levels of social anhedonia prospectively predict the development of schizophrenia and contribute to poorer outcomes for schizophrenia patients. Despite the strong association between social anhedonia and schizophrenia, the neural mechanisms that underlie individual differences in social anhedonia have not been studied and are thus poorly understood. Deficits in face emotion recognition are related to poorer social outcomes in schizophrenia, and it has been suggested that face emotion recognition deficits may be a behavioral marker for schizophrenia liability. In the current study, we used functional magnetic resonance imaging (fMRI) to see whether there are differences in the brain networks underlying basic face emotion processing in a community sample of individuals low vs. high in social anhedonia. We isolated the neural mechanisms related to face emotion processing by comparing face emotion discrimination with four other baseline conditions (identity discrimination of emotional faces, identity discrimination of neutral faces, object discrimination, and pattern discrimination). Results showed a group (high/low social anhedonia) × condition (emotion discrimination/control condition) interaction in the anterior portion of the rostral medial prefrontal cortex, right superior temporal gyrus, and left somatosensory cortex. As predicted, high (relative to low) social anhedonia participants showed less neural activity in face emotion processing regions during emotion discrimination as compared to each control condition. The findings suggest that social anhedonia is associated with abnormalities in networks responsible for basic processes associated with social cognition, and provide a starting point for understanding the neural basis of social motivation and our drive to seek social affiliation. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

PubMed Central

Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

2013-01-01

Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

The function and failure of sensory predictions.

PubMed

Bansal, Sonia; Ford, Judith M; Spering, Miriam

2018-04-23

Humans and other primates are equipped with neural mechanisms that allow them to automatically make predictions about future events, facilitating processing of expected sensations and actions. Prediction-driven control and monitoring of perceptual and motor acts are vital to normal cognitive functioning. This review provides an overview of corollary discharge mechanisms involved in predictions across sensory modalities and discusses consequences of predictive coding for cognition and behavior. Converging evidence now links impairments in corollary discharge mechanisms to neuropsychiatric symptoms such as hallucinations and delusions. We review studies supporting a prediction-failure hypothesis of perceptual and cognitive disturbances. We also outline neural correlates underlying prediction function and failure, highlighting similarities across the visual, auditory, and somatosensory systems. In linking basic psychophysical and psychophysiological evidence of visual, auditory, and somatosensory prediction failures to neuropsychiatric symptoms, our review furthers our understanding of disease mechanisms. © 2018 New York Academy of Sciences.

Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions.

PubMed

Diano, Matteo; Tamietto, Marco; Celeghin, Alessia; Weiskrantz, Lawrence; Tatu, Mona-Karina; Bagnis, Arianna; Duca, Sergio; Geminiani, Giuliano; Cauda, Franco; Costa, Tommaso

2017-03-27

The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala's psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure-function relation between brain-emotion from the traditional one-to-one mapping toward a network-based and dynamic perspective.

Biophoton signal transmission and processing in the brain.

PubMed

Tang, Rendong; Dai, Jiapei

2014-10-05

The transmission and processing of neural information in the nervous system plays a key role in neural functions. It is well accepted that neural communication is mediated by bioelectricity and chemical molecules via the processes called bioelectrical and chemical transmission, respectively. Indeed, the traditional theories seem to give valuable explanations for the basic functions of the nervous system, but difficult to construct general accepted concepts or principles to provide reasonable explanations of higher brain functions and mental activities, such as perception, learning and memory, emotion and consciousness. Therefore, many unanswered questions and debates over the neural encoding and mechanisms of neuronal networks remain. Cell to cell communication by biophotons, also called ultra-weak photon emissions, has been demonstrated in several plants, bacteria and certain animal cells. Recently, both experimental evidence and theoretical speculation have suggested that biophotons may play a potential role in neural signal transmission and processing, contributing to the understanding of the high functions of nervous system. In this paper, we review the relevant experimental findings and discuss the possible underlying mechanisms of biophoton signal transmission and processing in the nervous system. Copyright © 2014 Elsevier B.V. All rights reserved.

Learning in a Simple Motor System

ERIC Educational Resources Information Center

Broussard, Dianne M.; Kassardjian, Charles D.

2004-01-01

Motor learning is a very basic, essential form of learning that appears to share common mechanisms across different motor systems. We evaluate and compare a few conceptual models for learning in a relatively simple neural system, the vestibulo-ocular reflex (VOR) of vertebrates. We also compare the different animal models that have been used to…

Neural mechanisms of rhythm perception: current findings and future perspectives.

PubMed

Grahn, Jessica A

2012-10-01

Perception of temporal patterns is fundamental to normal hearing, speech, motor control, and music. Certain types of pattern understanding are unique to humans, such as musical rhythm. Although human responses to musical rhythm are universal, there is much we do not understand about how rhythm is processed in the brain. Here, I consider findings from research into basic timing mechanisms and models through to the neuroscience of rhythm and meter. A network of neural areas, including motor regions, is regularly implicated in basic timing as well as processing of musical rhythm. However, fractionating the specific roles of individual areas in this network has remained a challenge. Distinctions in activity patterns appear between "automatic" and "cognitively controlled" timing processes, but the perception of musical rhythm requires features of both automatic and controlled processes. In addition, many experimental manipulations rely on participants directing their attention toward or away from certain stimulus features, and measuring corresponding differences in neural activity. Many temporal features, however, are implicitly processed whether attended to or not, making it difficult to create controlled baseline conditions for experimental comparisons. The variety of stimuli, paradigms, and definitions can further complicate comparisons across domains or methodologies. Despite these challenges, the high level of interest and multitude of methodological approaches from different cognitive domains (including music, language, and motor learning) have yielded new insights and hold promise for future progress. Copyright © 2012 Cognitive Science Society, Inc.

Excitatory, inhibitory and facilitatory frequency response areas in the inferior colliculus of hearing impaired mice.

PubMed

Felix, Richard A; Portfors, Christine V

2007-06-01

Individuals with age-related hearing loss often have difficulty understanding complex sounds such as basic speech. The C57BL/6 mouse suffers from progressive sensorineural hearing loss and thus is an effective tool for dissecting the neural mechanisms underlying changes in complex sound processing observed in humans. Neural mechanisms important for processing complex sounds include multiple tuning and combination sensitivity, and these responses are common in the inferior colliculus (IC) of normal hearing mice. We examined neural responses in the IC of C57Bl/6 mice to single and combinations of tones to examine the extent of spectral integration in the IC after age-related high frequency hearing loss. Ten percent of the neurons were tuned to multiple frequency bands and an additional 10% displayed non-linear facilitation to the combination of two different tones (combination sensitivity). No combination-sensitive inhibition was observed. By comparing these findings to spectral integration properties in the IC of normal hearing CBA/CaJ mice, we suggest that high frequency hearing loss affects some of the neural mechanisms in the IC that underlie the processing of complex sounds. The loss of spectral integration properties in the IC during aging likely impairs the central auditory system's ability to process complex sounds such as speech.

Observe, simplify, titrate, model, and synthesize: A paradigm for analyzing behavior

PubMed Central

Alberts, Jeffrey R.

2013-01-01

Phenomena in behavior and their underlying neural mechanisms are exquisitely complex problems. Infrequently do we reflect on our basic strategies of investigation and analysis, or formally confront the actual challenges of achieving an understanding of the phenomena that inspire research. Philip Teitelbaum is distinct in his elegant approaches to understanding behavioral phenomena and their associated neural processes. He also articulated his views on effective approaches to scientific analyses of brain and behavior, his vision of how behavior and the nervous system are patterned, and what constitutes basic understanding. His rubrics involve careful observation and description of behavior, simplification of the complexity, analysis of elements, and re-integration through different forms of synthesis. Research on the development of huddling behavior by individual and groups of rats is reviewed in a context of Teitelbaum’s rubrics of research, with the goal of appreciating his broad and positive influence on the scientific community. PMID:22481081

Targeting Vascular Neural Network in Intracerebral Hemorrhage.

PubMed

Yin, Yi; Ge, Hongfei; Zhang, John H; Feng, Hua

2017-01-01

Intracerebral hemorrhage (ICH) is a common type of stroke associated with high mortality and morbidity. Recent randomized controlled trials could not prove that the current strategies are effective at improving the final outcome of the ICH patients. Here we want to explore potential intervention targets for ICH based on the framework of the vascular neural network (VNN). In this review, a brief history of the evolution of stroke pathophysiology from humoral theory to VNN is discussed. As current literature on pathophysiology of ICH is mainly focused on neuroprotection, here we want to evolve the central paradigm towards VNN. We stress mechanisms of vascular disruption and impaired blood flow harmony, which are clinically relevant but have received less attention in basic research. We propose that VNN could be a robust and practical paradigm in both ICH basic research and clinical practice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Repression by PRDM13 is critical for generating precision in neuronal identity

PubMed Central

Kollipara, Rahul K; Ma, Zhenzhong; Borromeo, Mark D; Chang, Joshua C

2017-01-01

The mechanisms that activate some genes while silencing others are critical to ensure precision in lineage specification as multipotent progenitors become restricted in cell fate. During neurodevelopment, these mechanisms are required to generate the diversity of neuronal subtypes found in the nervous system. Here we report interactions between basic helix-loop-helix (bHLH) transcriptional activators and the transcriptional repressor PRDM13 that are critical for specifying dorsal spinal cord neurons. PRDM13 inhibits gene expression programs for excitatory neuronal lineages in the dorsal neural tube. Strikingly, PRDM13 also ensures a battery of ventral neural tube specification genes such as Olig1, Olig2 and Prdm12 are excluded dorsally. PRDM13 does this via recruitment to chromatin by multiple neural bHLH factors to restrict gene expression in specific neuronal lineages. Together these findings highlight the function of PRDM13 in repressing the activity of bHLH transcriptional activators that together are required to achieve precise neuronal specification during mouse development. PMID:28850031

Role of Network Science in the Study of Anesthetic State Transitions.

PubMed

Lee, UnCheol; Mashour, George A

2018-04-23

The heterogeneity of molecular mechanisms, target neural circuits, and neurophysiologic effects of general anesthetics makes it difficult to develop a reliable and drug-invariant index of general anesthesia. No single brain region or mechanism has been identified as the neural correlate of consciousness, suggesting that consciousness might emerge through complex interactions of spatially and temporally distributed brain functions. The goal of this review article is to introduce the basic concepts of networks and explain why the application of network science to general anesthesia could be a pathway to discover a fundamental mechanism of anesthetic-induced unconsciousness. This article reviews data suggesting that reduced network efficiency, constrained network repertoires, and changes in cortical dynamics create inhospitable conditions for information processing and transfer, which lead to unconsciousness. This review proposes that network science is not just a useful tool but a necessary theoretical framework and method to uncover common principles of anesthetic-induced unconsciousness.

Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependentmore » and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy.« less

Reorganization of the Connectivity between Elementary Functions – A Model Relating Conscious States to Neural Connections

PubMed Central

Mogensen, Jesper; Overgaard, Morten

2017-01-01

In the present paper it is argued that the “neural correlate of consciousness” (NCC) does not appear to be a separate “module” – but an aspect of information processing within the neural substrate of various cognitive processes. Consequently, NCC can only be addressed adequately within frameworks that model the general relationship between neural processes and mental states – and take into account the dynamic connectivity of the brain. We presently offer the REFGEN (general reorganization of elementary functions) model as such a framework. This model builds upon and expands the REF (reorganization of elementary functions) and REFCON (of elementary functions and consciousness) models. All three models integrate the relationship between the neural and mental layers of description via the construction of an intermediate level dealing with computational states. The importance of experience based organization of neural and cognitive processes is stressed. The models assume that the mechanisms of consciousness are in principle the same as the basic mechanisms of all aspects of cognition – when information is processed to a sufficiently “high level” it becomes available to conscious experience. The NCC is within the REFGEN model seen as aspects of the dynamic and experience driven reorganizations of the synaptic connectivity between the neurocognitive “building blocks” of the model – the elementary functions. PMID:28473797

Dissociation of neural mechanisms underlying orientation processing in humans

PubMed Central

Ling, Sam; Pearson, Joel; Blake, Randolph

2009-01-01

Summary Orientation selectivity is a fundamental, emergent property of neurons in early visual cortex, and discovery of that property [1, 2] dramatically shaped how we conceptualize visual processing [3–6]. However, much remains unknown about the neural substrates of these basic building blocks of perception, and what is known primarily stems from animal physiology studies. To probe the neural concomitants of orientation processing in humans, we employed repetitive transcranial magnetic stimulation (rTMS) to attenuate neural responses evoked by stimuli presented within a local region of the visual field. Previous physiological studies have shown that rTMS can significantly suppress the neuronal spiking activity, hemodynamic responses, and local field potentials within a focused cortical region [7, 8]. By suppressing neural activity with rTMS, we were able to dissociate components of the neural circuitry underlying two distinct aspects of orientation processing: selectivity and contextual effects. Orientation selectivity gauged by masking was unchanged by rTMS, whereas an otherwise robust orientation repulsion illusion was weakened following rTMS. This dissociation implies that orientation processing relies on distinct mechanisms, only one of which was impacted by rTMS. These results are consistent with models positing that orientation selectivity is largely governed by the patterns of convergence of thalamic afferents onto cortical neurons, with intracortical activity then shaping population responses contained within those orientation-selective cortical neurons. PMID:19682905

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

NASA Astrophysics Data System (ADS)

Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

2016-10-01

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

Neural responses to exclusion predict susceptibility to social influence.

PubMed

Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

2014-05-01

Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

Neural responses to exclusion predict susceptibility to social influence

PubMed Central

Falk, Emily B.; Cascio, Christopher N.; O’Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J.; Bingham, C. Raymond; Shope, Jean T.; Ouimet, Marie Claude; Pradhan, Anuj K.; Simons-Morton, Bruce G.

2014-01-01

Purpose Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence, and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American teens, traffic-related crashes are leading causes of non-fatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents’ vulnerability to peer influence. Methods We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently-licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately one week after the neuroimaging session. Results Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside of the neuroimaging lab one week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. Conclusions These results speak to the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging lab. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. PMID:24759437

Localization of basic fibroblast growth factor binding sites in the chick embryonic neural retina.

PubMed

Cirillo, A; Arruti, C; Courtois, Y; Jeanny, J C

1990-12-01

We have investigated the localization of basic fibroblast growth factor (bFGF) binding sites during the development of the neural retina in the chick embryo. The specificity of the affinity of bFGF for its receptors was assessed by competition experiments with unlabelled growth factor or with heparin, as well as by heparitinase treatment of the samples. Two different types of binding sites were observed in the neural retina by light-microscopic autoradiography. The first type, localized mainly to basement membranes, was highly sensitive to heparitinase digestion and to competition with heparin. It was not developmentally regulated. The second type of binding site, resistant to heparin competition, appeared to be associated with retinal cells from the earliest stages studied (3-day-old embryo, stages 21-22 of Hamburger and Hamilton). Its distribution was found to vary during embryonic development, paralleling layering of the neural retina. Binding of bFGF to the latter sites was observed throughout the retinal neuroepithelium at early stages but displayed a distinct pattern at the time when the inner and outer plexiform layers were formed. During the development of the inner plexiform layer, a banded pattern of bFGF binding was observed. These bands, lying parallel to the vitreal surface, seemed to codistribute with the synaptic bands existing in the inner plexiform layer. The presence of intra-retinal bFGF binding sites whose distribution varies with embryonic development suggests a regulatory mechanism involving differential actions of bFGF on neural retinal cells.

Multi-Agent Market Modeling of Foreign Exchange Rates

NASA Astrophysics Data System (ADS)

Zimmermann, Georg; Neuneier, Ralph; Grothmann, Ralph

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The oeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach. The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market.

Action mechanisms for social cognition: behavioral and neural correlates of developing Theory of Mind

PubMed Central

Bowman, Lindsay C.; Thorpe, Samuel G.; Cannon, Erin N.; Fox, Nathan A.

2016-01-01

Many psychological theories posit foundational links between two fundamental constructs: (1) our ability to produce, perceive, and represent action; and (2) our ability to understand the meaning and motivation behind the action (i.e. Theory of Mind; ToM). This position is contentious, however, and long-standing competing theories of social-cognitive development debate roles for basic action-processing in ToM. Developmental research is key to investigating these hypotheses, but whether individual differences in neural and behavioral measures of motor action relate to social-cognitive development is unknown. We examined 3- to 5-year-old children’s (N = 26) EEG mu-desynchronization during production of object-directed action, and explored associations between mu-desynchronization and children’s behavioral motor skills, behavioral action-representation abilities, and behavioral ToM. For children with high (but not low) mu-desynchronization, motor skill related to action-representation abilities, and action-representation mediated relations between motor skill and ToM. Results demonstrate novel foundational links between action-processing and ToM, suggesting that basic motor action may be a key mechanism for social-cognitive development, thus shedding light on the origins and emergence of higher social cognition. PMID:27573916

An Integrative Model for the Neural Mechanism of Eye Movement Desensitization and Reprocessing (EMDR).

PubMed

Coubard, Olivier A

2016-01-01

Since the seminal report by Shapiro that bilateral stimulation induces cognitive and emotional changes, 26 years of basic and clinical research have examined the effects of Eye Movement Desensitization and Reprocessing (EMDR) in anxiety disorders, particularly in post-traumatic stress disorder (PTSD). The present article aims at better understanding EMDR neural mechanism. I first review procedural aspects of EMDR protocol and theoretical hypothesis about EMDR effects, and develop the reasons why the scientific community is still divided about EMDR. I then slide from psychology to physiology describing eye movements/emotion interaction from the physiological viewpoint, and introduce theoretical and technical tools used in movement research to re-examine EMDR neural mechanism. Using a recent physiological model for the neuropsychological architecture of motor and cognitive control, the Threshold Interval Modulation with Early Release-Rate of rIse Deviation with Early Release (TIMER-RIDER)-model, I explore how attentional control and bilateral stimulation may participate to EMDR effects. These effects may be obtained by two processes acting in parallel: (i) activity level enhancement of attentional control component; and (ii) bilateral stimulation in any sensorimotor modality, both resulting in lower inhibition enabling dysfunctional information to be processed and anxiety to be reduced. The TIMER-RIDER model offers quantitative predictions about EMDR effects for future research about its underlying physiological mechanisms.

Neural mechanisms of mother-infant bonding and pair bonding: Similarities, differences, and broader implications.

PubMed

Numan, Michael; Young, Larry J

2016-01-01

This article is part of a Special Issue "Parental Care". Mother-infant bonding is a characteristic of virtually all mammals. The maternal neural system may have provided the scaffold upon which other types of social bonds in mammals have been built. For example, most mammals exhibit a polygamous mating system, but monogamy and pair bonding between mating partners occur in ~5% of mammalian species. In mammals, it is plausible that the neural mechanisms that promote mother-infant bonding have been modified by natural selection to establish the capacity to develop a selective bond with a mate during the evolution of monogamous mating strategies. Here we compare the details of the neural mechanisms that promote mother-infant bonding in rats and other mammals with those that underpin pair bond formation in the monogamous prairie vole. Although details remain to be resolved, remarkable similarities and a few differences between the mechanisms underlying these two types of bond formation are revealed. For example, amygdala and nucleus accumbens-ventral pallidum (NA-VP) circuits are involved in both types of bond formation, and dopamine and oxytocin actions within NA appear to promote the synaptic plasticity that allows either infant or mating partner stimuli to persistently activate NA-VP attraction circuits, leading to an enduring social attraction and bonding. Further, although the medial preoptic area is essential for maternal behavior, its role in pair bonding remains to be determined. Our review concludes by examining the broader implications of this comparative analysis, and evidence is provided that the maternal care system may have also provided the basic neural foundation for other types of strong social relationships, beyond pair bonding, in mammals, including humans. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural mechanisms of mother-infant bonding and pair bonding: Similarities, differences, and broader implications

PubMed Central

Numan, Michael; Young, Larry J.

2015-01-01

Mother-infant bonding is a characteristic of virtually all mammals. The maternal neural system may have provided the scaffold upon which other types of social bonds in mammals have been built. For example, most mammals exhibit a polygamous mating system, but monogamy and pair bonding between mating partners occurs in ∼5% of mammalian species. In mammals, it is plausible that the neural mechanisms that promote mother-infant bonding have been modified by natural selection to establish the capacity to develop a selective bond with a mate during the evolution of monogamous mating strategies. Here we compare the details of the neural mechanisms that promote mother-infant bonding in rats and other mammals with those that underpin pair bond formation in the monogamous prairie vole. Although details remain to be resolved, remarkable similarities and a few differences between the mechanisms underlying these two types of bond formation are revealed. For example, amygdala and nucleus accumbens-ventral pallidum (NA-VP) circuits are involved in both types of bond formation, and dopamine and oxytocin action within NA appears to promote the synaptic plasticity that allows either infant or mating partner stimuli to persistently activate NA-VP attraction circuits, leading to an enduring social attraction and bonding. Further, although the medial preoptic area is essential for maternal behavior, its role in pair bonding remains to be determined. Our review concludes by examining the broader implications of this comparative analysis, and evidence is provided that the maternal care system may have also provided the basic neural foundation for other types of strong social relationships, beyond pair bonding, in mammals, including humans. PMID:26062432

Neuroplasticity of selective attention: Research foundations and preliminary evidence for a gene by intervention interaction

PubMed Central

Stevens, Courtney; Pakulak, Eric; Hampton Wray, Amanda; Bell, Theodore A.; Neville, Helen J.

2017-01-01

This article reviews the trajectory of our research program on selective attention, which has moved from basic research on the neural processes underlying selective attention to translational studies using selective attention as a neurobiological target for evidence-based interventions. We use this background to present a promising preliminary investigation of how genetic and experiential factors interact during development (i.e., gene × intervention interactions). Our findings provide evidence on how exposure to a family-based training can modify the associations between genotype (5-HTTLPR) and the neural mechanisms of selective attention in preschool children from lower socioeconomic status backgrounds. PMID:28819066

Neuroplasticity of selective attention: Research foundations and preliminary evidence for a gene by intervention interaction.

PubMed

Isbell, Elif; Stevens, Courtney; Pakulak, Eric; Hampton Wray, Amanda; Bell, Theodore A; Neville, Helen J

2017-08-29

This article reviews the trajectory of our research program on selective attention, which has moved from basic research on the neural processes underlying selective attention to translational studies using selective attention as a neurobiological target for evidence-based interventions. We use this background to present a promising preliminary investigation of how genetic and experiential factors interact during development (i.e., gene × intervention interactions). Our findings provide evidence on how exposure to a family-based training can modify the associations between genotype (5-HTTLPR) and the neural mechanisms of selective attention in preschool children from lower socioeconomic status backgrounds.

Benefits of detailed models of muscle activation and mechanics

NASA Technical Reports Server (NTRS)

Lehman, S. L.; Stark, L.

1981-01-01

Recent biophysical and physiological studies identified some of the detailed mechanisms involved in excitation-contraction coupling, muscle contraction, and deactivation. Mathematical models incorporating these mechanisms allow independent estimates of key parameters, direct interplay between basic muscle research and the study of motor control, and realistic model behaviors, some of which are not accessible to previous, simpler, models. The existence of previously unmodeled behaviors has important implications for strategies of motor control and identification of neural signals. New developments in the analysis of differential equations make the more detailed models feasible for simulation in realistic experimental situations.

The super-Turing computational power of plastic recurrent neural networks.

PubMed

Cabessa, Jérémie; Siegelmann, Hava T

2014-12-01

We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

PubMed Central

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots. PMID:26441629

Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

2018-05-01

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

The Impact of Sleep Deprivation on the Brain

PubMed

Trošt Bobić, Tatjana; Šečić, Ana; Zavoreo, Iris; Matijević, Valentina; Filipović, Branimir; Kolak, Željka; Bašić Kes, Vanja; Ciliga, Dubravka; Sajković, Dubravka

2016-09-01

Each sleep phase is characterized by specific chemical, cellular and anatomic events of vital importance for normal neural functioning. Different forms of sleep deprivation may lead to a decline of cognitive functions in individuals. Studies in this field make a distinction between total sleep deprivation, chronic sleep restriction, and the situation of sleep disruption. Investigations covering the acute effects of sleep deprivation on the brain show that the discovered behavioral deficits in most cases regenerate after two nights of complete sleep. However, some studies done on mice emphasize the possible chronic effects of long-term sleep deprivation or chronic restriction on the occurrence of neurodegenerative diseases such as Alzheimer’s disease and dementia. In order to better understand the acute and chronic effects of sleep loss, the mechanisms of neural adaptation in the situations of insufficient sleep need to be further investigated. Future integrative research on the impact of sleep deprivation on neural functioning measured through the macro level of cognitive functions and the micro molecular and cell level could contribute to more accurate conclusions about the basic cellular mechanisms responsible for the detected behavioral deficits occurring due to sleep deprivation.

Basic helix-loop-helix transcription factors in evolution: Roles in development of mesoderm and neural tissues.

PubMed

Gyoja, Fuki

2017-09-01

Basic helix-loop-helix (bHLH) transcription factors have attracted the attention of developmental and evolutionary biologists for decades because of their conserved functions in mesodermal and neural tissue formation in both vertebrates and fruit flies. Their evolutionary history is of special interest because it will likely provide insights into developmental processes and refinement of metazoan-specific traits. This review briefly considers advances in developmental biological studies on bHLHs/HLHs. I also discuss recent genome-wide surveys and molecular phylogenetic analyses of these factors in a wide range of metazoans. I hypothesize that interactions between metazoan-specific Group A, D, and E bHLH/HLH factors enabled a sophisticated transition system from cell proliferation to differentiation in multicellular development. This control mechanism probably emerged initially to organize a multicellular animal body and was subsequently recruited to form evolutionarily novel tissues, which differentiated during a later ontogenetic phase. © 2017 Wiley Periodicals, Inc.

From the basics to application of cell therapy, a steppingstone to the conquest of neurodegeneration: a meeting report.

PubMed

Park, Dong-Hyuk; Eve, David J; Borlongan, Cesario V; Klasko, Stephen K; Cruz, L Eduardo; Sanberg, Paul R

2009-02-01

The annual meeting of the American Society for Neural Therapy and Repair (ASNTR) showcases the latest research trends in neurodegenerative disease and the related medical regenerative science. The 2008 ASNTR meeting covered a variety of different topics ranging from basic research to exploration of currently unknown pathogenesis and mechanisms for specific neurodegenerative disease such as Parkinson's disease, Alzheimer's disease, or stroke. This included studies to characterize stem cells, such as neural stem cells, embryonic stem cells, bone marrow mesenchymal stem cells, and human umbilical cord blood cells, for transplantation and the conditions necessary to maximize the efficacy of endogenous and exogenous stem cells, such as isolation, purification, differentiation, and migration. Moreover, a number of studies looked at methods for more advanced application of transplantation of cells or specific factors, through tissue engineering or manipulation beyond simple injection. Finally, well-known or previously un-known dietary supplementation or pharmacological materials that can affect the nervous system positively or negatively, were also important topics.

3D visual mechinism by neural networkings

NASA Astrophysics Data System (ADS)

Sugiyama, Shigeki

2007-04-01

There are some computer vision systems that are available on a market but those are quite far from a real usage of our daily life in a sense of security guard or in a sense of a usage of recognition of a target object behaviour. Because those surroundings' sensing might need to recognize a detail description of an object, like "the distance to an object" and "an object detail figure" and "its figure of edging", which are not possible to have a clear picture of the mechanisms of them with the present recognition system. So for doing this, here studies on mechanisms of how a pair of human eyes can recognize a distance apart, an object edging, and an object in order to get basic essences of vision mechanisms. And those basic mechanisms of object recognition are simplified and are extended logically for applying to a computer vision system. Some of the results of these studies are introduced on this paper.

Female contact modulates male aggression via a sexually dimorphic GABAergic circuit in Drosophila

PubMed Central

Yuan, Quan; Song, Yuanquan; Yang, Chung-Hui; Jan, Lily Yeh; Jan, Yuh Nung

2014-01-01

Intraspecific male-male aggression, important for sexual selection, is regulated by environment, experience and internal states through largely undefined molecular and cellular mechanisms. To understand the basic neural pathway underlying the modulation of this innate behavior, we established a behavioral paradigm in Drosophila melanogaster and investigated the relationship between sexual experience and aggression. In the presence of mating partners, adult male flies exhibited elevated levels of aggression, which was largely suppressed by prior exposure to females via a sexually dimorphic neural mechanism. The suppression involved the ability of male flies to detect females by contact chemosensation through the pheromone-sensing ion channel, ppk29, and was mediated by male specific GABAergic neurons acting upon GABA-a receptor RDL in target cells. Silencing or activation of this circuit led to dis-inhibition or elimination of sex-related aggression, respectively. We propose that the GABAergic inhibition represents a critical cellular mechanism that enables prior experience to modulate aggression. PMID:24241395

Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

PubMed Central

Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

2015-01-01

Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

Neuromechanic: a computational platform for simulation and analysis of the neural control of movement.

PubMed

Bunderson, Nathan E; Bingham, Jeffrey T; Sohn, M Hongchul; Ting, Lena H; Burkholder, Thomas J

2012-10-01

Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states and muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization, and stability analysis tools to provide structural insights into the neural control of movement. Copyright © 2012 John Wiley & Sons, Ltd.

Processing of Communication Sounds: Contributions of Learning, Memory, and Experience

PubMed Central

Bigelow, James; Rossi, Breein

2013-01-01

Abundant evidence from both field and lab studies has established that conspecific vocalizations (CVs) are of critical ecological significance for a wide variety of species, including humans, nonhuman primates, rodents, and other mammals and birds. Correspondingly, a number of experiments have demonstrated behavioral processing advantages for CVs, such as in discrimination and memory tasks. Further, a wide range of experiments have described brain regions in many species that appear to be specialized for processing CVs. For example, several neural regions have been described in both mammals and birds wherein greater neural responses are elicited by CVs than by comparison stimuli such as heterospecific vocalizations, nonvocal complex sounds, and artificial stimuli. These observations raise the question of whether these regions reflect domain-specific neural mechanisms dedicated to processing CVs, or alternatively, if these regions reflect domain-general neural mechanisms for representing complex sounds of learned significance. Inasmuch as CVs can be viewed as complex combinations of basic spectrotemporal features, the plausibility of the latter position is supported by a large body of literature describing modulated cortical and subcortical representation of a variety of acoustic features that have been experimentally associated with stimuli of natural behavioral significance (such as food rewards). Herein, we review a relatively small body of existing literature describing the roles of experience, learning, and memory in the emergence of species-typical neural representations of CVs and auditory system plasticity. In both songbirds and mammals, manipulations of auditory experience as well as specific learning paradigms are shown to modulate neural responses evoked by CVs, either in terms of overall firing rate or temporal firing patterns. In some cases, CV-sensitive neural regions gradually acquire representation of non-CV stimuli with which subjects have training and experience. These results parallel literature in humans describing modulation of responses in face-sensitive neural regions through learning and experience. Thus, although many questions remain, the available evidence is consistent with the notion that CVs may acquire distinct neural representation through domain-general mechanisms for representing complex auditory objects that are of learned importance to the animal. PMID:23792078

The neurophysiology of sexual arousal.

PubMed

Schober, Justine M; Pfaff, Donald

2007-09-01

Our understanding of the process and initiation of sexual arousal is being enhanced by both animal and human studies, inclusive of basic science principles and research on clinical outcomes. Sexual arousal is dependent on neural (sensory and cognitive) factors, hormonal factors, genetic factors and, in the human case, the complex influences of culture and context. Sexual arousal activates the cognitive and physiologic processes that can eventually lead to sexual behavior. Sexual arousal comprises a particular subset of central nervous system arousal functions which depend on primitive, fundamental arousal mechanisms that cause generalized brain activity, but are manifest in a sociosexual context. The neurophysiology of sexual arousal is seen as a bidirectional system universal to all vertebrates. The following review includes known neural and genomic mechanisms of a hormone-dependent circuit for simple sex behavior. New information about hormone effects on causal steps related to sex hormones' nuclear receptor isoforms expressed by hypothalamic neurons continues to enrich our understanding of this neurophysiology.

On the role of spatial phase and phase correlation in vision, illusion, and cognition

PubMed Central

Gladilin, Evgeny; Eils, Roland

2015-01-01

Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dis)similarity that can be used for experimental validation of our hypothesis of “cognition by phase correlation.” PMID:25954190

On the role of spatial phase and phase correlation in vision, illusion, and cognition.

PubMed

Gladilin, Evgeny; Eils, Roland

2015-01-01

Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dis)similarity that can be used for experimental validation of our hypothesis of "cognition by phase correlation."

An Integrative Model for the Neural Mechanism of Eye Movement Desensitization and Reprocessing (EMDR)

PubMed Central

Coubard, Olivier A.

2016-01-01

Since the seminal report by Shapiro that bilateral stimulation induces cognitive and emotional changes, 26 years of basic and clinical research have examined the effects of Eye Movement Desensitization and Reprocessing (EMDR) in anxiety disorders, particularly in post-traumatic stress disorder (PTSD). The present article aims at better understanding EMDR neural mechanism. I first review procedural aspects of EMDR protocol and theoretical hypothesis about EMDR effects, and develop the reasons why the scientific community is still divided about EMDR. I then slide from psychology to physiology describing eye movements/emotion interaction from the physiological viewpoint, and introduce theoretical and technical tools used in movement research to re-examine EMDR neural mechanism. Using a recent physiological model for the neuropsychological architecture of motor and cognitive control, the Threshold Interval Modulation with Early Release-Rate of rIse Deviation with Early Release (TIMER-RIDER)—model, I explore how attentional control and bilateral stimulation may participate to EMDR effects. These effects may be obtained by two processes acting in parallel: (i) activity level enhancement of attentional control component; and (ii) bilateral stimulation in any sensorimotor modality, both resulting in lower inhibition enabling dysfunctional information to be processed and anxiety to be reduced. The TIMER-RIDER model offers quantitative predictions about EMDR effects for future research about its underlying physiological mechanisms. PMID:27092064

Combination Across Domains: An MEG Investigation into the Relationship between Mathematical, Pictorial, and Linguistic Processing

PubMed Central

Bemis, Douglas K.; Pylkkänen, Liina

2013-01-01

Debates surrounding the evolution of language often hinge upon its relationship to cognition more generally and many investigations have attempted to demark the boundary between the two. Though results from these studies suggest that language may recruit domain-general mechanisms during certain types of complex processing, the domain-generality of basic combinatorial mechanisms that lie at the core of linguistic processing is still unknown. Our previous work (Bemis and Pylkkänen, 2011, 2012) used magnetoencephalography to isolate neural activity associated with the simple composition of an adjective and a noun (“red boat”) and found increased activity during this processing localized to the left anterior temporal lobe (lATL), ventro-medial prefrontal cortex (vmPFC), and left angular gyrus (lAG). The present study explores the domain-generality of these effects and their associated combinatorial mechanisms through two parallel non-linguistic combinatorial tasks designed to be as minimal and natural as the linguistic paradigm. In the first task, we used pictures of colored shapes to elicit combinatorial conceptual processing similar to that evoked by the linguistic expressions and find increased activity again localized to the vmPFC during combinatorial processing. This result suggests that a domain-general semantic combinatorial mechanism operates during basic linguistic composition, and that activity generated by its processing localizes to the vmPFC. In the second task, we recorded neural activity as subjects performed simple addition between two small numerals. Consistent with a wide array of recent results, we find no effects related to basic addition that coincide with our linguistic effects and instead find increased activity localized to the intraparietal sulcus. This result suggests that the scope of the previously identified linguistic effects is restricted to compositional operations and does not extend generally to all tasks that are merely similar in form. PMID:23293621

Neurophysiology and neural engineering: a review.

PubMed

Prochazka, Arthur

2017-08-01

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. Copyright © 2017 the American Physiological Society.

The Behavioral and Neural Mechanisms Underlying the Tracking of Expertise

PubMed Central

Boorman, Erie D.; O’Doherty, John P.; Adolphs, Ralph; Rangel, Antonio

2013-01-01

Summary Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms’ expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent’s prediction was concordant rather than discordant with the subject’s own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network—medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus—represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. PMID:24360551

Sex differences in the development of brain mechanisms for processing biological motion.

PubMed

Anderson, L C; Bolling, D Z; Schelinski, S; Coffman, M C; Pelphrey, K A; Kaiser, M D

2013-12-01

Disorders related to social functioning including autism and schizophrenia differ drastically in incidence and severity between males and females. Little is known about the neural systems underlying these sex-linked differences in risk and resiliency. Using functional magnetic resonance imaging and a task involving the visual perception of point-light displays of coherent and scrambled biological motion, we discovered sex differences in the development of neural systems for basic social perception. In adults, we identified enhanced activity during coherent biological motion perception in females relative to males in a network of brain regions previously implicated in social perception including amygdala, medial temporal gyrus, and temporal pole. These sex differences were less pronounced in our sample of school-age youth. We hypothesize that the robust neural circuitry supporting social perception in females, which diverges from males beginning in childhood, may underlie sex differences in disorders related to social processing. © 2013 Elsevier Inc. All rights reserved.

Neural Circuits Underlying Fly Larval Locomotion

PubMed Central

Kohsaka, Hiroshi; Guertin, Pierre A.; Nose, Akinao

2017-01-01

Locomotion is a complex motor behavior that may be expressed in different ways using a variety of strategies depending upon species and pathological or environmental conditions. Quadrupedal or bipedal walking, running, swimming, flying and gliding constitute some of the locomotor modes enabling the body, in all cases, to move from one place to another. Despite these apparent differences in modes of locomotion, both vertebrate and invertebrate species share, at least in part, comparable neural control mechanisms for locomotor rhythm and pattern generation and modulation. Significant advances have been made in recent years in studies of the genetic aspects of these control systems. Findings made specifically using Drosophila (fruit fly) models and preparations have contributed to further understanding of the key role of genes in locomotion. This review focuses on some of the main findings made in larval fruit flies while briefly summarizing the basic advantages of using this powerful animal model for studying the neural locomotor system. PMID:27928962

Sex Differences in the Development of Brain Mechanisms for Processing Biological Motion

PubMed Central

Anderson, L.C.; Bolling, D.Z.; Schelinski, S.; Coffman, M.C.; Pelphrey, K.A.; Kaiser, M.D.

2013-01-01

Disorders related to social functioning including autism and schizophrenia differ drastically in incidence and severity between males and females. Little is known about the neural systems underlying these sex-linked differences in risk and resiliency. Using functional magnetic resonance imaging and a task involving the visual perception of point-light displays of coherent and scrambled biological motion, we discovered sex differences in the development of neural systems for basic social perception. In adults, we identified enhanced activity during coherent biological motion perception in females relative to males in a network of brain regions previously implicated in social perception including amygdala, medial temporal gyrus, and temporal pole. These sex differences were less pronounced in our sample of school-age youth. We hypothesize that the robust neural circuitry supporting social perception in females, which diverges from males beginning in childhood, may underlie sex differences in disorders related to social processing. PMID:23876243

Neural signatures of the response to emotional distraction: a review of evidence from brain imaging investigations

PubMed Central

Iordan, A. D.; Dolcos, S.; Dolcos, F.

2013-01-01

Prompt responses to emotional, potentially threatening, stimuli are supported by neural mechanisms that allow for privileged access of emotional information to processing resources. The existence of these mechanisms can also make emotional stimuli potent distracters, particularly when task-irrelevant. The ability to deploy cognitive control in order to cope with emotional distraction is essential for adaptive behavior, while reduced control may lead to enhanced emotional distractibility, which is often a hallmark of affective disorders. Evidence suggests that increased susceptibility to emotional distraction is linked to changes in the processing of emotional information that affect both the basic response to and coping with emotional distraction, but the neural correlates of these phenomena are not clear. The present review discusses emerging evidence from brain imaging studies addressing these issues, and highlights the following three aspects. First, the response to emotional distraction is associated with opposing patterns of activity in a ventral “hot” affective system (HotEmo, showing increased activity) and a dorsal “cold” executive system (ColdEx, showing decreased activity). Second, coping with emotional distraction involves top–down control in order to counteract the bottom-up influence of emotional distraction, and involves interactions between the amygdala and the prefrontal cortex. Third, both the response to and coping with emotional distraction are influenced by individual differences affecting emotional sensitivity and distractibility, which are linked to alterations of both HotEmo and ColdEx neural systems. Collectively, the available evidence identifies specific neural signatures of the response to emotional challenge, which are fundamental to understanding the mechanisms of emotion-cognition interactions in healthy functioning, and the changes linked to individual variation in emotional distractibility and susceptibility to affective disorders. PMID:23761741

The neural oscillations of conflict adaptation in the human frontal region.

PubMed

Tang, Dandan; Hu, Li; Chen, Antao

2013-07-01

Incongruency between print color and the semantic meaning of a word in a classical Stroop task activates the human conflict monitoring system and triggers a behavioral conflict. Conflict adaptation has been suggested to mediate the cortical processing of neural oscillations in such a conflict situation. However, the basic mechanisms that underlie the influence of conflict adaptation on the changes of neural oscillations are not clear. In the present study, electroencephalography (EEG) data were recorded from sixteen healthy human participants while they were performing a color-word Stroop task within a novel look-to-do transition design that included two response modalities. In the 'look' condition, participants were informed to look at the color of presented words but no responses were required; in the 'do' condition, they were informed to make arranged responses to the color of presented words. Behaviorally, a reliable conflict adaptation was observed. Time-frequency analysis revealed that (1) in the 'look' condition, theta-band activity in the left- and right-frontal regions reflected a conflict-related process at a response inhibition level; and (2) in the 'do' condition, both theta-band activity in the left-frontal region and alpha-band activity in the left-, right-, and centro-frontal regions reflected a process of conflict control, which triggered neural and behavioral adaptation. Taken together, these results suggest that there are frontal mechanisms involving neural oscillations that can mediate response inhibition processes and control behavioral conflict. Copyright © 2013 Elsevier B.V. All rights reserved.

A brain-based account of “basic-level” concepts

PubMed Central

Bauer, Andrew James; Just, Marcel Adam

2017-01-01

This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosch and others (Rosch et al., 1976). Applying machine learning techniques to fMRI data, it was possible to determine the semantic content encoded in the neural representations of object concepts at basic and subordinate levels of abstraction. The representation of basic-level concepts (e.g. bird) was spatially broad, encompassing sensorimotor brain areas that encode concrete object properties, and also language and heteromodal integrative areas that encode abstract semantic content. The representation of subordinate-level concepts (robin) was less widely distributed, concentrated in perceptual areas that underlie concrete content. Furthermore, basic-level concepts were representative of their subordinates in that they were neurally similar to their typical but not atypical subordinates (bird was neurally similar to robin but not woodpecker). The findings provide a brain-based account of the advantages that basic-level concepts enjoy in everyday life over subordinate-level concepts: the basic level is a broad topographical representation that encompasses both concrete and abstract semantic content, reflecting the multifaceted yet intuitive meaning of basic-level concepts. PMID:28826947

A brain-based account of "basic-level" concepts.

PubMed

Bauer, Andrew James; Just, Marcel Adam

2017-11-01

This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosch and others (Rosch et al., 1976). Applying machine learning techniques to fMRI data, it was possible to determine the semantic content encoded in the neural representations of object concepts at basic and subordinate levels of abstraction. The representation of basic-level concepts (e.g. bird) was spatially broad, encompassing sensorimotor brain areas that encode concrete object properties, and also language and heteromodal integrative areas that encode abstract semantic content. The representation of subordinate-level concepts (robin) was less widely distributed, concentrated in perceptual areas that underlie concrete content. Furthermore, basic-level concepts were representative of their subordinates in that they were neurally similar to their typical but not atypical subordinates (bird was neurally similar to robin but not woodpecker). The findings provide a brain-based account of the advantages that basic-level concepts enjoy in everyday life over subordinate-level concepts: the basic level is a broad topographical representation that encompasses both concrete and abstract semantic content, reflecting the multifaceted yet intuitive meaning of basic-level concepts. Copyright © 2017 Elsevier Inc. All rights reserved.

24 DOF EMG controlled hybrid actuated prosthetic hand.

PubMed

Atasoy, A; Kaya, E; Toptas, E; Kuchimov, S; Kaplanoglu, E; Ozkan, M

2016-08-01

A complete mechanical design concept of an electromyogram (EMG) controlled hybrid prosthetic hand, with 24 degree of freedom (DOF) anthropomorphic structure is presented. Brushless DC motors along with Shape Memory Alloy (SMA) actuators are used to achieve dexterous functionality. An 8 channel EMG is used for detecting 7 basic hand gestures for control purposes. The prosthetic hand will be integrated with the Neural Network (NNE) based controller in the next phase of the study.

Understanding Others' Regret: A fMRI Study

PubMed Central

Canessa, Nicola; Motterlini, Matteo; Di Dio, Cinzia; Perani, Daniela; Scifo, Paola; Cappa, Stefano F.; Rizzolatti, Giacomo

2009-01-01

Previous studies showed that the understanding of others' basic emotional experiences is based on a “resonant” mechanism, i.e., on the reactivation, in the observer's brain, of the cerebral areas associated with those experiences. The present study aimed to investigate whether the same neural mechanism is activated both when experiencing and attending complex, cognitively-generated, emotions. A gambling task and functional-Magnetic-Resonance-Imaging (fMRI) were used to test this hypothesis using regret, the negative cognitively-based emotion resulting from an unfavorable counterfactual comparison between the outcomes of chosen and discarded options. Do the same brain structures that mediate the experience of regret become active in the observation of situations eliciting regret in another individual? Here we show that observing the regretful outcomes of someone else's choices activates the same regions that are activated during a first-person experience of regret, i.e. the ventromedial prefrontal cortex, anterior cingulate cortex and hippocampus. These results extend the possible role of a mirror-like mechanism beyond basic emotions. PMID:19826471

Nuclear Orphan Receptor TLX Induces Oct-3/4 for the Survival and Maintenance of Adult Hippocampal Progenitors upon Hypoxia*

PubMed Central

Chavali, Pavithra Lakshminarasimhan; Saini, Ravi Kanth Rao; Matsumoto, Yoshiki; Ågren, Hans; Funa, Keiko

2011-01-01

Hypoxia promotes neural stem cell proliferation, the mechanism of which is poorly understood. Here, we have identified the nuclear orphan receptor TLX as a mediator for proliferation and pluripotency of neural progenitors upon hypoxia. We found an enhanced early protein expression of TLX under hypoxia potentiating sustained proliferation of neural progenitors. Moreover, TLX induction upon hypoxia in differentiating conditions leads to proliferation and a stem cell-like phenotype, along with coexpression of neural stem cell markers. Following hypoxia, TLX is recruited to the Oct-3/4 proximal promoter, augmenting the gene transcription and promoting progenitor proliferation and pluripotency. Knockdown of Oct-3/4 significantly reduced TLX-mediated proliferation, highlighting their interdependence in regulating the progenitor pool. Additionally, TLX synergizes with basic FGF to sustain cell viability upon hypoxia, since the knockdown of TLX along with the withdrawal of growth factor results in cell death. This can be attributed to the activation of Akt signaling pathway by TLX, the depletion of which results in reduced proliferation of progenitor cells. Cumulatively, the data presented here demonstrate a new role for TLX in neural stem cell proliferation and pluripotency upon hypoxia. PMID:21135096

Nuclear orphan receptor TLX induces Oct-3/4 for the survival and maintenance of adult hippocampal progenitors upon hypoxia.

PubMed

Chavali, Pavithra Lakshminarasimhan; Saini, Ravi Kanth Rao; Matsumoto, Yoshiki; Ågren, Hans; Funa, Keiko

2011-03-18

Hypoxia promotes neural stem cell proliferation, the mechanism of which is poorly understood. Here, we have identified the nuclear orphan receptor TLX as a mediator for proliferation and pluripotency of neural progenitors upon hypoxia. We found an enhanced early protein expression of TLX under hypoxia potentiating sustained proliferation of neural progenitors. Moreover, TLX induction upon hypoxia in differentiating conditions leads to proliferation and a stem cell-like phenotype, along with coexpression of neural stem cell markers. Following hypoxia, TLX is recruited to the Oct-3/4 proximal promoter, augmenting the gene transcription and promoting progenitor proliferation and pluripotency. Knockdown of Oct-3/4 significantly reduced TLX-mediated proliferation, highlighting their interdependence in regulating the progenitor pool. Additionally, TLX synergizes with basic FGF to sustain cell viability upon hypoxia, since the knockdown of TLX along with the withdrawal of growth factor results in cell death. This can be attributed to the activation of Akt signaling pathway by TLX, the depletion of which results in reduced proliferation of progenitor cells. Cumulatively, the data presented here demonstrate a new role for TLX in neural stem cell proliferation and pluripotency upon hypoxia.

Elastic modulus affects the growth and differentiation of neural stem cells

PubMed Central

Jiang, Xian-feng; Yang, Kai; Yang, Xiao-qing; Liu, Ying-fu; Cheng, Yuan-chi; Chen, Xu-yi; Tu, Yue

2015-01-01

It remains poorly understood if carrier hardness, elastic modulus, and contact area affect neural stem cell growth and differentiation. Tensile tests show that the elastic moduli of Tiansu and SMI silicone membranes are lower than that of an ordinary dish, while the elastic modulus of SMI silicone membrane is lower than that of Tiansu silicone membrane. Neural stem cells from the cerebral cortex of embryonic day 16 Sprague-Dawley rats were seeded onto ordinary dishes as well as Tiansu silicone membrane and SMI silicone membrane. Light microscopy showed that neural stem cells on all three carriers show improved adherence. After 7 days of differentiation, neuron specific enolase, glial fibrillary acidic protein, and myelin basic protein expression was detected by immunofluorescence. Moreover, flow cytometry revealed a higher rate of neural stem cell differentiation into astrocytes on Tiansu and SMI silicone membranes than on the ordinary dish, which was also higher on the SMI than the Tiansu silicone membrane. These findings confirm that all three cell carrier types have good biocompatibility, while SMI and Tiansu silicone membranes exhibit good mechanical homogenization. Thus, elastic modulus affects neural stem cell differentiation into various nerve cells. Within a certain range, a smaller elastic modulus results in a more obvious trend of cell differentiation into astrocytes. PMID:26604916

An Introduction to Neural Networks for Hearing Aid Noise Recognition.

ERIC Educational Resources Information Center

Kim, Jun W.; Tyler, Richard S.

1995-01-01

This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

Basic Emotions in Human Neuroscience: Neuroimaging and Beyond.

PubMed

Celeghin, Alessia; Diano, Matteo; Bagnis, Arianna; Viola, Marco; Tamietto, Marco

2017-01-01

The existence of so-called 'basic emotions' and their defining attributes represents a long lasting and yet unsettled issue in psychology. Recently, neuroimaging evidence, especially related to the advent of neuroimaging meta-analytic methods, has revitalized this debate in the endeavor of systems and human neuroscience. The core theme focuses on the existence of unique neural bases that are specific and characteristic for each instance of basic emotion. Here we review this evidence, outlining contradictory findings, strengths and limits of different approaches. Constructionism dismisses the existence of dedicated neural structures for basic emotions, considering that the assumption of a one-to-one relationship between neural structures and their functions is central to basic emotion theories. While these critiques are useful to pinpoint current limitations of basic emotions theories, we argue that they do not always appear equally generative in fostering new testable accounts on how the brain relates to affective functions. We then consider evidence beyond PET and fMRI, including results concerning the relation between basic emotions and awareness and data from neuropsychology on patients with focal brain damage. Evidence from lesion studies are indeed particularly informative, as they are able to bring correlational evidence typical of neuroimaging studies to causation, thereby characterizing which brain structures are necessary for, rather than simply related to, basic emotion processing. These other studies shed light on attributes often ascribed to basic emotions, such as automaticity of perception, quick onset, and brief duration. Overall, we consider that evidence in favor of the neurobiological underpinnings of basic emotions outweighs dismissive approaches. In fact, the concept of basic emotions can still be fruitful, if updated to current neurobiological knowledge that overcomes traditional one-to-one localization of functions in the brain. In particular, we propose that the structure-function relationship between brain and emotions is better described in terms of pluripotentiality, which refers to the fact that one neural structure can fulfill multiple functions, depending on the functional network and pattern of co-activations displayed at any given moment.

Toward an account of clinical anxiety predicated on basic, neurally mapped mechanisms of Pavlovian fear-learning: the case for conditioned overgeneralization.

PubMed

Lissek, Shmuel

2012-04-01

The past two decades have brought dramatic progress in the neuroscience of anxiety due, in no small part, to animal findings specifying the neurobiology of Pavlovian fear-conditioning. Fortuitously, this neurally mapped process of fear learning is widely expressed in humans, and has been centrally implicated in the etiology of clinical anxiety. Fear-conditioning experiments in anxiety patients thus represent a unique opportunity to bring recent advances in animal neuroscience to bear on working, brain-based models of clinical anxiety. The current presentation details the neural basis and clinical relevance of fear conditioning, and highlights generalization of conditioned fear to stimuli resembling the conditioned danger cue as one of the more robust conditioning markers of clinical anxiety. Studies testing such generalization across a variety of anxiety disorders (panic, generalized anxiety disorder, and social anxiety disorder) with systematic methods developed in animals will next be presented. Finally, neural accounts of overgeneralization deriving from animal and human data will be described with emphasis given to implications for the neurobiology and treatment of clinical anxiety. © 2012 Wiley Periodicals, Inc.

Summary of papers presented at the 2012 seventh international cough symposium

PubMed Central

2013-01-01

Twenty six papers were presented as posters in the Seventh International Symposium on Cough; 12 papers were presented in the Basic Science of Cough session, and 14 papers presented in the Clinical Science of Cough session. These papers explored a wide spectrum of cough-related areas including pathophysiological mechanisms, treatment and detection of cough, and symptom assessment and perception, and were grouped into several general themes for facilitate the discussion. Studies presented in these posters have provided new information that should improve our knowledge on the basic physiology and pharmacology of cough, and the peripheral and central neural mechanisms involved in the generation of the cough motor pattern. In addition, in the clinical science section, studies reporting potential new anti-tussive agents and further characterisation of cough symptoms and perception have provided a base for the fruitful strategies for the development of novel anti-tussive therapies and cough management. PMID:23639195

Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

DTIC Science & Technology

2010-09-24

system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

The neural mediators of kindness-based meditation: a theoretical model

PubMed Central

Mascaro, Jennifer S.; Darcher, Alana; Negi, Lobsang T.; Raison, Charles L.

2015-01-01

Although kindness-based contemplative practices are increasingly employed by clinicians and cognitive researchers to enhance prosocial emotions, social cognitive skills, and well-being, and as a tool to understand the basic workings of the social mind, we lack a coherent theoretical model with which to test the mechanisms by which kindness-based meditation may alter the brain and body. Here, we link contemplative accounts of compassion and loving-kindness practices with research from social cognitive neuroscience and social psychology to generate predictions about how diverse practices may alter brain structure and function and related aspects of social cognition. Contingent on the nuances of the practice, kindness-based meditation may enhance the neural systems related to faster and more basic perceptual or motor simulation processes, simulation of another’s affective body state, slower and higher-level perspective-taking, modulatory processes such as emotion regulation and self/other discrimination, and combinations thereof. This theoretical model will be discussed alongside best practices for testing such a model and potential implications and applications of future work. PMID:25729374

A plausible neural circuit for decision making and its formation based on reinforcement learning.

PubMed

Wei, Hui; Dai, Dawei; Bu, Yijie

2017-06-01

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

Sensorimotor integration: basic concepts, abnormalities related to movement disorders and sensorimotor training-induced cortical reorganization.

PubMed

Machado, Sergio; Cunha, Marlo; Velasques, Bruna; Minc, Daniel; Teixeira, Silmar; Domingues, Clayton A; Silva, Julio G; Bastos, Victor H; Budde, Henning; Cagy, Mauricio; Basile, Luis; Piedade, Roberto; Ribeiro, Pedro

2010-10-01

Sensorimotor integration is defined as the capability of the central nervous system to integrate different sources of stimuli, and parallelly, to transform such inputs in motor actions. To review the basic principles of sensorimotor integration, such as, its neural bases and its elementary mechanisms involved in specific goal-directed tasks performed by healthy subjects, and the abnormalities reported in the most common movement disorders, such as, Parkinson' disease, dystonia and stroke, like the cortical reorganization-related mechanisms. Whether these disorders are associated with an abnormal peripheral sensory input or defective central processing is still unclear, but most of the data support a central mechanism. We found that the sensorimotor integration process plays a potential role in elementary mechanisms involved in specific goal-directed tasks performed by healthy subjects and in occurrence of abnormalities in most common movement disorders and, moreover, play a potential role on the acquisition of abilities that have as critical factor the coupling of different sensory data which will constitute the basis of elaboration of motor outputs consciously goal-directed.

An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

NASA Astrophysics Data System (ADS)

Vrettaros, John; Vouros, George; Drigas, Athanasios S.

This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

[Brain Mechanisms for Measuring Time: Population Coding of Durations].

PubMed

Hayashi, Masamichi J

2016-11-01

Temporal processing is crucial in many aspects of our perception and action. While there is mounting evidence for the encoding mechanisms of spatial ("where") and identity ("what") information, those of temporal information ("when") remain largely unknown. Recent studies suggested that, similarly to the basic visual stimulus features such as orientation, motion direction, and numerical quantity, event durations are also represented by a population of neurons that are tuned for specific, preferred durations. This paper first reviews recent psychophysical studies on duration aftereffect. Changes in the three parameters (response gain, shift, and width of tuning curves) are then discussed that may need to be taken into account in the putative duration-channel model. Next, the potential neural basis of the duration channels is examined by overviewing recent neuroimaging and electrophysiological studies on time perception. Finally, this paper proposes a general neural basis of timing that commonly represents time-differences independent of stimulus types (e.g., a single duration v.s. multiple brief events). This extends the idea of the "when pathway" from the perception of temporal order to the general timing mechanisms for the perception of duration, temporal frequency, and synchrony.

Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity

PubMed Central

Cain, Nicholas; Barreiro, Andrea K.; Shadlen, Michael

2013-01-01

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be exceedingly precise. The need for fine tuning can be overcome via a “robust integrator” mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis. PMID:23446688

A model of TMS-induced I-waves in motor cortex.

PubMed

Rusu, Cătălin V; Murakami, Max; Ziemann, Ulf; Triesch, Jochen

2014-01-01

Transcranial magnetic stimulation (TMS) allows to manipulate neural activity non-invasively, and much research is trying to exploit this ability in clinical and basic research settings. In a standard TMS paradigm, single-pulse stimulation over motor cortex produces repetitive responses in descending motor pathways called I-waves. However, the details of how TMS induces neural activity patterns in cortical circuits to produce these responses remain poorly understood. According to a traditional view, I-waves are due to repetitive synaptic inputs to pyramidal neurons in layer 5 (L5) of motor cortex, but the potential origin of such repetitive inputs is unclear. Here we aim to test the plausibility of an alternative mechanism behind D- and I-wave generation through computational modeling. This mechanism relies on the broad distribution of conduction delays of synaptic inputs arriving at different parts of L5 cells' dendritic trees and their spike generation mechanism. Our model consists of a detailed L5 pyramidal cell and a population of layer 2 and 3 (L2/3) neurons projecting onto it with synapses exhibiting short-term depression. I-waves are simulated as superpositions of spike trains from a large population of L5 cells. Our model successfully reproduces all basic characteristics of I-waves observed in epidural responses during in vivo recordings of conscious humans. In addition, it shows how the complex morphology of L5 neurons might play an important role in the generation of I-waves. In the model, later I-waves are formed due to inputs to distal synapses, while earlier ones are driven by synapses closer to the soma. Finally, the model offers an explanation for the inhibition and facilitation effects in paired-pulse stimulation protocols. In contrast to previous models, which required either neural oscillators or chains of inhibitory interneurons acting upon L5 cells, our model is fully feed-forward without lateral connections or loops. It parsimoniously explains findings from a range of experiments and should be considered as a viable alternative explanation of the generating mechanism of I-waves. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantum physics in neuroscience and psychology: A neurophysicalmodel of the mind/brain interaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwartz, Jeffrey M.; Stapp, Henry P.; Beauregard, Mario

Neuropsychological research on the neural basis of behavior generally posits that brain mechanisms will ultimately suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain is made up entirely of material particles and fields, and that all causal mechanisms relevant to neuroscience can therefore be formulated solely in terms of properties of these elements. Thus terms having intrinsic mentalistic and/or experiential content (e.g., ''feeling,'' ''knowing,'' and ''effort'') are not included as primary causal factors. This theoretical restriction is motivated primarily by ideas about the natural world that have been known to be fundamentally incorrectmore » for more than three quarters of a century. Contemporary basic physical theory differs profoundly from classical physics on the important matter of how the consciousness of human agents enters into the structure of empirical phenomena. The new principles contradict the older idea that local mechanical processes alone can account for the structure of all observed empirical data. Contemporary physical theory brings directly and irreducibly into the overall causal structure certain psychologically described choices made by human agents about how they will act. This key development in basic physical theory is applicable to neuroscience, and it provides neuroscientists and psychologists with an alternative conceptual framework for describing neural processes. Indeed, due to certain structural features of ion channels critical to synaptic function, contemporary physical theory must in principle be used when analyzing human brain dynamics. The new framework, unlike its classical-physics-based predecessor is erected directly upon, and is compatible with, the prevailing principles of physics, and is able to represent more adequately than classical concepts the neuroplastic mechanisms relevant to the growing number of empirical studies of the capacity of directed attention and mental effort to systematically alter brain function.« less

Quantum physics in neuroscience and psychology: a neurophysical model of mind–brain interaction

PubMed Central

Schwartz, Jeffrey M; Stapp, Henry P; Beauregard, Mario

2005-01-01

Neuropsychological research on the neural basis of behaviour generally posits that brain mechanisms will ultimately suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain is made up entirely of material particles and fields, and that all causal mechanisms relevant to neuroscience can therefore be formulated solely in terms of properties of these elements. Thus, terms having intrinsic mentalistic and/or experiential content (e.g. ‘feeling’, ‘knowing’ and ‘effort’) are not included as primary causal factors. This theoretical restriction is motivated primarily by ideas about the natural world that have been known to be fundamentally incorrect for more than three-quarters of a century. Contemporary basic physical theory differs profoundly from classic physics on the important matter of how the consciousness of human agents enters into the structure of empirical phenomena. The new principles contradict the older idea that local mechanical processes alone can account for the structure of all observed empirical data. Contemporary physical theory brings directly and irreducibly into the overall causal structure certain psychologically described choices made by human agents about how they will act. This key development in basic physical theory is applicable to neuroscience, and it provides neuroscientists and psychologists with an alternative conceptual framework for describing neural processes. Indeed, owing to certain structural features of ion channels critical to synaptic function, contemporary physical theory must in principle be used when analysing human brain dynamics. The new framework, unlike its classic-physics-based predecessor, is erected directly upon, and is compatible with, the prevailing principles of physics. It is able to represent more adequately than classic concepts the neuroplastic mechanisms relevant to the growing number of empirical studies of the capacity of directed attention and mental effort to systematically alter brain function. PMID:16147524

Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes

NASA Astrophysics Data System (ADS)

Xie, Chong; Liu, Jia; Fu, Tian-Ming; Dai, Xiaochuan; Zhou, Wei; Lieber, Charles M.

2015-12-01

Direct electrical recording and stimulation of neural activity using micro-fabricated silicon and metal micro-wire probes have contributed extensively to basic neuroscience and therapeutic applications; however, the dimensional and mechanical mismatch of these probes with the brain tissue limits their stability in chronic implants and decreases the neuron-device contact. Here, we demonstrate the realization of a three-dimensional macroporous nanoelectronic brain probe that combines ultra-flexibility and subcellular feature sizes to overcome these limitations. Built-in strains controlling the local geometry of the macroporous devices are designed to optimize the neuron/probe interface and to promote integration with the brain tissue while introducing minimal mechanical perturbation. The ultra-flexible probes were implanted frozen into rodent brains and used to record multiplexed local field potentials and single-unit action potentials from the somatosensory cortex. Significantly, histology analysis revealed filling-in of neural tissue through the macroporous network and attractive neuron-probe interactions, consistent with long-term biocompatibility of the device.

The power of simulation: imagining one's own and other's behavior.

PubMed

Decety, Jean; Grèzes, Julie

2006-03-24

A large number of cognitive neuroscience studies point to the similarities in the neural circuits activated during the generation, imagination, as well as observation of one's own and other's behavior. Such findings support the shared representations account of social cognition, which is suggested to provide the basic mechanism for social interaction. Mental simulation may also be a representational tool to understand the self and others. However, successfully navigating these shared representations--both within oneself and between individuals--constitutes an essential functional property of any autonomous agent. It will be argued that self-awareness and agency, mediated by the temporoparietal (TPJ) area and the prefrontal cortex, are critical aspects of the social mind. Thus, differences as well as similarities between self and other representations at the neural level may be related to the degrees of self-awareness and agency. Overall, these data support the view that social cognition draws on both domain-general mechanisms and domain-specific embodied representations.

Underlying Mechanisms of Tinnitus: Review and Clinical Implications

PubMed Central

Henry, James A.; Roberts, Larry E.; Caspary, Donald M.; Theodoroff, Sarah M.; Salvi, Richard J.

2016-01-01

Background The study of tinnitus mechanisms has increased tenfold in the last decade. The common denominator for all of these studies is the goal of elucidating the underlying neural mechanisms of tinnitus with the ultimate purpose of finding a cure. While these basic science findings may not be immediately applicable to the clinician who works directly with patients to assist them in managing their reactions to tinnitus, a clear understanding of these findings is needed to develop the most effective procedures for alleviating tinnitus. Purpose The goal of this review is to provide audiologists and other health-care professionals with a basic understanding of the neurophysiological changes in the auditory system likely to be responsible for tinnitus. Results It is increasingly clear that tinnitus is a pathology involving neuroplastic changes in central auditory structures that take place when the brain is deprived of its normal input by pathology in the cochlea. Cochlear pathology is not always expressed in the audiogram but may be detected by more sensitive measures. Neural changes can occur at the level of synapses between inner hair cells and the auditory nerve and within multiple levels of the central auditory pathway. Long-term maintenance of tinnitus is likely a function of a complex network of structures involving central auditory and nonauditory systems. Conclusions Patients often have expectations that a treatment exists to cure their tinnitus. They should be made aware that research is increasing to discover such a cure and that their reactions to tinnitus can be mitigated through the use of evidence-based behavioral interventions. PMID:24622858

Neural correlates of math anxiety - an overview and implications.

PubMed

Artemenko, Christina; Daroczy, Gabriella; Nuerk, Hans-Christoph

2015-01-01

Math anxiety is a common phenomenon which can have a negative impact on numerical and arithmetic performance. However, so far little is known about the underlying neurocognitive mechanisms. This mini review provides an overview of studies investigating the neural correlates of math anxiety which provide several hints regarding its influence on math performance: while behavioral studies mostly observe an influence of math anxiety on difficult math tasks, neurophysiological studies show that processing efficiency is already affected in basic number processing. Overall, the neurocognitive literature suggests that (i) math anxiety elicits emotion- and pain-related activation during and before math activities, (ii) that the negative emotional response to math anxiety impairs processing efficiency, and (iii) that math deficits triggered by math anxiety may be compensated for by modulating the cognitive control or emotional regulation network. However, activation differs strongly between studies, depending on tasks, paradigms, and samples. We conclude that neural correlates can help to understand and explore the processes underlying math anxiety, but the data are not very consistent yet.

Neural correlates of math anxiety – an overview and implications

PubMed Central

Artemenko, Christina; Daroczy, Gabriella; Nuerk, Hans-Christoph

2015-01-01

Math anxiety is a common phenomenon which can have a negative impact on numerical and arithmetic performance. However, so far little is known about the underlying neurocognitive mechanisms. This mini review provides an overview of studies investigating the neural correlates of math anxiety which provide several hints regarding its influence on math performance: while behavioral studies mostly observe an influence of math anxiety on difficult math tasks, neurophysiological studies show that processing efficiency is already affected in basic number processing. Overall, the neurocognitive literature suggests that (i) math anxiety elicits emotion- and pain-related activation during and before math activities, (ii) that the negative emotional response to math anxiety impairs processing efficiency, and (iii) that math deficits triggered by math anxiety may be compensated for by modulating the cognitive control or emotional regulation network. However, activation differs strongly between studies, depending on tasks, paradigms, and samples. We conclude that neural correlates can help to understand and explore the processes underlying math anxiety, but the data are not very consistent yet. PMID:26388824

Convergent dysregulation of frontal cortical cognitive and reward systems in eating disorders.

PubMed

Stefano, George B; Ptáček, Radek; Kuželová, Hana; Mantione, Kirk J; Raboch, Jiří; Papezova, Hana; Kream, Richard M

2013-05-10

A substantive literature has drawn a compelling case for the functional involvement of mesolimbic/prefrontal cortical neural reward systems in normative control of eating and in the etiology and persistence of severe eating disorders that affect diverse human populations. Presently, we provide a short review that develops an equally compelling case for the importance of dysregulated frontal cortical cognitive neural networks acting in concert with regional reward systems in the regulation of complex eating behaviors and in the presentation of complex pathophysiological symptoms associated with major eating disorders. Our goal is to highlight working models of major eating disorders that incorporate complementary approaches to elucidate functionally interactive neural circuits defined by their regulatory neurochemical phenotypes. Importantly, we also review evidence-based linkages between widely studied psychiatric and neurodegenerative syndromes (e.g., autism spectrum disorders and Parkinson's disease) and co-morbid eating disorders to elucidate basic mechanisms involving dopaminergic transmission and its regulation by endogenously expressed morphine in these same cortical regions.

Implicit memory in music and language.

PubMed

Ettlinger, Marc; Margulis, Elizabeth H; Wong, Patrick C M

2011-01-01

Research on music and language in recent decades has focused on their overlapping neurophysiological, perceptual, and cognitive underpinnings, ranging from the mechanism for encoding basic auditory cues to the mechanism for detecting violations in phrase structure. These overlaps have most often been identified in musicians with musical knowledge that was acquired explicitly, through formal training. In this paper, we review independent bodies of work in music and language that suggest an important role for implicitly acquired knowledge, implicit memory, and their associated neural structures in the acquisition of linguistic or musical grammar. These findings motivate potential new work that examines music and language comparatively in the context of the implicit memory system.

Award for Distinguished Scientific Contributions: Terry E. Robinson.

PubMed

2016-11-01

The APA Awards for Distinguished Scientific Contributions are presented to persons who, in the opinion of the Committee on Scientific Awards, have made distinguished theoretical or empirical contributions to basic research in psychology. One of the 2016 award winners is Terry E. Robinson, who received this award for "outstanding contributions to understanding the psychological and neural mechanisms underlying stimulant drug responses." Robinson's award citation, biography, and a selected bibliography are presented here. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Neurophysiology Summary

NASA Technical Reports Server (NTRS)

Paloski, William H.

2001-01-01

The terrestrial gravitational field serves as an important orientation reference for human perception and movement, being continually monitored by sensory receptors in the skin, muscles, joints, and vestibular otolith organs. Cues from these graviceptors are used by the brain to estimate spatial orientation and to control balance and movement. Changes in these cues associated with the tonic changes in gravity (gravito-inertial force),during the launch and entry phases of space flight missions result in altered perceptions, degraded motor control performance, and in some cases, "motion" sickness during, and for a period of time after, the g-transitions. In response to these transitions, however, physiological and behavioral response mechanisms are triggered to compensate for altered graviceptor cues and/or to adapt to the new sensory environment. Basic research in the neurophysiology discipline is focused on understanding the characteristic features of and the underlying mechanisms for the normal human response to tonic changes in the gravito-inertial force environment. These studies address fundamental questions regarding the role of graviceptors in orientation and movement in the terrestrial environment, as well as the capacity, specificity, and modes for neural plasticity in the sensory-motor and perceptual systems of the brain. At the 2001 workshop basic research studies were presented addressing: neuroanatomical responses to altered gravity environments, the neural mechanisms for resolving the ambiguity between tilting and translational stimuli in otolith organ sensory input, interactions between the vestibular system and the autonomic nervous system , the roles of haptic and visual cues in spatial orientation, mechanisms for training environment-appropriate sensorimotor responses triggered by environment-specific context cues, and studies of sensori-motor control of posture and locomotion in the terrestrial environment with and without recent exposure to space flight. Building on these basic research studies are more applied studies focused on the development of countermeasures to the untoward neurophysiological responses to space flight. At the 2001 workshop, applied research studies were presented addressing issues related to the use of rotational artificial gravity (centripetal acceleration) as a multisystem (bone, muscle, cardiovascular, and, perhaps, neurovestibular) countermeasure. Also presented was a clinical study reporting on a new rating system for clinical evaluation of postflight functional neurological status.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Combinations of stroke neurorehabilitation to facilitate motor recovery: perspectives on Hebbian plasticity and homeostatic metaplasticity

PubMed Central

Takeuchi, Naoyuki; Izumi, Shin-Ichi

2015-01-01

Motor recovery after stroke involves developing new neural connections, acquiring new functions, and compensating for impairments. These processes are related to neural plasticity. Various novel stroke rehabilitation techniques based on basic science and clinical studies of neural plasticity have been developed to aid motor recovery. Current research aims to determine whether using combinations of these techniques can synergistically improve motor recovery. When different stroke neurorehabilitation therapies are combined, the timing of each therapeutic program must be considered to enable optimal neural plasticity. Synchronizing stroke rehabilitation with voluntary neural and/or muscle activity can lead to motor recovery by targeting Hebbian plasticity. This reinforces the neural connections between paretic muscles and the residual motor area. Homeostatic metaplasticity, which stabilizes the activity of neurons and neural circuits, can either augment or reduce the synergic effect depending on the timing of combination therapy and types of neurorehabilitation that are used. Moreover, the possibility that the threshold and degree of induced plasticity can be altered after stroke should be noted. This review focuses on the mechanisms underlying combinations of neurorehabilitation approaches and their future clinical applications. We suggest therapeutic approaches for cortical reorganization and maximal functional gain in patients with stroke, based on the processes of Hebbian plasticity and homeostatic metaplasticity. Few of the possible combinations of stroke neurorehabilitation have been tested experimentally; therefore, further studies are required to determine the appropriate combination for motor recovery. PMID:26157374

Basic Emotions in Human Neuroscience: Neuroimaging and Beyond

PubMed Central

Celeghin, Alessia; Diano, Matteo; Bagnis, Arianna; Viola, Marco; Tamietto, Marco

2017-01-01

The existence of so-called ‘basic emotions’ and their defining attributes represents a long lasting and yet unsettled issue in psychology. Recently, neuroimaging evidence, especially related to the advent of neuroimaging meta-analytic methods, has revitalized this debate in the endeavor of systems and human neuroscience. The core theme focuses on the existence of unique neural bases that are specific and characteristic for each instance of basic emotion. Here we review this evidence, outlining contradictory findings, strengths and limits of different approaches. Constructionism dismisses the existence of dedicated neural structures for basic emotions, considering that the assumption of a one-to-one relationship between neural structures and their functions is central to basic emotion theories. While these critiques are useful to pinpoint current limitations of basic emotions theories, we argue that they do not always appear equally generative in fostering new testable accounts on how the brain relates to affective functions. We then consider evidence beyond PET and fMRI, including results concerning the relation between basic emotions and awareness and data from neuropsychology on patients with focal brain damage. Evidence from lesion studies are indeed particularly informative, as they are able to bring correlational evidence typical of neuroimaging studies to causation, thereby characterizing which brain structures are necessary for, rather than simply related to, basic emotion processing. These other studies shed light on attributes often ascribed to basic emotions, such as automaticity of perception, quick onset, and brief duration. Overall, we consider that evidence in favor of the neurobiological underpinnings of basic emotions outweighs dismissive approaches. In fact, the concept of basic emotions can still be fruitful, if updated to current neurobiological knowledge that overcomes traditional one-to-one localization of functions in the brain. In particular, we propose that the structure-function relationship between brain and emotions is better described in terms of pluripotentiality, which refers to the fact that one neural structure can fulfill multiple functions, depending on the functional network and pattern of co-activations displayed at any given moment. PMID:28883803

A Neural Mechanism for Nonconscious Activation of Conditioned Placebo and Nocebo Responses.

PubMed

Jensen, Karin B; Kaptchuk, Ted J; Chen, Xiaoyan; Kirsch, Irving; Ingvar, Martin; Gollub, Randy L; Kong, Jian

2015-10-01

Fundamental aspects of human behavior operate outside of conscious awareness. Yet, theories of conditioned responses in humans, such as placebo and nocebo effects on pain, have a strong emphasis on conscious recognition of contextual cues that trigger the response. Here, we investigated the neural pathways involved in nonconscious activation of conditioned pain responses, using functional magnetic resonance imaging in healthy participants. Nonconscious compared with conscious activation of conditioned placebo analgesia was associated with increased activation of the orbitofrontal cortex, a structure with direct connections to affective brain regions and basic reward processing. During nonconscious nocebo, there was increased activation of the thalamus, amygdala, and hippocampus. In contrast to previous assumptions about conditioning in humans, our results show that conditioned pain responses can be elicited independently of conscious awareness and our results suggest a hierarchical activation of neural pathways for nonconscious and conscious conditioned responses. Demonstrating that the human brain has a nonconscious mechanism for responding to conditioned cues has major implications for the role of associative learning in behavioral medicine and psychiatry. Our results may also open up for novel approaches to translational animal-to-human research since human consciousness and animal cognition is an inherent paradox in all behavioral science. © The Author 2014. Published by Oxford University Press.

FGF2 and insulin signaling converge to regulate cyclin D expression in multipotent neural stem cells.

PubMed

Adepoju, Adedamola; Micali, Nicola; Ogawa, Kazuya; Hoeppner, Daniel J; McKay, Ronald D G

2014-03-01

The ex vivo expansion of stem cells is making major contribution to biomedical research. The multipotent nature of neural precursors acutely isolated from the developing central nervous system has been established in a series of studies. Understanding the mechanisms regulating cell expansion in tissue culture would support their expanded use either in cell therapies or to define disease mechanisms. Basic fibroblast growth factor (FGF2) and insulin, ligands for tyrosine kinase receptors, are sufficient to sustain neural stem cells (NSCs) in culture. Interestingly, real-time imaging shows that these cells become multipotent every time they are passaged. Here, we analyze the role of FGF2 and insulin in the brief period when multipotent cells are present. FGF2 signaling results in the phosphorylation of Erk1/2, and activation of c-Fos and c-Jun that lead to elevated cyclin D mRNA levels. Insulin signals through the PI3k/Akt pathway to regulate cyclins at the post-transcriptional level. This precise Boolean regulation extends our understanding of the proliferation of multipotent NSCs and provides a basis for further analysis of proliferation control in the cell states defined by real-time mapping of the cell lineages that form the central nervous system. © 2013 AlphaMed Press.

Vascular-metabolic and GABAergic Inhibitory Correlates of Neural Variability Modulation. A Combined fMRI and PET Study.

PubMed

Qin, Pengmin; Duncan, Niall W; Chen, David Yen-Ting; Chen, Chi-Jen; Huang, Li-Kai; Huang, Zirui; Lin, Chien-Yuan E; Wiebking, Christine; Yang, Che-Ming; Northoff, Georg; Lane, Timothy J

2018-05-21

Neural activity varies continually from moment to moment. Such temporal variability (TV) has been highlighted as a functionally specific brain property playing a fundamental role in cognition. We sought to investigate the mechanisms involved in TV changes between two basic behavioral states, namely having the eyes open (EO) or eyes closed (EC) in vivo in humans. To these ends we acquired BOLD fMRI, ASL, and [ 18 F]-fluoro-deoxyglucose PET in a group of healthy participants (n = 15), along with BOLD fMRI and [ 18 F]-flumazenil PET in a separate group (n = 19). Focusing on an EO- vs EC-sensitive region in the occipital cortex (identified in an independent sample), we show that TV is constrained in the EO condition compared to EC. This reduction is correlated with an increase in energy consumption and with regional GABA A receptor density. This suggests that the modulation of TV by behavioral state involves an increase in overall neural activity that is related to an increased effect from GABAergic inhibition in addition to any excitatory changes. These findings contribute to our understanding of the mechanisms underlying activity variability in the human brain and its control. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

ERPs recorded during early second language exposure predict syntactic learning.

PubMed

Batterink, Laura; Neville, Helen J

2014-09-01

Millions of adults worldwide are faced with the task of learning a second language (L2). Understanding the neural mechanisms that support this learning process is an important area of scientific inquiry. However, most previous studies on the neural mechanisms underlying L2 acquisition have focused on characterizing the results of learning, relying upon end-state outcome measures in which learning is assessed after it has occurred, rather than on the learning process itself. In this study, we adopted a novel and more direct approach to investigate neural mechanisms engaged during L2 learning, in which we recorded ERPs from beginning adult learners as they were exposed to an unfamiliar L2 for the first time. Learners' proficiency in the L2 was then assessed behaviorally using a grammaticality judgment task, and ERP data acquired during initial L2 exposure were sorted as a function of performance on this task. High-proficiency learners showed a larger N100 effect to open-class content words compared with closed-class function words, whereas low-proficiency learners did not show a significant N100 difference between open- and closed-class words. In contrast, amplitude of the N400 word category effect correlated with learners' L2 comprehension, rather than predicting syntactic learning. Taken together, these results indicate that learners who spontaneously direct greater attention to open- rather than closed-class words when processing L2 input show better syntactic learning, suggesting a link between selective attention to open-class content words and acquisition of basic morphosyntactic rules. These findings highlight the importance of selective attention mechanisms for L2 acquisition.

Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

PubMed Central

McDonough, Ian M.; Nashiro, Kaoru

2014-01-01

An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity. PMID:24959130

Neurophysiology and Neuroanatomy of Reflexive and Volitional Saccades: Evidence from Studies of Humans

PubMed Central

McDowell, Jennifer E.; Dyckman, Kara A.; Austin, Benjamin; Clementz, Brett A.

2008-01-01

This review provides a summary of the contributions made by human functional neuroimaging studies to the understanding of neural correlates of saccadic control. The generation of simple visually-guided saccades (redirections of gaze to a visual stimulus or prosaccades) and more complex volitional saccades require similar basic neural circuitry with additional neural regions supporting requisite higher level processes. The saccadic system has been studied extensively in non-human primates (e.g. single unit recordings) and humans (e.g. lesions and neuroimaging). Considerable knowledge of this system’s functional neuroanatomy makes it useful for investigating models of cognitive control. The network involved in prosaccade generation (by definition exogenously-driven) includes subcortical (striatum, thalamus, superior colliculus, and cerebellar vermis) and cortical structures (primary visual, extrastriate, and parietal cortices, and frontal and supplementary eye fields). Activation in these regions is also observed during endogenously-driven voluntary saccades (e.g. antisaccades, ocular motor delayed response or memory saccades, predictive tracking tasks and anticipatory saccades, and saccade sequencing), all of which require complex cognitive processes like inhibition and working memory. These additional requirements are supported by changes in neural activity in basic saccade circuitry and by recruitment of additional neural regions (such as prefrontal and anterior cingulate cortices). Activity in visual cortex is modulated as a function of task demands and may predict the type of saccade to be generated, perhaps via top-down control mechanisms. Neuroimaging studies suggest two foci of activation within FEF - medial and lateral - which may correspond to volitional and reflexive demands, respectively. Future research on saccade control could usefully (i) delineate important anatomical subdivisions that underlie functional differences, (ii) evaluate functional connectivity of anatomical regions supporting saccade generation using methods such as ICA and structural equation modeling, (iii) investigate how context affects behavior and brain activity, and (iv) use multi-modal neuroimaging to maximize spatial and temporal resolution. PMID:18835656

Discrete Neural Signatures of Basic Emotions.

PubMed

Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

2016-06-01

Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory

PubMed Central

Alnajjar, Fady; Yamashita, Yuichi; Tani, Jun

2013-01-01

Higher-order cognitive mechanisms (HOCM), such as planning, cognitive branching, switching, etc., are known to be the outcomes of a unique neural organizations and dynamics between various regions of the frontal lobe. Although some recent anatomical and neuroimaging studies have shed light on the architecture underlying the formation of such mechanisms, the neural dynamics and the pathways in and between the frontal lobe to form and/or to tune the stability level of its working memory remain controversial. A model to clarify this aspect is therefore required. In this study, we propose a simple neurocomputational model that suggests the basic concept of how HOCM, including the cognitive branching and switching in particular, may mechanistically emerge from time-based neural interactions. The proposed model is constructed such that its functional and structural hierarchy mimics, to a certain degree, the biological hierarchy that is believed to exist between local regions in the frontal lobe. Thus, the hierarchy is attained not only by the force of the layout architecture of the neural connections but also through distinct types of neurons, each with different time properties. To validate the model, cognitive branching and switching tasks were simulated in a physical humanoid robot driven by the model. Results reveal that separation between the lower and the higher-level neurons in such a model is an essential factor to form an appropriate working memory to handle cognitive branching and switching. The analyses of the obtained result also illustrates that the breadth of this separation is important to determine the characteristics of the resulting memory, either static memory or dynamic memory. This work can be considered as a joint research between synthetic and empirical studies, which can open an alternative research area for better understanding of brain mechanisms. PMID:23423881

The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory.

PubMed

Alnajjar, Fady; Yamashita, Yuichi; Tani, Jun

2013-01-01

Higher-order cognitive mechanisms (HOCM), such as planning, cognitive branching, switching, etc., are known to be the outcomes of a unique neural organizations and dynamics between various regions of the frontal lobe. Although some recent anatomical and neuroimaging studies have shed light on the architecture underlying the formation of such mechanisms, the neural dynamics and the pathways in and between the frontal lobe to form and/or to tune the stability level of its working memory remain controversial. A model to clarify this aspect is therefore required. In this study, we propose a simple neurocomputational model that suggests the basic concept of how HOCM, including the cognitive branching and switching in particular, may mechanistically emerge from time-based neural interactions. The proposed model is constructed such that its functional and structural hierarchy mimics, to a certain degree, the biological hierarchy that is believed to exist between local regions in the frontal lobe. Thus, the hierarchy is attained not only by the force of the layout architecture of the neural connections but also through distinct types of neurons, each with different time properties. To validate the model, cognitive branching and switching tasks were simulated in a physical humanoid robot driven by the model. Results reveal that separation between the lower and the higher-level neurons in such a model is an essential factor to form an appropriate working memory to handle cognitive branching and switching. The analyses of the obtained result also illustrates that the breadth of this separation is important to determine the characteristics of the resulting memory, either static memory or dynamic memory. This work can be considered as a joint research between synthetic and empirical studies, which can open an alternative research area for better understanding of brain mechanisms.

Stem cell transplantation therapy for multifaceted therapeutic benefits after stroke.

PubMed

Wei, Ling; Wei, Zheng Z; Jiang, Michael Qize; Mohamad, Osama; Yu, Shan Ping

2017-10-01

One of the exciting advances in modern medicine and life science is cell-based neurovascular regeneration of damaged brain tissues and repair of neuronal structures. The progress in stem cell biology and creation of adult induced pluripotent stem (iPS) cells has significantly improved basic and pre-clinical research in disease mechanisms and generated enthusiasm for potential applications in the treatment of central nervous system (CNS) diseases including stroke. Endogenous neural stem cells and cultured stem cells are capable of self-renewal and give rise to virtually all types of cells essential for the makeup of neuronal structures. Meanwhile, stem cells and neural progenitor cells are well-known for their potential for trophic support after transplantation into the ischemic brain. Thus, stem cell-based therapies provide an attractive future for protecting and repairing damaged brain tissues after injury and in various disease states. Moreover, basic research on naïve and differentiated stem cells including iPS cells has markedly improved our understanding of cellular and molecular mechanisms of neurological disorders, and provides a platform for the discovery of novel drug targets. The latest advances indicate that combinatorial approaches using cell based therapy with additional treatments such as protective reagents, preconditioning strategies and rehabilitation therapy can significantly improve therapeutic benefits. In this review, we will discuss the characteristics of cell therapy in different ischemic models and the application of stem cells and progenitor cells as regenerative medicine for the treatment of stroke. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural sensitivity to social deviance predicts attentive processing of peer-group judgment.

PubMed

Schnuerch, Robert; Trautmann-Lengsfeld, Sina Alexa; Bertram, Mario; Gibbons, Henning

2014-01-01

The detection of one's deviance from social norms is an essential mechanism of individual adjustment to group behavior and, thus, for the perpetuation of norms within groups. It has been suggested that error signals in mediofrontal cortex provide the neural basis of such deviance detection, which contributes to later adjustment to the norm. In the present study, we used event-related potentials (ERPs) to demonstrate that, across participants, the strength of mediofrontal brain correlates of the detection of deviance from a peer group's norms was negatively related to attentive processing of the same group's judgments in a later task. We propose that an individual's perception of social deviance might bias basic cognitive processing during further interaction with the group. Strongly perceiving disagreement with a group could cause an individual to avoid or inhibit this group's judgments.

LORETA Neurofeedback in the Precuneus: Operant Conditioning in Basic Mechanisms of Self-Regulation.

PubMed

Cannon, Rex L; Baldwin, Debora R; Diloreto, Dominic J; Phillips, Sherman T; Shaw, Tiffany L; Levy, Jacob J

2014-10-01

Low-resolution brain electomagnetic tomography (LORETA) neurofeedback provides a mechanism to influence the electrical activity of the brain in intracranial space. The aim of this study was to determine the effects of LORETA neurofeedback (LNFB) in the precuneus as a mechanism for improving self-regulation in controls and a heterogeneous diagnostic group (DX). Thirteen participants completed between 10 and 20 sessions of LNFB training in a 3-voxel cluster in the left precuneus. The participants included 5 nonclinical university students, and 8 adults with heterogeneous psychiatric diagnoses. We assessed the effects of LNFB with neurophysiological measures as well as pre- and post-Personality Assessment Inventory (PAI) subscales and selected subtests from the Delis-Kaplan Executive Function System (DKEFS). There was a significant total relative power increase at the precuneus for baseline contrasts for the control group. The DX group did not reach significant levels. All participants showed improvements in executive functions and tended to report significantly less psychopathology. The basic neural mechanisms of self-regulation are poorly understood. The data obtained in this study demonstrate that LNFB in a heterogeneous population enhances executive functions while concordantly decreasing endorsement of psychological symptoms. The alpha frequency in the brain may represent integrative functioning relative to operant efficiency and self-regulatory mechanisms. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Mechanical failure modes of chronically implanted planar silicon-based neural probes for laminar recording

PubMed Central

Kozai, Takashi D. Y.; Catt, Kasey; Li, Xia; Gugel, Zhannetta V.; Olafsson, Valur T.; Vazquez, Alberto L.; Cui, X. Tracy

2014-01-01

Penetrating intracortical electrode arrays that record brain activity longitudinally are powerful tools for basic neuroscience research and emerging clinical applications. However, regardless of the technology used, signals recorded by these electrodes degrade over time. The failure mechanisms of these electrodes are understood to be a complex combination of the biological reactive tissue response and material failure of the device over time. While mechanical mismatch between the brain tissue and implanted neural electrodes have been studied as a source of chronic inflammation and performance degradation, the electrode failure caused by mechanical mismatch between different material properties and different structural components within a device have remained poorly characterized. Using Finite Element Model (FEM) we simulate the mechanical strain on a planar silicon electrode. The results presented here demonstrate that mechanical mismatch between iridium and silicon leads to concentrated strain along the border of the two materials. This strain is further focused on small protrusions such as the electrical traces in planar silicon electrodes. These findings are confirmed with chronic in vivo data (133–189 days) in mice by correlating a combination of single-unit electrophysiology, evoked multi-unit recordings, electrochemical impedance spectroscopy, and scanning electron microscopy from traces and electrode sites with our modeling data. Several modes of mechanical failure of chronically implanted planar silicon electrodes are found that result in degradation and/or loss of recording. These findings highlight the importance of strains and material properties of various subcomponents within an electrode array. PMID:25453935

Functional electrical stimulation-facilitated proliferation and regeneration of neural precursor cells in the brains of rats with cerebral infarction

PubMed Central

Xiang, Yun; Liu, Huihua; Yan, Tiebin; Zhuang, Zhiqiang; Jin, Dongmei; Peng, Yuan

2014-01-01

Previous studies have shown that proliferation of endogenous neural precursor cells cannot alone compensate for the damage to neurons and axons. From the perspective of neural plasticity, we observed the effects of functional electrical stimulation treatment on endogenous neural precursor cell proliferation and expression of basic fibroblast growth factor and epidermal growth factor in the rat brain on the infarct side. Functional electrical stimulation was performed in rat models of acute middle cerebral artery occlusion. Simultaneously, we set up a placebo stimulation group and a sham-operated group. Immunohistochemical staining showed that, at 7 and 14 days, compared with the placebo group, the numbers of nestin (a neural precursor cell marker)-positive cells in the subgranular zone and subventricular zone were increased in the functional electrical stimulation treatment group. Western blot assays and reverse-transcription PCR showed that total protein levels and gene expression of epidermal growth factor and basic fibroblast growth factor were also upregulated on the infarct side. Prehensile traction test results showed that, at 14 days, prehension function of rats in the functional electrical stimulation group was significantly better than in the placebo group. These results suggest that functional electrical stimulation can promote endogenous neural precursor cell proliferation in the brains of acute cerebral infarction rats, enhance expression of basic fibroblast growth factor and epidermal growth factor, and improve the motor function of rats. PMID:25206808

Social Fear Learning: from Animal Models to Human Function.

PubMed

Debiec, Jacek; Olsson, Andreas

2017-07-01

Learning about potential threats is critical for survival. Learned fear responses are acquired either through direct experiences or indirectly through social transmission. Social fear learning (SFL), also known as vicarious fear learning, is a paradigm successfully used for studying the transmission of threat information between individuals. Animal and human studies have begun to elucidate the behavioral, neural and molecular mechanisms of SFL. Recent research suggests that social learning mechanisms underlie a wide range of adaptive and maladaptive phenomena, from supporting flexible avoidance in dynamic environments to intergenerational transmission of trauma and anxiety disorders. This review discusses recent advances in SFL studies and their implications for basic, social and clinical sciences. Copyright © 2017 Elsevier Ltd. All rights reserved.

Implicit Memory in Music and Language

PubMed Central

Ettlinger, Marc; Margulis, Elizabeth H.; Wong, Patrick C. M.

2011-01-01

Research on music and language in recent decades has focused on their overlapping neurophysiological, perceptual, and cognitive underpinnings, ranging from the mechanism for encoding basic auditory cues to the mechanism for detecting violations in phrase structure. These overlaps have most often been identified in musicians with musical knowledge that was acquired explicitly, through formal training. In this paper, we review independent bodies of work in music and language that suggest an important role for implicitly acquired knowledge, implicit memory, and their associated neural structures in the acquisition of linguistic or musical grammar. These findings motivate potential new work that examines music and language comparatively in the context of the implicit memory system. PMID:21927608

Gestalt factors modulate basic spatial vision.

PubMed

Sayim, B; Westheimer, G; Herzog, M H

2010-05-01

Human perception of a stimulus varies depending on the context in which the stimulus is presented. Such contextual modulation has often been explained by two basic neural mechanisms: lateral inhibition and spatial pooling. In the present study, we presented observers with a vernier stimulus flanked by single lines; observers' ability to discriminate the offset direction of the vernier stimulus deteriorated in accordance with both explanations. However, when the flanking lines were part of a geometric shape (i.e., a good Gestalt), this deterioration strongly diminished. These findings cannot be explained by lateral inhibition or spatial pooling. It seems that Gestalt factors play an important role in contextual modulation. We propose that contextual modulation can be used as a quantitative measure to investigate the rules governing the grouping of elements into meaningful wholes.

Introduction to Concepts in Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Predictive information processing in music cognition. A critical review.

PubMed

Rohrmeier, Martin A; Koelsch, Stefan

2012-02-01

Expectation and prediction constitute central mechanisms in the perception and cognition of music, which have been explored in theoretical and empirical accounts. We review the scope and limits of theoretical accounts of musical prediction with respect to feature-based and temporal prediction. While the concept of prediction is unproblematic for basic single-stream features such as melody, it is not straight-forward for polyphonic structures or higher-order features such as formal predictions. Behavioural results based on explicit and implicit (priming) paradigms provide evidence of priming in various domains that may reflect predictive behaviour. Computational learning models, including symbolic (fragment-based), probabilistic/graphical, or connectionist approaches, provide well-specified predictive models of specific features and feature combinations. While models match some experimental results, full-fledged music prediction cannot yet be modelled. Neuroscientific results regarding the early right-anterior negativity (ERAN) and mismatch negativity (MMN) reflect expectancy violations on different levels of processing complexity, and provide some neural evidence for different predictive mechanisms. At present, the combinations of neural and computational modelling methodologies are at early stages and require further research. Copyright © 2012 Elsevier B.V. All rights reserved.

Coding the presence of visual objects in a recurrent neural network of visual cortex.

PubMed

Zwickel, Timm; Wachtler, Thomas; Eckhorn, Reinhard

2007-01-01

Before we can recognize a visual object, our visual system has to segregate it from its background. This requires a fast mechanism for establishing the presence and location of objects independently of their identity. Recently, border-ownership neurons were recorded in monkey visual cortex which might be involved in this task [Zhou, H., Friedmann, H., von der Heydt, R., 2000. Coding of border ownership in monkey visual cortex. J. Neurosci. 20 (17), 6594-6611]. In order to explain the basic mechanisms required for fast coding of object presence, we have developed a neural network model of visual cortex consisting of three stages. Feed-forward and lateral connections support coding of Gestalt properties, including similarity, good continuation, and convexity. Neurons of the highest area respond to the presence of an object and encode its position, invariant of its form. Feedback connections to the lowest area facilitate orientation detectors activated by contours belonging to potential objects, and thus generate the experimentally observed border-ownership property. This feedback control acts fast and significantly improves the figure-ground segregation required for the consecutive task of object recognition.

Neural correlates of processing "self-conscious" vs. "basic" emotions.

PubMed

Gilead, Michael; Katzir, Maayan; Eyal, Tal; Liberman, Nira

2016-01-29

Self-conscious emotions are prevalent in our daily lives and play an important role in both normal and pathological behavior. Despite their immense significance, the neural substrates that are involved in the processing of such emotions are surprisingly under-studied. In light of this, we conducted an fMRI study in which participants thought of various personal events which elicited feelings of negative and positive self-conscious (i.e., guilt, pride) or basic (i.e., anger, joy) emotions. We performed a conjunction analysis to investigate the neural correlates associated with processing events that are related to self-conscious vs. basic emotions, irrespective of valence. The results show that processing self-conscious emotions resulted in activation within frontal areas associated with self-processing and self-control, namely, the mPFC extending to the dACC, and within the lateral-dorsal prefrontal cortex. Processing basic emotions resulted in activation throughout relatively phylogenetically-ancient regions of the cortex, namely in visual and tactile processing areas and in the insular cortex. Furthermore, self-conscious emotions differentially activated the mPFC such that the negative self-conscious emotion (guilt) was associated with a more dorsal activation, and the positive self-conscious emotion (pride) was associated with a more ventral activation. We discuss how these results shed light on the nature of mental representations and neural systems involved in self-reflective and affective processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Molecular Genetics of Autism Spectrum Disorders: Genomic Mechanisms, Neuroimmunopathology, and Clinical Implications

PubMed Central

Guerra, Daniel J.

2011-01-01

Autism spectrum disorders (ASDs) have become increasingly common in recent years. The discovery of single-nucleotide polymorphisms and accompanying copy number variations within the genome has increased our understanding of the architecture of the disease. These genetic and genomic alterations coupled with epigenetic phenomena have pointed to a neuroimmunopathological mechanism for ASD. Model animal studies, developmental biology, and affective neuroscience laid a foundation for dissecting the neural pathways impacted by these disease-generating mechanisms. The goal of current autism research is directed toward a systems biological approach to find the most basic genetic and environmental causes to this severe developmental disease. It is hoped that future genomic and neuroimmunological research will be directed toward finding the road toward prevention, treatment, and cure of ASD. PMID:22937247

Nonpolitical images evoke neural predictors of political ideology.

PubMed

Ahn, Woo-Young; Kishida, Kenneth T; Gu, Xiaosi; Lohrenz, Terry; Harvey, Ann; Alford, John R; Smith, Kevin B; Yaffe, Gideon; Hibbing, John R; Dayan, Peter; Montague, P Read

2014-11-17

Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms that may serve to defend against environmental challenges like contamination and physical threat. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion). We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants' conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject's political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

[Advances in mass spectrometry-based approaches for neuropeptide analysis].

PubMed

Ji, Qianyue; Ma, Min; Peng, Xin; Jia, Chenxi; Ji, Qianyue

2017-07-25

Neuropeptides are an important class of endogenous bioactive substances involved in the function of the nervous system, and connect the brain and other neural and peripheral organs. Mass spectrometry-based neuropeptidomics are designed to study neuropeptides in a large-scale manner and obtain important molecular information to further understand the mechanism of nervous system regulation and the pathogenesis of neurological diseases. This review summarizes the basic strategies for the study of neuropeptides using mass spectrometry, including sample preparation and processing, qualitative and quantitative methods, and mass spectrometry imagining.

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

PubMed

Walter, Florian; Röhrbein, Florian; Knoll, Alois

2015-12-01

The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modulation of attention network activation under antidepressant agents in healthy subjects.

PubMed

Graf, Heiko; Abler, Birgit; Hartmann, Antonie; Metzger, Coraline D; Walter, Martin

2013-07-01

While antidepressants are supposed to exert similar effects on mood and drive via various mechanisms of action, diverging effects are observed regarding side-effects and accordingly on neural correlates of motivation, emotion, reward and salient stimuli processing as a function of the drugs impact on neurotransmission. In the context of erotic stimulation, a unidirectional modulation of attentional functioning despite opposite effects on sexual arousal has been suggested for the selective serotonin reuptake-inhibitor (SSRI) paroxetine and the selective dopamine and noradrenaline reuptake-inhibitor (SDNRI) bupropion. To further elucidate the effects of antidepressant-related alterations of neural attention networks, we investigated 18 healthy males under subchronic administration (7 d) of paroxetine (20 mg), bupropion (150 mg) and placebo within a randomized placebo-controlled cross-over double-blind functional magnetic resonance imaging (fMRI) design during an established preceding attention task. Neuropsychological effects beyond the fMRI-paradigm were assessed by measuring alertness and divided attention. Comparing preceding attention periods of salient vs. neutral pictures, we revealed congruent effects of both drugs vs. placebo within the anterior midcingulate cortex, dorsolateral prefrontal cortex, anterior prefrontal cortex, superior temporal gyrus, anterior insula and the thalamus. Relatively decreased activation in this network was paralleled by slower reaction times in the divided attention task in both verum conditions compared to placebo. Our results suggest similar effects of antidepressant treatments on behavioural and neural attentional functioning by diverging neurochemical pathways. Concurrent alterations of brain regions within a fronto-parietal and cingulo-opercular attention network for top-down control could point to basic neural mechanisms of antidepressant action irrespective of receptor profiles.

Spinal cord injury: promising interventions and realistic goals.

PubMed

McDonald, John W; Becker, Daniel

2003-10-01

Long regarded as impossible, spinal cord repair is approaching the realm of reality as efforts to bridge the gap between bench and bedside point to novel approaches to treatment. It is important to recognize that the research playing field is rapidly changing and that new mechanisms of resource development are required to effectively make the transition from basic science discoveries to effective clinical treatments. This article reviews recent laboratory studies and phase 1 clinical trials in neural and nonneural cell transplantation, stressing that the transition from basic science to clinical applications requires a parallel rather than serial approach, with continuous, two-way feedback to most efficiently translate basic science findings, through evaluation and optimization, to clinical treatments. An example of mobilizing endogenous stem cells for repair is reviewed, with emphasis on the rapid application of basic science to clinical therapy. Successful and efficient transition from basic science to clinical applications requires (1) a parallel rather than a serial approach; (2) development of centers that integrate three spheres of science, translational, transitional, and clinical trials; and (3) development of novel resources to fund the most critically limited step of transitional to clinical trials.

Avoidance-based human Pavlovian-to-instrumental transfer

PubMed Central

Lewis, Andrea H.; Niznikiewicz, Michael A.; Delamater, Andrew R.; Delgado, Mauricio R.

2013-01-01

The Pavlovian-to-instrumental transfer (PIT) paradigm probes the influence of Pavlovian cues over instrumentally learned behavior. The paradigm has been used extensively to probe basic cognitive and motivational processes in studies of animal learning but, more recently, PIT and its underlying neural basis have been extended to investigations in humans. These initial neuroimaging studies of PIT have focused on the influence of appetitively conditioned stimuli on instrumental responses maintained by positive reinforcement, and highlight the involvement of the striatum. In the current study, we sought to understand the neural correlates of PIT in an aversive Pavlovian learning situation when instrumental responding was maintained through negative reinforcement. Participants exhibited specific PIT, wherein selective increases in instrumental responding to conditioned stimuli occurred when the stimulus signaled a specific aversive outcome whose omission negatively reinforced the instrumental response. Additionally, a general PIT effect was observed such that when a stimulus was associated with a different aversive outcome than was used to negatively reinforce instrumental behavior, the presence of that stimulus caused a non-selective increase in overall instrumental responding. Both specific and general PIT behavioral effects correlated with increased activation in corticostriatal circuitry, particularly in the striatum, a region involved in cognitive and motivational processes. These results suggest that avoidance-based PIT utilizes a similar neural mechanism to that seen with PIT in an appetitive context, which has implications for understanding mechanisms of drug-seeking behavior during addiction and relapse. PMID:24118624

Enhanced expression of FNDC5 in human embryonic stem cell-derived neural cells along with relevant embryonic neural tissues.

PubMed

Ghahrizjani, Fatemeh Ahmadi; Ghaedi, Kamran; Salamian, Ahmad; Tanhaei, Somayeh; Nejati, Alireza Shoaraye; Salehi, Hossein; Nabiuni, Mohammad; Baharvand, Hossein; Nasr-Esfahani, Mohammad Hossein

2015-02-25

Availability of human embryonic stem cells (hESCs) has enhanced the capability of basic and clinical research in the context of human neural differentiation. Derivation of neural progenitor (NP) cells from hESCs facilitates the process of human embryonic development through the generation of neuronal subtypes. We have recently indicated that fibronectin type III domain containing 5 protein (FNDC5) expression is required for appropriate neural differentiation of mouse embryonic stem cells (mESCs). Bioinformatics analyses have shown the presence of three isoforms for human FNDC5 mRNA. To differentiate which isoform of FNDC5 is involved in the process of human neural differentiation, we have used hESCs as an in vitro model for neural differentiation by retinoic acid (RA) induction. The hESC line, Royan H5, was differentiated into a neural lineage in defined adherent culture treated by RA and basic fibroblast growth factor (bFGF). We collected all cell types that included hESCs, rosette structures, and neural cells in an attempt to assess the expression of FNDC5 isoforms. There was a contiguous increase in all three FNDC5 isoforms during the neural differentiation process. Furthermore, the highest level of expression of the isoforms was significantly observed in neural cells compared to hESCs and the rosette structures known as neural precursor cells (NPCs). High expression levels of FNDC5 in human fetal brain and spinal cord tissues have suggested the involvement of this gene in neural tube development. Additional research is necessary to determine the major function of FDNC5 in this process. Copyright © 2014 Elsevier B.V. All rights reserved.

Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

NASA Astrophysics Data System (ADS)

Tanadi, Theo

2018-03-01

Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

The Promise of Neurotechnology in Clinical Translational Science.

PubMed

White, Susan W; Richey, John A; Gracanin, Denis; Bell, Martha Ann; LaConte, Stephen; Coffman, Marika; Trubanova, Andrea; Kim, Inyoung

2015-09-01

Neurotechnology is broadly defined as a set of devices used to understand neural processes and applications that can potentially facilitate the brain's ability to repair itself. In the past decade, an increasingly explicit understanding of basic biological mechanisms of brain-related illnesses has produced applications that allow a direct yet noninvasive method to index and manipulate the functioning of the human nervous system. Clinical scientists are poised to apply this technology to assess, treat, and better understand complex socioemotional processes that underlie many forms of psychopathology. In this review, we describe the potential benefits and hurdles, both technical and methodological, of neurotechnology in the context of clinical dysfunction. We also offer a framework for developing and evaluating neurotechnologies that is intended to expedite progress at the nexus of clinical science and neural interface designs by providing a comprehensive vocabulary to describe the necessary features of neurotechnology in the clinic.

The Promise of Neurotechnology in Clinical Translational Science

PubMed Central

White, Susan W.; Richey, John A.; Gracanin, Denis; Bell, Martha Ann; LaConte, Stephen; Coffman, Marika; Trubanova, Andrea; Kim, Inyoung

2014-01-01

Neurotechnology is broadly defined as a set of devices used to understand neural processes and applications that can potentially facilitate the brain’s ability to repair itself. In the past decade, an increasingly explicit understanding of basic biological mechanisms of brain-related illnesses has produced applications that allow a direct yet noninvasive method to index and manipulate the functioning of the human nervous system. Clinical scientists are poised to apply this technology to assess, treat, and better understand complex socioemotional processes that underlie many forms of psychopathology. In this review, we describe the potential benefits and hurdles, both technical and methodological, of neurotechnology in the context of clinical dysfunction. We also offer a framework for developing and evaluating neurotechnologies that is intended to expedite progress at the nexus of clinical science and neural interface designs by providing a comprehensive vocabulary to describe the necessary features of neurotechnology in the clinic. PMID:26504676

Emergence of binocular functional properties in a monocular neural circuit

PubMed Central

Ramdya, Pavan; Engert, Florian

2010-01-01

Sensory circuits frequently integrate converging inputs while maintaining precise functional relationships between them. For example, in mammals with stereopsis, neurons at the first stages of binocular visual processing show a close alignment of receptive-field properties for each eye. Still, basic questions about the global wiring mechanisms that enable this functional alignment remain unanswered, including whether the addition of a second retinal input to an otherwise monocular neural circuit is sufficient for the emergence of these binocular properties. We addressed this question by inducing a de novo binocular retinal projection to the larval zebrafish optic tectum and examining recipient neuronal populations using in vivo two-photon calcium imaging. Notably, neurons in rewired tecta were predominantly binocular and showed matching direction selectivity for each eye. We found that a model based on local inhibitory circuitry that computes direction selectivity using the topographic structure of both retinal inputs can account for the emergence of this binocular feature. PMID:19160507

The historical development of neuroscience in physical rehabilitation.

PubMed

Cohen, H; Reed, K L

1996-01-01

Neuroscience and occupational therapy in physical rehabilitation have developed along parallel tracks. As physicians began to study the neural bases of motor control, they also began to reconsider the sequelae of "hopeless" diagnoses as conditions that they could influence. This change in some physicians' understanding of the neural mechanisms of motor control influenced other clinicians' ideas about patient care. Early work on treatment of patients with cerebral palsy and polio led to improvements in treatment approaches used to facilitate motor skill and functional motor ability in patients with upper motor neuron disorders. From the 1950s to the present, therapists have refined their treatment techniques as knowledge from neuroscience has become available. A few therapists, who are gradually increasing in number, have turned to the laboratory to study basic neuroscience problems that affect clinical treatment. This article describes the development of neuroscience research and neurorehabilitation theories and indicates common themes.

Vision science and schizophrenia research: toward a re-view of the disorder. Editors' introduction to special section.

PubMed

Silverstein, Steven M; Keane, Brian P

2011-07-01

This theme section on vision science and schizophrenia research demonstrates that our understanding of the disorder could be significantly accelerated by a greater adoption of the methods of vision science. In this introduction, we briefly describe what vision science is, how it has advanced our understanding of schizophrenia, and what challenges and opportunities lay ahead regarding schizophrenia research. We then summarize the articles that follow. These include reviews of abnormal form perception (perceptual organization and backward masking) and motion processing, and an article on reduced size contrast illusions experienced by hearing but not deaf persons with schizophrenia. These articles reveal that the methods of basic vision research can provide insights into a number of aspects of the disorder, including pathophysiology, development, cognition, social cognition, and phenomenology. Importantly, studies of visual processing in schizophrenia make it clear that there are impairments in the functioning of basic neural mechanisms (e.g., center-surround modulation, contextual modulation of feedforward processing, reentrant processing) that are found throughout the cortex and that are operative in multiple forms of cognitive dysfunction in the illness. Such evidence allows for an updated view of schizophrenia as a condition involving generalized failures in neural network formation and maintenance, as opposed to a primary failure in a higher level factor (e.g., cognitive control) that accounts for all other types of perceptual and cognitive dysfunction. Finally, studies of vision in schizophrenia can identify sensitive probes of neural functioning that can be used as biomarkers of treatment response.

Vision Science and Schizophrenia Research: Toward a Re-view of the Disorder Editors’ Introduction to Special Section

PubMed Central

Silverstein, Steven M.; Keane, Brian P.

2011-01-01

This theme section on vision science and schizophrenia research demonstrates that our understanding of the disorder could be significantly accelerated by a greater adoption of the methods of vision science. In this introduction, we briefly describe what vision science is, how it has advanced our understanding of schizophrenia, and what challenges and opportunities lay ahead regarding schizophrenia research. We then summarize the articles that follow. These include reviews of abnormal form perception (perceptual organization and backward masking) and motion processing, and an article on reduced size contrast illusions experienced by hearing but not deaf persons with schizophrenia. These articles reveal that the methods of basic vision research can provide insights into a number of aspects of the disorder, including pathophysiology, development, cognition, social cognition, and phenomenology. Importantly, studies of visual processing in schizophrenia make it clear that there are impairments in the functioning of basic neural mechanisms (eg, center-surround modulation, contextual modulation of feedforward processing, reentrant processing) that are found throughout the cortex and that are operative in multiple forms of cognitive dysfunction in the illness. Such evidence allows for an updated view of schizophrenia as a condition involving generalized failures in neural network formation and maintenance, as opposed to a primary failure in a higher level factor (eg, cognitive control) that accounts for all other types of perceptual and cognitive dysfunction. Finally, studies of vision in schizophrenia can identify sensitive probes of neural functioning that can be used as biomarkers of treatment response. PMID:21700588

Smiling faces and cash bonuses: Exploring common affective coding across positive and negative emotional and motivational stimuli using fMRI.

PubMed

Park, Haeme R P; Kostandyan, Mariam; Boehler, C Nico; Krebs, Ruth M

2018-06-01

Although it is clear that emotional and motivational manipulations yield a strong influence on cognition and behaviour, these domains have mostly been investigated in independent research lines. Therefore, it remains poorly understood how far these affective manipulations overlap in terms of their underlying neural activations, especially in light of previous findings that suggest a shared valence mechanism across multiple affective processing domains (e.g., monetary incentives, primary rewards, emotional events). This is particularly interesting considering the commonality between emotional and motivational constructs in terms of their basic affective nature (positive vs. negative), but dissociations in terms of instrumentality, in that only reward-related stimuli are typically associated with performance-contingent outcomes. Here, we aimed to examine potential common neural processes triggered by emotional and motivational stimuli in matched tasks within participants using functional magnetic resonance imaging (fMRI). Across tasks, we found shared valence effects in the ventromedial prefrontal cortex and left inferior frontal gyrus (part of dorsolateral prefrontal cortex), with increased activity for positive and negative stimuli, respectively. Despite this commonality, emotion and reward tasks featured differential behavioural patterns in that negative valence effects (performance costs) were exclusive to emotional stimuli, while positive valence effects (performance benefits) were only observed for reward-related stimuli. Overall, our data suggest a common affective coding mechanism across different task domains and support the idea that monetary incentives entail signed basic valence signals, above and beyond the instruction to perform both gain and loss trials as accurately as possible to maximise the outcome.

Why Are There Developmental Stages in Language Learning? A Developmental Robotics Model of Language Development.

PubMed

Morse, Anthony F; Cangelosi, Angelo

2017-02-01

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to "switch" between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills. Copyright © 2016 Cognitive Science Society, Inc.

[Education and training in neurology: update].

PubMed

Yanagisawa, Nobuo

2010-11-01

Progress in basic neurosciences and advances in technology in the last decades have contributed to clarification of neural mechanisms in behavior or cognition in health and disease. They have elaborated diagnosis and treatment of nervous diseases remarkably. Needs in neurologists in both primary and specific medical services are rapidly increasing, with aging society and progresses in medical care in Japan. Attraction of neurology for students and junior residents is a great concern of Japanese Society of Neurology. In the undergraduate education, recent achievement in basic neurosciences including neurogenetics, molecular cytology, physio-pathology and imaging technique should be taught comprehensively. In the early postgraduate course for two years, neurology is either elective or obligatory depending on the curriculum of training institutions. Work at the stroke care unit is strongly recommended in the course of emergency service, which is mandatory. Experiences in acute infectious diseases, in various stages of neurodegenerative diseases, in collaboration with other specialist doctors for systemic diseases including metabolic or collagen diseases, in collaboration with other medical personnel in care of dementia are all included in advanced stages of postgraduate education before board examination. In summary, studies for practical services as well as clinical researches, teaching of symptoms and signs based on neural functions, and socio-economical issues for chronic nervous diseases in aged society are important in the education in neurology.

Achieving "organic compositionality" through self-organization: reviews on brain-inspired robotics experiments.

PubMed

Tani, Jun; Nishimoto, Ryunosuke; Paine, Rainer W

2008-05-01

The current paper examines how compositional structures can self-organize in given neuro-dynamical systems when robot agents are forced to learn multiple goal-directed behaviors simultaneously. Firstly, we propose a basic model accounting for the roles of parietal-premotor interactions for representing skills for goal-directed behaviors. The basic model had been implemented in a set of robotics experiments employing different neural network architectures. The comparative reviews among those experimental results address the issues of local vs distributed representations in representing behavior and the effectiveness of level structures associated with different sensory-motor articulation mechanisms. It is concluded that the compositional structures can be acquired "organically" by achieving generalization in learning and by capturing the contextual nature of skilled behaviors under specific conditions. Furthermore, the paper discusses possible feedback for empirical neuroscience studies in the future.

A freely-moving monkey treadmill model.

PubMed

Foster, Justin D; Nuyujukian, Paul; Freifeld, Oren; Gao, Hua; Walker, Ross; I Ryu, Stephen; H Meng, Teresa; Murmann, Boris; J Black, Michael; Shenoy, Krishna V

2014-08-01

Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

Sexual orientation-related differences in prepulse inhibition of the human startle response.

PubMed

Rahman, Qazi; Kumari, Veena; Wilson, Glenn D

2003-10-01

Prepulse inhibition (PPI) refers to a reduction in the startle response to a strong sensory stimulus when this stimulus is preceded by a weaker stimulus--the prepulse. PPI reflects a nonlearned sensorimotor gating mechanism and also shows a robust gender difference, with women exhibiting lower PPI than men. The present study examined the eyeblink startle responses to acoustic stimuli of 59 healthy heterosexual and homosexual men and women. Homosexual women showed significantly masculinized PPI compared with heterosexual women, whereas no difference was observed in PPI between homosexual and heterosexual men. These data provide the first evidence for within-gender differences in basic sensorimotor gating mechanisms and implicate the known neural substrates of PPI in human sexual orientation. (c) 2003 APA, all rights reserved

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research

PubMed Central

Lachaux, Jean-Philippe; Axmacher, Nikolai; Mormann, Florian; Halgren, Eric; Crone, Nathan E.

2013-01-01

Human intracranial EEG (iEEG) recordings are primarily performed in epileptic patients for presurgical mapping. When patients perform cognitive tasks, iEEG signals reveal high-frequency neural activities (HFA, between around 40 Hz and 150 Hz) with exquisite anatomical, functional and temporal specificity. Such HFA were originally interpreted in the context of perceptual or motor binding, in line with animal studies on gamma-band (‘40Hz’) neural synchronization. Today, our understanding of HFA has evolved into a more general index of cortical processing: task-induced HFA reveals, with excellent spatial and time resolution, the participation of local neural ensembles in the task-at-hand, and perhaps the neural communication mechanisms allowing them to do so. This review promotes the claim that studying HFA with iEEG provides insights into the neural bases of cognition that cannot be derived as easily from other approaches, such as fMRI. We provide a series of examples supporting that claim, drawn from studies on memory, language and default-mode networks, and successful attempts of real-time functional mapping. These examples are followed by several guidelines for HFA research, intended for new groups interested by this approach. Overall, iEEG research on HFA should play an increasing role in cognitive neuroscience in humans, because it can be explicitly linked to basic research in animals. We conclude by discussing the future evolution of this field, which might expand that role even further, for instance through the use of multi-scale electrodes and the fusion of iEEG with MEG and fMRI. PMID:22750156

Mechanisms of Long Non-Coding RNAs in the Assembly and Plasticity of Neural Circuitry.

PubMed

Wang, Andi; Wang, Junbao; Liu, Ying; Zhou, Yan

2017-01-01

The mechanisms underlying development processes and functional dynamics of neural circuits are far from understood. Long non-coding RNAs (lncRNAs) have emerged as essential players in defining identities of neural cells, and in modulating neural activities. In this review, we summarized latest advances concerning roles and mechanisms of lncRNAs in assembly, maintenance and plasticity of neural circuitry, as well as lncRNAs' implications in neurological disorders. We also discussed technical advances and challenges in studying functions and mechanisms of lncRNAs in neural circuitry. Finally, we proposed that lncRNA studies would advance our understanding on how neural circuits develop and function in physiology and disease conditions.

Modelling cochlear mechanics.

PubMed

Ni, Guangjian; Elliott, Stephen J; Ayat, Mohammad; Teal, Paul D

2014-01-01

The cochlea plays a crucial role in mammal hearing. The basic function of the cochlea is to map sounds of different frequencies onto corresponding characteristic positions on the basilar membrane (BM). Sounds enter the fluid-filled cochlea and cause deflection of the BM due to pressure differences between the cochlear fluid chambers. These deflections travel along the cochlea, increasing in amplitude, until a frequency-dependent characteristic position and then decay away rapidly. The hair cells can detect these deflections and encode them as neural signals. Modelling the mechanics of the cochlea is of help in interpreting experimental observations and also can provide predictions of the results of experiments that cannot currently be performed due to technical limitations. This paper focuses on reviewing the numerical modelling of the mechanical and electrical processes in the cochlea, which include fluid coupling, micromechanics, the cochlear amplifier, nonlinearity, and electrical coupling.

[Neurobiología y psicoanálisis].

PubMed

Rosler, J Roberto

2002-01-01

There would be a conceptual bridge between Psychoanalysis and the Neurosciences that would allow the translation of psychoanalytic concepts into neural mechanisms and vice-versa. Different Freudian postulates, such as that different types of anxiety would emerge from various cerebral interactions, the motivational regulatory functions of the impulse, the conscious emotion as the perception of something basically unconscious, the mechanism of repression in the traumatic memory, the existence of a system associated with the unconscious affective processes and regulated by the principle of pleasure - displeasure, the emotional representation as a basis of the more primitive cerebral structures, and the Oedipo complex, among others, are finding their biological ratification in different laboratory studies. This conceptual bridge would not only be a "Psychoanalysis-Neurobiological mechanisms" translator, but would also, through the integrated conceptualization of the psychoanalytical neurobiological aspects of emotion, generate relevant therapeutic models.

Modelling Cochlear Mechanics

PubMed Central

Elliott, Stephen J.; Teal, Paul D.

2014-01-01

The cochlea plays a crucial role in mammal hearing. The basic function of the cochlea is to map sounds of different frequencies onto corresponding characteristic positions on the basilar membrane (BM). Sounds enter the fluid-filled cochlea and cause deflection of the BM due to pressure differences between the cochlear fluid chambers. These deflections travel along the cochlea, increasing in amplitude, until a frequency-dependent characteristic position and then decay away rapidly. The hair cells can detect these deflections and encode them as neural signals. Modelling the mechanics of the cochlea is of help in interpreting experimental observations and also can provide predictions of the results of experiments that cannot currently be performed due to technical limitations. This paper focuses on reviewing the numerical modelling of the mechanical and electrical processes in the cochlea, which include fluid coupling, micromechanics, the cochlear amplifier, nonlinearity, and electrical coupling. PMID:25136555

What does semantic tiling of the cortex tell us about semantics?

PubMed

Barsalou, Lawrence W

2017-10-01

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) feature and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Nonpolitical Images Evoke Neural Predictors of Political Ideology

PubMed Central

Ahn, Woo-Young; Kishida, Kenneth T.; Gu, Xiaosi; Lohrenz, Terry; Harvey, Ann; Alford, John R.; Smith, Kevin B.; Yaffe, Gideon; Hibbing, John R.; Dayan, Peter; Montague, P. Read

2014-01-01

Summary Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy [1, 2]. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms [3–7] that may serve to defend against environmental challenges like contamination and physical threat [8–12]. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion) [13]. We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants’ conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject’s political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated. PMID:25447997

Noradrenaline effects on social behaviour, intergroup relations, and moral decisions.

PubMed

Terbeck, S; Savulescu, J; Chesterman, L P; Cowen, P J

2016-07-01

Recent research has begun to elucidate the neural basis of higher order social concepts, such as the mechanisms involved in intergroup relations, and moral judgments. Most theories have concentrated on higher order emotions, such as guilt, shame, or empathy, as core mechanisms. Accordingly, psychopharmacological and neurobiological studies have investigated the effects of manipulating serotonin or oxytocin activity on moral and social decisions and attitudes. However, recently it has been determined that changes in more basic emotions, such as fear and anger, might also have a significant role in social and moral cognition. This article summarizes psychopharmacological and fMRI research on the role of noradrenaline in higher order social cognition suggesting that indeed noradrenergic mediated affective changes might play key - and probably causal - role in certain social attitudes and moral judgments. Social judgments may also be directly influenced by numerous neurotransmitter manipulations but these effects could be mediated by modulation of basic emotions which appear to play an essential role in the formation of social concepts and moral behaviour. Copyright © 2016. Published by Elsevier Ltd.

Optical-Correlator Neural Network Based On Neocognitron

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

PubMed Central

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied. PMID:26928718

What songbirds teach us about learning

NASA Astrophysics Data System (ADS)

Brainard, Michael S.; Doupe, Allison J.

2002-05-01

Bird fanciers have known for centuries that songbirds learn their songs. This learning has striking parallels to speech acquisition: like humans, birds must hear the sounds of adults during a sensitive period, and must hear their own voice while learning to vocalize. With the discovery and investigation of discrete brain structures required for singing, songbirds are now providing insights into neural mechanisms of learning. Aided by a wealth of behavioural observations and species diversity, studies in songbirds are addressing such basic issues in neuroscience as perceptual and sensorimotor learning, developmental regulation of plasticity, and the control and function of adult neurogenesis.

Unraveling Cajal's view of the olfactory system

PubMed Central

Figueres-Oñate, María; Gutiérrez, Yolanda; López-Mascaraque, Laura

2014-01-01

The olfactory system has a highly regular organization of interconnected synaptic circuits from the periphery. It is therefore an excellent model for understanding general principles about how the brain processes information. Cajal revealed the basic cell types and their interconnections at the end of the XIX century. Since his original descriptions, the observation and analysis of the olfactory system and its components represents a major topic in neuroscience studies, providing important insights into the neural mechanisms. In this review, we will highlight the importance of Cajal contributions and his legacy to the actual knowledge of the olfactory system. PMID:25071462

Advanced technology and truth in advertising

NASA Astrophysics Data System (ADS)

Landauer, Rolf

1990-09-01

Most proposals for new technological approaches fail, and that is reasonable. Despite that, most of the technological proposals arising from basic science are promoted unhesitantly, with little attention to critical appraisal, even little opportunity for the presentation of criticism. We discuss several case histories related to devices intended to displace the transistor in computer logic. Our list includes devices using control of quantum mechanically coherent electron transmission, devices operating at a molecular level, and devices using nonlinear electromagnetic interaction. Neural networks are placed in a different category; something seems to be coming out of this field after several decades of effort.

The Potential Neural Mechanisms of Acute Indirect Vibration

PubMed Central

2011-01-01

There is strong evidence to suggest that acute indirect vibration acts on muscle to enhance force, power, flexibility, balance and proprioception suggesting neural enhancement. Nevertheless, the neural mechanism(s) of vibration and its potentiating effect have received little attention. One proposal suggests that spinal reflexes enhance muscle contraction through a reflex activity known as tonic vibration stretch reflex (TVR), which increases muscle activation. However, TVR is based on direct, brief, and high frequency vibration (>100 Hz) which differs to indirect vibration, which is applied to the whole body or body parts at lower vibration frequency (5-45 Hz). Likewise, muscle tuning and neuromuscular aspects are other candidate mechanisms used to explain the vibration phenomenon. But there is much debate in terms of identifying which neural mechanism(s) are responsible for acute vibration; due to a number of studies using various vibration testing protocols. These protocols include: different methods of application, vibration variables, training duration, exercise types and a range of population groups. Therefore, the neural mechanism of acute vibration remain equivocal, but spinal reflexes, muscle tuning and neuromuscular aspects are all viable factors that may contribute in different ways to increasing muscular performance. Additional research is encouraged to determine which neural mechanism(s) and their contributions are responsible for acute vibration. Testing variables and vibration applications need to be standardised before reaching a consensus on which neural mechanism(s) occur during and post-vibration. Key points There is strong evidence to suggest that acute indirect vibration acts on muscle to enhance force, power, flexibility, balance and proprioception, but little attention has been given to the neural mechanism(s) of acute indirect vibration. Current findings suggest that acute vibration exposure may cause a neural response, but there is little consensus on identifying which neural mechanism(s) are specifically responsible. This is due to a number of studies using various vibration testing protocols (i.e.varying frequencies, amplitudes, durations, and methods of application). Spinal reflexes, muscle tuning and neuromuscular aspects and central motor command are all viable neuromechanical factors that may contribute at different stages to transiently increasing muscular performance. Additional research is encouraged to determine when (pre, during and post) the different neural mechanism(s) respond to direct and indirect vibration stimuli. PMID:24149291

Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions.

PubMed

Hyafil, Alexandre; Giraud, Anne-Lise; Fontolan, Lorenzo; Gutkin, Boris

2015-11-01

Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial attention improves the quality of population codes in human visual cortex.

PubMed

Saproo, Sameer; Serences, John T

2010-08-01

Selective attention enables sensory input from behaviorally relevant stimuli to be processed in greater detail, so that these stimuli can more accurately influence thoughts, actions, and future goals. Attention has been shown to modulate the spiking activity of single feature-selective neurons that encode basic stimulus properties (color, orientation, etc.). However, the combined output from many such neurons is required to form stable representations of relevant objects and little empirical work has formally investigated the relationship between attentional modulations on population responses and improvements in encoding precision. Here, we used functional MRI and voxel-based feature tuning functions to show that spatial attention induces a multiplicative scaling in orientation-selective population response profiles in early visual cortex. In turn, this multiplicative scaling correlates with an improvement in encoding precision, as evidenced by a concurrent increase in the mutual information between population responses and the orientation of attended stimuli. These data therefore demonstrate how multiplicative scaling of neural responses provides at least one mechanism by which spatial attention may improve the encoding precision of population codes. Increased encoding precision in early visual areas may then enhance the speed and accuracy of perceptual decisions computed by higher-order neural mechanisms.

Interactions between glutamate, dopamine, and the neuronal signature of response inhibition in the human striatum.

PubMed

Lorenz, Robert C; Gleich, Tobias; Buchert, Ralph; Schlagenhauf, Florian; Kühn, Simone; Gallinat, Jürgen

2015-10-01

Response inhibition is a basic mechanism in cognitive control and dysfunctional in major psychiatric disorders. The neuronal mechanisms are in part driven by dopamine in the striatum. Animal data suggest a regulatory role of glutamate on the level of the striatum. We used a trimodal imaging procedure of the human striatum including F18-DOPA positron emission tomography, proton magnetic resonance spectroscopy, and functional magnetic resonance imaging of a stop signal task. We investigated dopamine synthesis capacity and glutamate concentration in vivo and their relation to functional properties of response inhibition. A mediation analysis revealed a significant positive association between dopamine synthesis capacity and inhibition-related neural activity in the caudate nucleus. This relationship was significantly mediated by striatal glutamate concentration. Furthermore, stop signal reaction time was inversely related to striatal activity during inhibition. The data show, for the first time in humans, an interaction between dopamine, glutamate, and the neural signature of response inhibition in the striatum. This finding stresses the importance of the dopamine-glutamate interaction for behavior and may facilitate the understanding of psychiatric disorders characterized by impaired response inhibition. © 2015 Wiley Periodicals, Inc.

The New Neurobiology of Autism

PubMed Central

Minshew, Nancy J.; Williams, Diane L.

2008-01-01

This review covers a fraction of the new research developments in autism but establishes the basic elements of the new neurobiologic understanding of autism. Autism is a polygenetic developmental neurobiologic disorder with multiorgan system involvement, though it predominantly involves central nervous system dysfunction. The evidence supports autism as a disorder of the association cortex, both its neurons and their projections. In particular, it is a disorder of connectivity, which appears, from current evidence, to primarily involve intrahemispheric connectivity. The focus of connectivity studies thus far has been on white matter, but alterations in functional magnetic resonance imaging activation suggest that intracortical connectivity is also likely to be disturbed. Furthermore, the disorder has a broad impact on cognitive and neurologic functioning. Deficits in high-functioning individuals occur in processing that places high demands on integration of information and coordination of multiple neural systems. Intact or enhanced abilities share a dependence on low information-processing demands and local neural connections. This multidomain model with shared characteristics predicts an underlying pathophysiologic mechanism that impacts the brain broadly, according to a common neurobiologic principle. The multiorgan system involvement and diversity of central nervous system findings suggest an epigenetic mechanism. PMID:17620483

Investigating the physiology of brain activation with MRI

NASA Astrophysics Data System (ADS)

Buxton, Richard B.; Uludag, Kamil; Dubowitz, David J.

2004-04-01

Functional magnetic resonance imaging (fMRI) has become a powerful tool for investigating the working human brain based on the blood oxygenation level dependent (BOLD) effect on the MR signal. However, despite the widespread use of fMRI techniques for mapping brain activation, the basic physiological mechanisms underlying the observed signal changes are still poorly understood. Arterial spin labeling (ASL) techniques, which measure cerebral blood flow (CBF) and the BOLD effect simultaneously, provide a useful tool for investigating these physiological questions. In this paper, recent results of studies manipulating the baseline CBF both pharmacologically and physiologically will be discussed. These data are consistent with a feed-forward mechanism of neurovascular coupling, and suggest that the CBF change itself may be a more robust reflection of neural activity changes than the BOLD effect. Consistent with these data, a new thermodynamic hypothesis is proposed for the physiological function of CBF regulation: maintenance of the [O2]/[CO2] concentration ratio at the mitochondria in order to preserve the free energy available from oxidative metabolism. A kinetic model based on this hypothesis provides a reasonable quantitative description of the CBF changes associated with neural activity and altered blood gases (CO2 and O2).

GaAs Optoelectronic Integrated-Circuit Neurons

NASA Technical Reports Server (NTRS)

Lin, Steven H.; Kim, Jae H.; Psaltis, Demetri

1992-01-01

Monolithic GaAs optoelectronic integrated circuits developed for use as artificial neurons. Neural-network computer contains planar arrays of optoelectronic neurons, and variable synaptic connections between neurons effected by diffraction of light from volume hologram in photorefractive material. Basic principles of neural-network computers explained more fully in "Optoelectronic Integrated Circuits For Neural Networks" (NPO-17652). In present circuits, devices replaced by metal/semiconductor field effect transistors (MESFET's), which consume less power.

Neural Meta-Memes Framework for Combinatorial Optimization

NASA Astrophysics Data System (ADS)

Song, Li Qin; Lim, Meng Hiot; Ong, Yew Soon

In this paper, we present a Neural Meta-Memes Framework (NMMF) for combinatorial optimization. NMMF is a framework which models basic optimization algorithms as memes and manages them dynamically when solving combinatorial problems. NMMF encompasses neural networks which serve as the overall planner/coordinator to balance the workload between memes. We show the efficacy of the proposed NMMF through empirical study on a class of combinatorial problem, the quadratic assignment problem (QAP).

Moving towards causality in attention-deficit hyperactivity disorder: overview of neural and genetic mechanisms

PubMed Central

Gallo, Eduardo F; Posner, Jonathan

2016-01-01

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by developmentally inappropriate levels of inattention and hyperactivity or impulsivity. The heterogeneity of its clinical manifestations and the differential responses to treatment and varied prognoses have long suggested myriad underlying causes. Over the past decade, clinical and basic research efforts have uncovered many behavioural and neurobiological alterations associated with ADHD, from genes to higher order neural networks. Here, we review the neurobiology of ADHD by focusing on neural circuits implicated in the disorder and discuss how abnormalities in circuitry relate to symptom presentation and treatment. We summarise the literature on genetic variants that are potentially related to the development of ADHD, and how these, in turn, might affect circuit function and relevant behaviours. Whether these underlying neurobiological factors are causally related to symptom presentation remains unresolved. Therefore, we assess efforts aimed at disentangling issues of causality, and showcase the shifting research landscape towards endophenotype refinement in clinical and preclinical settings. Furthermore, we review approaches being developed to understand the neurobiological underpinnings of this complex disorder including the use of animal models, neuromodulation, and pharmaco-imaging studies. PMID:27183902

Distinct pathways of neural coupling for different basic emotions.

PubMed

Tettamanti, Marco; Rognoni, Elena; Cafiero, Riccardo; Costa, Tommaso; Galati, Dario; Perani, Daniela

2012-01-16

Emotions are complex events recruiting distributed cortical and subcortical cerebral structures, where the functional integration dynamics within the involved neural circuits in relation to the nature of the different emotions are still unknown. Using fMRI, we measured the neural responses elicited by films representing basic emotions (fear, disgust, sadness, happiness). The amygdala and the associative cortex were conjointly activated by all basic emotions. Furthermore, distinct arrays of cortical and subcortical brain regions were additionally activated by each emotion, with the exception of sadness. Such findings informed the definition of three effective connectivity models, testing for the functional integration of visual cortex and amygdala, as regions processing all emotions, with domain-specific regions, namely: i) for fear, the frontoparietal system involved in preparing adaptive motor responses; ii) for disgust, the somatosensory system, reflecting protective responses against contaminating stimuli; iii) for happiness: medial prefrontal and temporoparietal cortices involved in understanding joyful interactions. Consistently with these domain-specific models, the results of the effective connectivity analysis indicate that the amygdala is involved in distinct functional integration effects with cortical networks processing sensorimotor, somatosensory, or cognitive aspects of basic emotions. The resulting effective connectivity networks may serve to regulate motor and cognitive behavior based on the quality of the induced emotional experience. Copyright © 2011. Published by Elsevier Inc.

The SUMO Pathway Promotes Basic Helix-Loop-Helix Proneural Factor Activity via a Direct Effect on the Zn Finger Protein Senseless

PubMed Central

Chen, Angela; Huang, Yan Chang; Wang, Pin Yao; Kemp, Sadie E.

2012-01-01

During development, proneural transcription factors of the basic helix-loop-helix (bHLH) family are required to commit cells to a neural fate. In Drosophila neurogenesis, a key mechanism promoting sense organ precursor (SOP) fate is the synergy between proneural factors and their coactivator Senseless in transcriptional activation of target genes. Here we present evidence that posttranslational modification by SUMO enhances this synergy via an effect on Senseless protein. We show that Senseless is a direct target for SUMO modification and that mutagenesis of a predicted SUMOylation motif in Senseless reduces Senseless/proneural synergy both in vivo and in cell culture. We propose that SUMOylation of Senseless via lysine 509 promotes its synergy with proneural proteins during transcriptional activation and hence regulates an important step in neurogenesis leading to the formation and maturation of the SOPs. PMID:22586269

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

PubMed

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

Family of new operations equivalency of neuro-fuzzy logic: optoelectronic realization and applications

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Yatskovsky, Victor I.; Ogorodnik, K. V.; Lischenko, Sergey

2002-07-01

The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 2.5 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 - 20 thousands. The base operations in EMs are normalized equivalency operations. The family of new operations equivalency and non-equivalency of neuro-fuzzy logic's, which we have elaborated on the based of such generalized operations of fuzzy-logic's as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) equivalency which uses relations of t-norm and s-norm to fuzzy negation are proposed. Among these elements the following should be underlined: (1) the element which fulfills the operation of limited difference; (2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); (3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations equivalency are shown. These realization on the basic of new multifunctional optoelectronical BISPIN- devices (MOEBD) represent the circuit with constant and pulse optical input signals. They are modeling the operation of limited difference. These circuits realize frequency- dynamic neuron models and neural networks. Experimental results of these MOEBD and equivalency circuits, which fulfill the limited difference operation are discussed. For effective realization of neural networks on the basic of EMs as it is shown in report, picture elements are required as main nodes to implement element operations equivalence ('non-equivalence') of neuro-fuzzy logic's.

Emotional Intelligence predicts individual differences in social exchange reasoning.

PubMed

Reis, Deidre L; Brackett, Marc A; Shamosh, Noah A; Kiehl, Kent A; Salovey, Peter; Gray, Jeremy R

2007-04-15

When assessed with performance measures, Emotional Intelligence (EI) correlates positively with the quality of social relationships. However, the bases of such correlations are not understood in terms of cognitive and neural information processing mechanisms. We investigated whether a performance measure of EI is related to reasoning about social situations (specifically social exchange reasoning) using versions of the Wason Card Selection Task. In an fMRI study (N=16), higher EI predicted hemodynamic responses during social reasoning in the left frontal polar and left anterior temporal brain regions, even when controlling for responses on a very closely matched task (precautionary reasoning). In a larger behavioral study (N=48), higher EI predicted faster social exchange reasoning, after controlling for precautionary reasoning. The results are the first to directly suggest that EI is mediated in part by mechanisms supporting social reasoning and validate a new approach to investigating EI in terms of more basic information processing mechanisms.

Postdoctoral Fellow | Center for Cancer Research

Cancer.gov

The Neural Development Section (NDS) headed by Dr. Lino Tessarollo has an open postdoctoral fellow position. The candidate should have a background in neurobiology and basic expertise in molecular biology, cell biology, immunoistochemistry and biochemistry.  Experience in confocal analysis is desired. The NDS study the biology of neurotrophin and Trk receptors function by using both in vitro and in vivo approaches. Our group makes extensive use of engineered mouse models and cell culture models. The current research emphasis is on understanding the molecular mechanisms by which activated trk receptor function. Specifically, we are dissecting the molecular mechanism responsible for modulating Trk receptors activity, including their interaction with specific scaffold proteins and proteins leading to de-activation of Trk signaling. Moreover, we are attempting to identify new signaling pathways activated by truncated Trk receptors.

Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use.

PubMed

Harlé, Katia M; Stewart, Jennifer L; Zhang, Shunan; Tapert, Susan F; Yu, Angela J; Paulus, Martin P

2015-11-01

Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Neural mechanisms of single corrective steps evoked in the standing rabbit

PubMed Central

Hsu, L.-J.; Zelenin, P. V.; Lyalka, V. F.; Vemula, M. G.; Orlovsky, G. N.; Deliagina, T. G.

2017-01-01

Single steps in different directions are often used for postural corrections. However, our knowledge about the neural mechanisms underlying their generation is scarce. This study was aimed to characterize the corrective steps generated in response to disturbances of the basic body configuration caused by forward, backward or outward displacement of the hindlimb, as well as to reveal location in the CNS of the corrective step generating mechanisms. Video recording of the motor response to translation of the supporting surface under the hindlimb along with contact forces and activity of back and limb muscles was performed in freely standing intact and in fixed postmammillary rabbits. In intact rabbits, displacement of the hindlimb in any direction caused a lateral trunk movement towards the contralateral hindlimb, and then a corrective step in the direction opposite to the initial displacement. The time difference between onsets of these two events varied considerably. The EMG pattern in the supporting hindlimb was similar for all directions of corrective steps. It caused the increase in the limb stiffness. EMG pattern in the stepping limb differed in steps with different directions. In postmammillary rabbits the corrective stepping movements, as well as EMG patterns in both stepping and standing hindlimbs were similar to those observed in intact rabbits. This study demonstrates that the corrective trunk and limb movements are generated by separate mechanisms activated by sensory signals from the deviated limb. The neuronal networks generating postural corrective steps reside in the brainstem, cerebellum, and spinal cord. PMID:28215990

The status and future of acupuncture mechanism research.

PubMed

Napadow, Vitaly; Ahn, Andrew; Longhurst, John; Lao, Lixing; Stener-Victorin, Elisabet; Harris, Richard; Langevin, Helene M

2008-09-01

On November 8-9, 2007, the Society for Acupuncture Research (SAR) hosted an international conference to mark the tenth anniversary of the landmark NIH [National Institutes of Health] Consensus Development Conference on Acupuncture. More than 300 acupuncture researchers, practitioners, students, funding agency personnel, and health policy analysts from 20 countries attended the SAR meeting held at the University of Maryland School of Medicine, Baltimore, MD. This paper summarizes important invited lectures in the area of basic and translational acupuncture research. Specific areas include the scientific assessment of acupuncture points and meridians, the neural mechanisms of cardiovascular regulation by acupuncture, mechanisms for electroacupuncture applied to persistent inflammation and pain, basic and translational research on acupuncture in gynecologic applications, the application of functional neuroimaging to acupuncture research with specific application to carpal-tunnel syndrome and fibromyalgia, and the association of the connective tissue system to acupuncture research. In summary, mechanistic models for acupuncture effects that have been investigated experimentally have focused on the effects of acupuncture needle stimulation on the nervous system, muscles, and connective tissue. These mechanistic models are not mutually exclusive. Iterative testing, expanding, and perhaps merging of such models will potentially lead to an incremental understanding of the effects of manual and electrical stimulation of acupuncture needles that is solidly rooted in physiology.

Grasping actions and social interaction: neural bases and anatomical circuitry in the monkey

PubMed Central

Rozzi, Stefano; Coudé, Gino

2015-01-01

The study of the neural mechanisms underlying grasping actions showed that cognitive functions are deeply embedded in motor organization. In the first part of this review, we describe the anatomical structure of the motor cortex in the monkey and the cortical and sub-cortical connections of the different motor areas. In the second part, we review the neurophysiological literature showing that motor neurons are not only involved in movement execution, but also in the transformation of object physical features into motor programs appropriate to grasp them (through visuo-motor transformations). We also discuss evidence indicating that motor neurons can encode the goal of motor acts and the intention behind action execution. Then, we describe one of the mechanisms—the mirror mechanism—considered to be at the basis of action understanding and intention reading, and describe the anatomo-functional pathways through which information about the social context can reach the areas containing mirror neurons. Finally, we briefly show that a clear similarity exists between monkey and human in the organization of the motor and mirror systems. Based on monkey and human literature, we conclude that the mirror mechanism relies on a more extended network than previously thought, and possibly subserves basic social functions. We propose that this mechanism is also involved in preparing appropriate complementary response to observed actions, allowing two individuals to become attuned and cooperate in joint actions. PMID:26236258

Abnormal late visual responses and alpha oscillations in neurofibromatosis type 1: a link to visual and attention deficits

PubMed Central

2014-01-01

Background Neurofibromatosis type 1 (NF1) affects several areas of cognitive function including visual processing and attention. We investigated the neural mechanisms underlying the visual deficits of children and adolescents with NF1 by studying visual evoked potentials (VEPs) and brain oscillations during visual stimulation and rest periods. Methods Electroencephalogram/event-related potential (EEG/ERP) responses were measured during visual processing (NF1 n = 17; controls n = 19) and idle periods with eyes closed and eyes open (NF1 n = 12; controls n = 14). Visual stimulation was chosen to bias activation of the three detection mechanisms: achromatic, red-green and blue-yellow. Results We found significant differences between the groups for late chromatic VEPs and a specific enhancement in the amplitude of the parieto-occipital alpha amplitude both during visual stimulation and idle periods. Alpha modulation and the negative influence of alpha oscillations in visual performance were found in both groups. Conclusions Our findings suggest abnormal later stages of visual processing and enhanced amplitude of alpha oscillations supporting the existence of deficits in basic sensory processing in NF1. Given the link between alpha oscillations, visual perception and attention, these results indicate a neural mechanism that might underlie the visual sensitivity deficits and increased lapses of attention observed in individuals with NF1. PMID:24559228

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

PubMed

Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-07-20

Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.

PubMed

Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio; Latham, Peter E; Fellin, Tommaso

2017-02-08

The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information. We show how this framework leads to a new statistical analysis of neural activity recorded during behavior that can identify such neural codes, and we discuss how to combine intersection-based analysis of neural recordings with intervention on neural activity to determine definitively whether specific neural activity features are involved in a task. Copyright © 2017 Elsevier Inc. All rights reserved.

Shindigs, brunches, and rodeos: the neural basis of event words.

PubMed

Bedny, Marina; Dravida, Swethasri; Saxe, Rebecca

2014-09-01

Events (e.g., "running" or "eating") constitute a basic type within human cognition and human language. We asked whether thinking about events, as compared to other conceptual categories, depends on partially independent neural circuits. Indirect evidence for this hypothesis comes from previous studies showing elevated posterior temporal responses to verbs, which typically label events. Neural responses to verbs could, however, be driven either by their grammatical or by their semantic properties. In the present experiment, we separated the effects of grammatical class (verb vs. noun) and semantic category (event vs. object) by measuring neural responses to event nouns (e.g., "the hurricane"). Participants rated the semantic relatedness of event nouns, as well as of two categories of object nouns-animals (e.g., "the alligator") and plants (e.g., "the acorn")-and three categories of verbs-manner of motion (e.g., "to roll"), emission (e.g., "to sparkle"), and perception (e.g., "to gaze"). As has previously been observed, we found larger responses to verbs than to object nouns in the left posterior middle (LMTG) and superior (LSTG) temporal gyri. Crucially, we also found that the LMTG responds more to event than to object nouns. These data suggest that part of the posterior lateral temporal response to verbs is driven by their semantic properties. By contrast, a more superior region, at the junction of the temporal and parietal cortices, responded more to verbs than to all nouns, irrespective of their semantic category. We concluded that the neural mechanisms engaged when thinking about event and object categories are partially dissociable.

Neural substrates of trait ruminations in depression

PubMed Central

Mandell, Darcy; Siegle, Greg; Shutt, Luann; Feldmiller, Josh; Thase, Michael E.

2014-01-01

Rumination in depression is a risk factor for longer, more intense, and harder-to-treat depressions. But there appear to be multiple types of depressive rumination – whether they all share these vulnerability mechanisms, and thus would benefit from the same types of clinical attention is unclear. In the current study, we examined neural correlates of empirically-derived dimensions of trait rumination in 35 depressed participants. These individuals and 29 never-depressed controls completed 17 self-report measures of rumination and an alternating emotion-processing/executive-control task during functional magnetic resonance imaging (fMRI) assessment. We examined associations of regions of interest—the amygdala and other cortical regions subserving a potential role in deficient cognitive control and elaborative emotion-processing—with trait rumination. Rumination of all types was generally associated with increased sustained amygdala reactivity. When controlling for amygdala reactivity, distinct activity patterns in hippocampus were also associated with specific dimensions of rumination. We discuss the possibly utility of targeting more basic biological substrates of emotional reactivity in depressed patients who frequently ruminate. PMID:24661157

Twitching in Sensorimotor Development from Sleeping Rats to Robots

PubMed Central

Marques, Hugo Gravato; Iida, Fumiya

2013-01-01

It is still not known how the “rudimentary” movements of fetuses and infants are transformed into the coordinated, flexible, and adaptive movements of adults. In addressing this important issue, we consider a behavior that has been perennially viewed as a functionless by-product of a dreaming brain: the jerky limb movements called myoclonic twitches. Recent work has identified the neural mechanisms that produce twitching as well as those that convey sensory feedback from twitching limbs to the spinal cord and brain. In turn, these mechanistic insights have helped inspire new ideas about the functional roles that twitching might play in the self-organization of spinal and supraspinal sensorimotor circuits. Striking support for these ideas is coming from the field of developmental robotics: When twitches are mimicked in robot models of the musculoskeletal system, basic neural circuitry self-organizes. Mutually inspired biological and synthetic approaches promise not only to produce better robots, but also to solve fundamental problems concerning the developmental origins of sensorimotor maps in the spinal cord and brain. PMID:23787051

The neural network to determine the mechanical properties of the steels

NASA Astrophysics Data System (ADS)

Yemelyanov, Vitaliy; Yemelyanova, Nataliya; Safonova, Marina; Nedelkin, Aleksey

2018-04-01

The authors describe the neural network structure and software that is designed and developed to determine the mechanical properties of steels. The neural network is developed to refine upon the values of the steels properties. The results of simulations of the developed neural network are shown. The authors note the low standard error of the proposed neural network. To realize the proposed neural network the specialized software has been developed.

Dynamical foundations of the neural circuit for bayesian decision making.

PubMed

Morita, Kenji

2009-07-01

On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

Discrete Neural Correlates for the Recognition of Negative Emotions: Insights from Frontotemporal Dementia

PubMed Central

Kumfor, Fiona; Irish, Muireann; Hodges, John R.; Piguet, Olivier

2013-01-01

Patients with frontotemporal dementia have pervasive changes in emotion recognition and social cognition, yet the neural changes underlying these emotion processing deficits remain unclear. The multimodal system model of emotion proposes that basic emotions are dependent on distinct brain regions, which undergo significant pathological changes in frontotemporal dementia. As such, this syndrome may provide important insight into the impact of neural network degeneration upon the innate ability to recognise emotions. This study used voxel-based morphometry to identify discrete neural correlates involved in the recognition of basic emotions (anger, disgust, fear, sadness, surprise and happiness) in frontotemporal dementia. Forty frontotemporal dementia patients (18 behavioural-variant, 11 semantic dementia, 11 progressive nonfluent aphasia) and 27 healthy controls were tested on two facial emotion recognition tasks: The Ekman 60 and Ekman Caricatures. Although each frontotemporal dementia group showed impaired recognition of negative emotions, distinct associations between emotion-specific task performance and changes in grey matter intensity emerged. Fear recognition was associated with the right amygdala; disgust recognition with the left insula; anger recognition with the left middle and superior temporal gyrus; and sadness recognition with the left subcallosal cingulate, indicating that discrete neural substrates are necessary for emotion recognition in frontotemporal dementia. The erosion of emotion-specific neural networks in neurodegenerative disorders may produce distinct profiles of performance that are relevant to understanding the neurobiological basis of emotion processing. PMID:23805313

Potential involvement of kinesin-1 in the regulation of subcellular localization of Girdin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muramatsu, Aya; Enomoto, Atsushi, E-mail: enomoto@iar.nagoya-u.ac.jp; Kato, Takuya

Girdin is an actin-binding protein that has multiple functions in postnatal neural development and cancer progression. We previously showed that Girdin is a regulator of migration for neuroblasts born from neural stem cells in the subventricular zone (SVZ) and the dentate gyrus of the hippocampus in the postnatal brain. Despite a growing list of Girdin-interacting proteins, the mechanism of Girdin-mediated migration has not been fully elucidated. Girdin interacts with Disrupted-In-Schizophrenia 1 and partitioning-defective 3, both of which have been shown to interact with the kinesin microtubule motor proteins. Based on this, we have identified that Girdin also interacts with kinesin-1,more » a member of neuronal kinesin proteins. Although a direct interaction of Girdin and kinesin-1 has not been determined, it is of interest to find that Girdin loss-of-function mutant mice with the mutation of a basic amino acid residue-rich region (Basic mut mice) exhibit limited interaction with kinesin-1. Furthermore, expression of a kinesin-1 mutant with motor defects, leads to Girdin mislocalization. Finally, consistent with previous studies on the role of kinesin proteins in trafficking a cell–cell adhesion molecule N-cadherin, Basic mut mice showed an aberrant expression pattern of N-cadherin in migrating SVZ neuroblasts. These findings suggest a potential role of Girdin/kinesin-1 interaction in the regulation of neuroblast migration in the postnatal brain. - Highlights: • Girdin is a regulator of migration for neuroblasts in the postnatal brain. • Girdin interacts with kinesin-1, a member of neuronal kinesin proteins. • Girdin mutant mice showed an aberrant expression of N-cadherin in neuroblasts.« less

Effects of leukemia inhibitory factor and basic fibroblast growth factor on free radicals and endogenous stem cell proliferation in a mouse model of cerebral infarction.

PubMed

Huang, Weihui; Li, Yadan; Lin, Yufeng; Ye, Xue; Zang, Dawei

2012-07-05

The present study established a mouse model of cerebral infarction by middle cerebral artery occlusion, and monitored the effect of 25 μg/kg leukemia inhibitory factor and (or) basic fibroblast growth factor administration 2 hours after model establishment. Results showed that following administration, the number of endogenous neural stem cells in the infarct area significantly increased, malondialdehyde content in brain tissue homogenates significantly decreased, nitric oxide content, glutathione peroxidase and superoxide dismutase activity significantly elevated, and mouse motor function significantly improved as confirmed by the rotarod and bar grab tests. In particular, the effect of leukemia inhibitory factor in combination with basic fibroblast growth factor was the most significant. Results indicate that leukemia inhibitory factor and basic fibroblast growth factor can improve the microenvironment after cerebral infarction by altering free radical levels, improving the quantity of endogenous neural stem cells, and promoting neurological function of mice with cerebral infarction.

The science of neural interface systems.

PubMed

Hatsopoulos, Nicholas G; Donoghue, John P

2009-01-01

The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.

Mirror neuron system: basic findings and clinical applications.

PubMed

Iacoboni, Marco; Mazziotta, John C

2007-09-01

In primates, ventral premotor and rostral inferior parietal neurons fire during the execution of hand and mouth actions. Some cells (called mirror neurons) also fire when hand and mouth actions are just observed. Mirror neurons provide a simple neural mechanism for understanding the actions of others. In humans, posterior inferior frontal and rostral inferior parietal areas have mirror properties. These human areas are relevant to imitative learning and social behavior. Indeed, the socially isolating condition of autism is associated with a deficit in mirror neuron areas. Strategies inspired by mirror neuron research recently have been used in the treatment of autism and in motor rehabilitation after stroke.

Stochastic architecture for Hopfield neural nets

NASA Technical Reports Server (NTRS)

Pavel, Sandy

1992-01-01

An expandable stochastic digital architecture for recurrent (Hopfield like) neural networks is proposed. The main features and basic principles of stochastic processing are presented. The stochastic digital architecture is based on a chip with n full interconnected neurons with a pipeline, bit processing structure. For large applications, a flexible way to interconnect many such chips is provided.

Decreased Connectivity and Cerebellar Activity in Autism during Motor Task Performance

ERIC Educational Resources Information Center

Mostofsky, Stewart H.; Powell, Stephanie K.; Simmonds, Daniel J.; Goldberg, Melissa C.; Caffo, Brian; Pekar, James J.

2009-01-01

Although motor deficits are common in autism, the neural correlates underlying the disruption of even basic motor execution are unknown. Motor deficits may be some of the earliest identifiable signs of abnormal development and increased understanding of their neural underpinnings may provide insight into autism-associated differences in parallel…

Comparison of Intelligent Systems in Detecting a Child's Mathematical Gift

ERIC Educational Resources Information Center

Pavlekovic, Margita; Zekic-Susac, Marijana; Djurdjevic, Ivana

2009-01-01

This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children's mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child's mathematical gift…

Reading for Meaning in Dyslexic and Young Children: Distinct Neural Pathways but Common Endpoints

ERIC Educational Resources Information Center

Schulz, Enrico; Maurer, Urs; van der Mark, Sanne; Bucher, Kerstin; Brem, Silvia; Martin, Ernst; Brandeis, Daniel

2009-01-01

Developmental dyslexia is a highly prevalent and specific disorder of reading acquisition characterised by impaired reading fluency and comprehension. We have previously identified fMRI- and ERP-based neural markers of impaired sentence reading in dyslexia that indicated both deviant basic word processing and deviant semantic incongruency…

Neurophysiology and Neuroanatomy of Reflexive and Volitional Saccades: Evidence from Studies of Humans

ERIC Educational Resources Information Center

McDowell, Jennifer E.; Dyckman, Kara A.; Austin, Benjamin P.; Clementz, Brett A.

2008-01-01

This review provides a summary of the contributions made by human functional neuroimaging studies to the understanding of neural correlates of saccadic control. The generation of simple visually guided saccades (redirections of gaze to a visual stimulus or pro-saccades) and more complex volitional saccades require similar basic neural circuitry…

Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Basic aerodynamic coefficients are modeled as functions of angles of attack and sideslip with vehicle lateral symmetry and compressibility effects. Most of the aerodynamic parameters can be well-fitted using polynomial functions. In this paper a fast, reliable way of predicting aerodynamic coefficients is produced using a neural network. The training data for the neural network is derived from wind tunnel test and numerical simulations. The coefficients of lift, drag, pitching moment are expressed as a function of alpha (angle of attack) and Mach number. The results produced from preliminary neural network analysis are very good.

Program Helps Simulate Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Cognitive Control Mechanisms, Emotion & Memory: A neural perspective with implications for psychopathology

PubMed Central

Banich, Marie T.; Mackiewicz, Kristen L.; Depue, Brendan E.; Whitmer, Anson; Miller, Gregory A.; Heller, Wendy

2009-01-01

In this paper we provide a focused review of the literature examining neural mechanisms involved in cognitive control over memory processes that can influence, and in turn are influenced, by emotional processes. The review is divided into two parts, the first focusing on working memory and the second on long-term memory. With regard to working memory, we discuss the neural bases of 1) control mechanisms that can select against distracting emotional information, 2) mechanisms that can regulate emotional reactions or responses, 3) how mood state influences cognitive control, and 4) individual differences in control mechanisms. For long-term memory, we briefly review 1) the neural substrates of emotional memory, 2) the cognitive and neural mechanisms that are involved in controlling emotional memories and 3) how these systems are altered in post-traumatic stress disorder. Finally, we consider tentative generalizations that can be drawn from this relatively unexplored conjunction of research endeavors. PMID:18948135

A freely-moving monkey treadmill model

NASA Astrophysics Data System (ADS)

Foster, Justin D.; Nuyujukian, Paul; Freifeld, Oren; Gao, Hua; Walker, Ross; Ryu, Stephen I.; Meng, Teresa H.; Murmann, Boris; Black, Michael J.; Shenoy, Krishna V.

2014-08-01

Objective. Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. Approach. We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Main results. Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Significance. Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

Brain Response to a Humanoid Robot in Areas Implicated in the Perception of Human Emotional Gestures

PubMed Central

Chaminade, Thierry; Zecca, Massimiliano; Blakemore, Sarah-Jayne; Takanishi, Atsuo; Frith, Chris D.; Micera, Silvestro; Dario, Paolo; Rizzolatti, Giacomo; Gallese, Vittorio; Umiltà, Maria Alessandra

2010-01-01

Background The humanoid robot WE4-RII was designed to express human emotions in order to improve human-robot interaction. We can read the emotions depicted in its gestures, yet might utilize different neural processes than those used for reading the emotions in human agents. Methodology Here, fMRI was used to assess how brain areas activated by the perception of human basic emotions (facial expression of Anger, Joy, Disgust) and silent speech respond to a humanoid robot impersonating the same emotions, while participants were instructed to attend either to the emotion or to the motion depicted. Principal Findings Increased responses to robot compared to human stimuli in the occipital and posterior temporal cortices suggest additional visual processing when perceiving a mechanical anthropomorphic agent. In contrast, activity in cortical areas endowed with mirror properties, like left Broca's area for the perception of speech, and in the processing of emotions like the left anterior insula for the perception of disgust and the orbitofrontal cortex for the perception of anger, is reduced for robot stimuli, suggesting lesser resonance with the mechanical agent. Finally, instructions to explicitly attend to the emotion significantly increased response to robot, but not human facial expressions in the anterior part of the left inferior frontal gyrus, a neural marker of motor resonance. Conclusions Motor resonance towards a humanoid robot, but not a human, display of facial emotion is increased when attention is directed towards judging emotions. Significance Artificial agents can be used to assess how factors like anthropomorphism affect neural response to the perception of human actions. PMID:20657777

Olfactory regulation of the sexual behavior and reproductive physiology of the laboratory mouse: effects and neural mechanisms.

PubMed

Kelliher, Kevin R; Wersinger, Scott R

2009-01-01

In many species, chemical compounds emitted by conspecifics exert profound effects on reproductive physiology and sexual behavior. This is particularly true in the mouse, where such cues advance and delay puberty, suppress and facilitate estrous cycles, and cause the early termination of pregnancy. They also facilitate sexual behavior and inform mate selection. The mouse has a rich and complex repertoire of social behaviors. The technologies of molecular genetics are well developed in the mouse. Gene expression can be experimentally manipulated in the mouse relatively easily and in a time- and tissue-specific manner. Thus, the mouse is an excellent model in which to investigate the genetic, neural, and hormonal bases by which chemical compounds released by other mice affect physiology and behavior. These chemical cues are detected and processed by the olfactory system and other specialized but less well characterized sensory organs. The sensory information reaches brain regions that regulate hormone levels as well as those that are involved in behavior and alters the function of these brain regions. The effects of these chemical compounds have important implications for the laboratory animal facility as well as for researchers. We begin with an overview of the basic structure and function of the olfactory system and of the connections among brain regions that receive olfactory stimuli. We discuss the effects of chemosensory cues on the behavior and physiology of the organism along with what is known about the neural and hormonal mechanisms underlying these effects. We also describe some of the implications for the laboratory animal facility.

Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

NASA Technical Reports Server (NTRS)

Fusaro, Robert L.

1998-01-01

Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

Artificial neural network in cosmic landscape

NASA Astrophysics Data System (ADS)

Liu, Junyu

2017-12-01

In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.

A critical review on the applications of artificial neural networks in winemaking technology.

PubMed

Moldes, O A; Mejuto, J C; Rial-Otero, R; Simal-Gandara, J

2017-09-02

Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.

Brain Representations of Basic Physics Concepts

NASA Astrophysics Data System (ADS)

Just, Marcel Adam

2017-09-01

The findings concerning physics concepts build on the remarkable new ability to determine the neural signature (or activation pattern) corresponding to an individual concept using fMRI brain imaging. Moreover, the neural signatures can be decomposed into meaningful underlying dimensions, identifying the individual, interpretable components of the neural representation of a concept. The investigation of physics concepts representations reveals how relatively recent physics concepts (formalized only in the last few centuries) are stored in the millenia-old information system of the human brain.

Mechanical roles of apical constriction, cell elongation, and cell migration during neural tube formation in Xenopus.

PubMed

Inoue, Yasuhiro; Suzuki, Makoto; Watanabe, Tadashi; Yasue, Naoko; Tateo, Itsuki; Adachi, Taiji; Ueno, Naoto

2016-12-01

Neural tube closure is an important and necessary process during the development of the central nervous system. The formation of the neural tube structure from a flat sheet of neural epithelium requires several cell morphogenetic events and tissue dynamics to account for the mechanics of tissue deformation. Cell elongation changes cuboidal cells into columnar cells, and apical constriction then causes them to adopt apically narrow, wedge-like shapes. In addition, the neural plate in Xenopus is stratified, and the non-neural cells in the deep layer (deep cells) pull the overlying superficial cells, eventually bringing the two layers of cells to the midline. Thus, neural tube closure appears to be a complex event in which these three physical events are considered to play key mechanical roles. To test whether these three physical events are mechanically sufficient to drive neural tube formation, we employed a three-dimensional vertex model and used it to simulate the process of neural tube closure. The results suggest that apical constriction cued the bending of the neural plate by pursing the circumference of the apical surface of the neural cells. Neural cell elongation in concert with apical constriction further narrowed the apical surface of the cells and drove the rapid folding of the neural plate, but was insufficient for complete neural tube closure. Migration of the deep cells provided the additional tissue deformation necessary for closure. To validate the model, apical constriction and cell elongation were inhibited in Xenopus laevis embryos. The resulting cell and tissue shapes resembled the corresponding simulation results.

Eye surface temperature detects stress response in budgerigars (Melopsittacus undulatus).

PubMed

Ikkatai, Yuko; Watanabe, Shigeru

2015-08-05

Previous studies have suggested that stressors not only increase body core temperature but also body surface temperature in many animals. However, it remains unclear whether surface temperature could be used as an alternative to directly measure body core temperature, particularly in birds. We investigated whether surface temperature is perceived as a stress response in budgerigars. Budgerigars have been used as popular animal models to investigate various neural mechanisms such as visual perception, vocal learning, and imitation. Developing a new technique to understand the basic physiological mechanism would help neuroscience researchers. First, we found that cloacal temperature correlated with eye surface temperature. Second, eye surface temperature increased after handling stress. Our findings suggest that eye surface temperature is closely related to cloacal temperature and that the stress response can be measured by eye surface temperature in budgerigars.

Acoustic and higher-level representations of naturalistic auditory scenes in human auditory and frontal cortex.

PubMed

Hausfeld, Lars; Riecke, Lars; Formisano, Elia

2018-06-01

Often, in everyday life, we encounter auditory scenes comprising multiple simultaneous sounds and succeed to selectively attend to only one sound, typically the most relevant for ongoing behavior. Studies using basic sounds and two-talker stimuli have shown that auditory selective attention aids this by enhancing the neural representations of the attended sound in auditory cortex. It remains unknown, however, whether and how this selective attention mechanism operates on representations of auditory scenes containing natural sounds of different categories. In this high-field fMRI study we presented participants with simultaneous voices and musical instruments while manipulating their focus of attention. We found an attentional enhancement of neural sound representations in temporal cortex - as defined by spatial activation patterns - at locations that depended on the attended category (i.e., voices or instruments). In contrast, we found that in frontal cortex the site of enhancement was independent of the attended category and the same regions could flexibly represent any attended sound regardless of its category. These results are relevant to elucidate the interacting mechanisms of bottom-up and top-down processing when listening to real-life scenes comprised of multiple sound categories. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Brain activity is related to individual differences in the number of items stored in auditory short-term memory for pitch: evidence from magnetoencephalography.

PubMed

Grimault, Stephan; Nolden, Sophie; Lefebvre, Christine; Vachon, François; Hyde, Krista; Peretz, Isabelle; Zatorre, Robert; Robitaille, Nicolas; Jolicoeur, Pierre

2014-07-01

We used magnetoencephalography (MEG) to examine brain activity related to the maintenance of non-verbal pitch information in auditory short-term memory (ASTM). We focused on brain activity that increased with the number of items effectively held in memory by the participants during the retention interval of an auditory memory task. We used very simple acoustic materials (i.e., pure tones that varied in pitch) that minimized activation from non-ASTM related systems. MEG revealed neural activity in frontal, temporal, and parietal cortices that increased with a greater number of items effectively held in memory by the participants during the maintenance of pitch representations in ASTM. The present results reinforce the functional role of frontal and temporal cortices in the retention of pitch information in ASTM. This is the first MEG study to provide both fine spatial localization and temporal resolution on the neural mechanisms of non-verbal ASTM for pitch in relation to individual differences in the capacity of ASTM. This research contributes to a comprehensive understanding of the mechanisms mediating the representation and maintenance of basic non-verbal auditory features in the human brain. Copyright © 2014 Elsevier Inc. All rights reserved.

Intrinsic and Extrinsic Neuromodulation of Olfactory Processing.

PubMed

Lizbinski, Kristyn M; Dacks, Andrew M

2017-01-01

Neuromodulation is a ubiquitous feature of neural systems, allowing flexible, context specific control over network dynamics. Neuromodulation was first described in invertebrate motor systems and early work established a basic dichotomy for neuromodulation as having either an intrinsic origin (i.e., neurons that participate in network coding) or an extrinsic origin (i.e., neurons from independent networks). In this conceptual dichotomy, intrinsic sources of neuromodulation provide a "memory" by adjusting network dynamics based upon previous and ongoing activation of the network itself, while extrinsic neuromodulators provide the context of ongoing activity of other neural networks. Although this dichotomy has been thoroughly considered in motor systems, it has received far less attention in sensory systems. In this review, we discuss intrinsic and extrinsic modulation in the context of olfactory processing in invertebrate and vertebrate model systems. We begin by discussing presynaptic modulation of olfactory sensory neurons by local interneurons (LNs) as a mechanism for gain control based on ongoing network activation. We then discuss the cell-class specific effects of serotonergic centrifugal neurons on olfactory processing. Finally, we briefly discuss the integration of intrinsic and extrinsic neuromodulation (metamodulation) as an effective mechanism for exerting global control over olfactory network dynamics. The heterogeneous nature of neuromodulation is a recurring theme throughout this review as the effects of both intrinsic and extrinsic modulation are generally non-uniform.

How Early Hormones Shape Gender Development

PubMed Central

Berenbaum, Sheri A.; Beltz, Adriene M.

2015-01-01

Many important psychological characteristics show sex differences, and are influenced by sex hormones at different developmental periods. We focus on the role of sex hormones in early development, particularly the differential effects of prenatal androgens on aspects of gender development. Increasing evidence confirms that prenatal androgens have facilitative effects on male-typed activity interests and engagement (including child toy preferences and adult careers), and spatial abilities, but relatively minimal effects on gender identity. Recent emphasis has been directed to the psychological mechanisms underlying these effects (including sex differences in propulsive movement, and androgen effects on interest in people versus things), and neural substrates of androgen effects (including regional brain volumes, and neural responses to mental rotation, sexually arousing stimuli, emotion, and reward). Ongoing and planned work is focused on understanding the ways in which hormones act jointly with the social environment across time to produce varying trajectories of gender development, and clarifying mechanisms by which androgens affect behaviors. Such work will be facilitated by applying lessons from other species, and by expanding methodology. Understanding hormonal influences on gender development enhances knowledge of psychological development generally, and has important implications for basic and applied questions, including sex differences in psychopathology, women’s underrepresentation in science and math, and clinical care of individuals with variations in gender expression. PMID:26688827

Brain-machine interfaces: electrophysiological challenges and limitations.

PubMed

Lega, Bradley C; Serruya, Mijail D; Zaghloul, Kareem A

2011-01-01

Brain-machine interfaces (BMI) seek to directly communicate with the human nervous system in order to diagnose and treat intrinsic neurological disorders. While the first generation of these devices has realized significant clinical successes, they often rely on gross electrical stimulation using empirically derived parameters through open-loop mechanisms of action that are not yet fully understood. Their limitations reflect the inherent challenge in developing the next generation of these devices. This review identifies lessons learned from the first generation of BMI devices (chiefly deep brain stimulation), identifying key problems for which the solutions will aid the development of the next generation of technologies. Our analysis examines four hypotheses for the mechanism by which brain stimulation alters surrounding neurophysiologic activity. We then focus on motor prosthetics, describing various approaches to overcoming the problems of decoding neural signals. We next turn to visual prosthetics, an area for which the challenges of signal coding to match neural architecture has been partially overcome. Finally, we close with a review of cortical stimulation, examining basic principles that will be incorporated into the design of future devices. Throughout the review, we relate the issues of each specific topic to the common thread of BMI research: translating new knowledge of network neuroscience into improved devices for neuromodulation.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

PubMed Central

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

A connectionist model of category learning by individuals with high-functioning autism spectrum disorder.

PubMed

Dovgopoly, Alexander; Mercado, Eduardo

2013-06-01

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

Role of Dlx6 in regulation of an endothelin-1-dependent, dHAND branchial arch enhancer

PubMed Central

Charité, Jeroen; McFadden, David G.; Merlo, Giorgio; Levi, Giovanni; Clouthier, David E.; Yanagisawa, Masashi; Richardson, James A.; Olson, Eric N.

2001-01-01

Neural crest cells play a key role in craniofacial development. The endothelin family of secreted polypeptides regulates development of several neural crest sublineages, including the branchial arch neural crest. The basic helix–loop–helix transcription factor dHAND is also required for craniofacial development, and in endothelin-1 (ET-1) mutant embryos, dHAND expression in the branchial arches is down-regulated, implicating it as a transcriptional effector of ET-1 action. To determine the mechanism that links ET-1 signaling to dHAND transcription, we analyzed the dHAND gene for cis-regulatory elements that control transcription in the branchial arches. We describe an evolutionarily conserved dHAND enhancer that requires ET-1 signaling for activity. This enhancer contains four homeodomain binding sites that are required for branchial arch expression. By comparing protein binding to these sites in branchial arch extracts from endothelin receptor A (EdnrA) mutant and wild-type mouse embryos, we identified Dlx6, a member of the Distal-less family of homeodomain proteins, as an ET-1-dependent binding factor. Consistent with this conclusion, Dlx6 was down-regulated in branchial arches from EdnrA mutant mice. These results suggest that Dlx6 acts as an intermediary between ET-1 signaling and dHAND transcription during craniofacial morphogenesis. PMID:11711438

Platelet-rich plasma for regeneration of neural feedback pathways around dental implants: a concise review and outlook on future possibilities

PubMed Central

Huang, Yan; Bornstein, Michael M; Lambrichts, Ivo; Yu, Hai-Yang; Politis, Constantinus; Jacobs, Reinhilde

2017-01-01

Along with the development of new materials, advanced medical imaging and surgical techniques, osseointegrated dental implants are considered a successful and constantly evolving treatment modality for the replacement of missing teeth in patients with complete or partial edentulism. The importance of restoring the peripheral neural feedback pathway and thus repairing the lack of periodontal mechanoreceptors after tooth extraction has been highlighted in the literature. Nevertheless, regenerating the nerve fibers and reconstructing the neural feedback pathways around osseointegrated implants remain a challenge. Recent studies have provided evidence that platelet-rich plasma (PRP) therapy is a promising treatment for musculoskeletal injuries. Because of its high biological safety, convenience and usability, PRP therapy has gradually gained popularity in the clinical field. Although much remains to be learned, the growth factors from PRP might play key roles in peripheral nerve repair mechanisms. This review presents known growth factors contributing to the biological efficacy of PRP and illustrates basic and (pre-)clinical evidence regarding the use of PRP and its relevant products in peripheral nerve regeneration. In addition, the potential of local application of PRP for structural and functional recovery of injured peripheral nerves around dental implants is discussed. PMID:28282030

Dividing the Self: Distinct Neural Substrates of Task-Based and Automatic Self-Prioritization after Brain Damage

ERIC Educational Resources Information Center

Sui, Jie; Chechlacz, Magdalena; Humphreys, Glyn W.

2012-01-01

Facial self-awareness is a basic human ability dependent on a distributed bilateral neural network and revealed through prioritized processing of our own over other faces. Using non-prosopagnosic patients we show, for the first time, that facial self-awareness can be fractionated into different component processes. Patients performed two face…

Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation after Brain Damage

ERIC Educational Resources Information Center

Kleim, Jeffrey A.; Jones, Theresa A.

2008-01-01

Purpose: This paper reviews 10 principles of experience-dependent neural plasticity and considerations in applying them to the damaged brain. Method: Neuroscience research using a variety of models of learning, neurological disease, and trauma are reviewed from the perspective of basic neuroscientists but in a manner intended to be useful for the…

Swallowing and Dysphagia Rehabilitation: Translating Principles of Neural Plasticity into Clinically Oriented Evidence

ERIC Educational Resources Information Center

Robbins, JoAnne; Butler, Susan G.; Daniels, Stephanie K.; Gross, Roxann Diez; Langmore, Susan; Lazarus, Cathy L.; Martin-Harris, Bonnie; McCabe, Daniel; Musson, Nan; Rosenbek, John

2008-01-01

Purpose: This review presents the state of swallowing rehabilitation science as it relates to evidence for neural plastic changes in the brain. The case is made for essential collaboration between clinical and basic scientists to expand the positive influences of dysphagia rehabilitation in synergy with growth in technology and knowledge. The…

A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network.

PubMed

Amiri, Masoud; Amiri, Mahmood; Nazari, Soheila; Faez, Karim

2016-12-07

Hyper-synchronous neural oscillations are the character of several neurological diseases such as epilepsy. On the other hand, glial cells and particularly astrocytes can influence neural synchronization. Therefore, based on the recent researches, a new bio-inspired stimulator is proposed which basically is a dynamical model of the astrocyte biophysical model. The performance of the new stimulator is investigated on a large-scale, cortical network. Both excitatory and inhibitory synapses are also considered in the simulated spiking neural network. The simulation results show that the new stimulator has a good performance and is able to reduce recurrent abnormal excitability which in turn avoids the hyper-synchronous neural firing in the spiking neural network. In this way, the proposed stimulator has a demand controlled characteristic and is a good candidate for deep brain stimulation (DBS) technique to successfully suppress the neural hyper-synchronization. Copyright © 2016 Elsevier Ltd. All rights reserved.

Calculation of precise firing statistics in a neural network model

NASA Astrophysics Data System (ADS)

Cho, Myoung Won

2017-08-01

A precise prediction of neural firing dynamics is requisite to understand the function of and the learning process in a biological neural network which works depending on exact spike timings. Basically, the prediction of firing statistics is a delicate manybody problem because the firing probability of a neuron at a time is determined by the summation over all effects from past firing states. A neural network model with the Feynman path integral formulation is recently introduced. In this paper, we present several methods to calculate firing statistics in the model. We apply the methods to some cases and compare the theoretical predictions with simulation results.

The neurobiology of abnormal manifestations of aggression--a review of hypothalamic mechanisms in cats, rodents, and humans.

PubMed

Haller, Jozsef

2013-04-01

Aggression research was for long dominated by the assumption that aggression-related psychopathologies result from the excessive activation of aggression-promoting brain mechanisms. This assumption was recently challenged by findings with models of aggression that mimic etiological factors of aggression-related psychopathologies. Subjects submitted to such procedures show abnormal attack features (mismatch between provocation and response, disregard of species-specific rules, and insensitivity toward the social signals of opponents). We review here 12 such laboratory models and the available human findings on the neural background of abnormal aggression. We focus on the hypothalamus, a region tightly involved in the execution of attacks. Data show that the hypothalamic mechanisms controlling attacks (general activation levels, local serotonin, vasopressin, substance P, glutamate, GABA, and dopamine neurotransmission) undergo etiological factor-dependent changes. Findings suggest that the emotional component of attacks differentiates two basic types of hypothalamic mechanisms. Aggression associated with increased arousal (emotional/reactive aggression) is paralleled by increased mediobasal hypothalamic activation, increased hypothalamic vasopressinergic, but diminished hypothalamic serotonergic neurotransmission. In aggression models associated with low arousal (unemotional/proactive aggression), the lateral but not the mediobasal hypothalamus is over-activated. In addition, the anti-aggressive effect of serotonergic neurotransmission is lost and paradoxical changes were noticed in vasopressinergic neurotransmission. We conclude that there is no single 'neurobiological road' to abnormal aggression: the neural background shows qualitative, etiological factor-dependent differences. Findings obtained with different models should be viewed as alternative mechanisms rather than conflicting data. The relevance of these findings for understanding and treating of aggression-related psychopathologies is discussed. This article is part of a Special Issue entitled 'Extrasynaptic ionotropic receptors'. Copyright © 2012 Elsevier Inc. All rights reserved.

Insights into Metabolic Mechanisms Underlying Folate-Responsive Neural Tube Defects: A Minireview

PubMed Central

Beaudin, Anna E.; Stover, Patrick J.

2015-01-01

Neural tube defects (NTDs), including anencephaly and spina bifida, arise from the failure of neurulation during early embryonic development. Neural tube defects are common birth defects with a heterogenous and multifactorial etiology with interacting genetic and environmental risk factors. Although the mechanisms resulting in failure of neural tube closure are unknown, up to 70% of NTDs can be prevented by maternal folic acid supplementation. However, the metabolic mechanisms underlying the association between folic acid and NTD pathogenesis have not been identified. This review summarizes our current understanding of the mechanisms by which impairments in folate metabolism might ultimately lead to failure of neural tube closure, with an emphasis on untangling the relative contributions of nutritional deficiency and genetic risk factors to NTD pathogenesis. PMID:19180567

Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

PubMed Central

Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

2016-01-01

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

A Working Module for the Neurovascular Unit in the Sexually Dimorphic Nucleus of the Preoptic Area.

PubMed

He, Zhen; Cui, Li; Ferguson, Sherry A; Paule, Merle G

2018-01-01

The neurovascular unit (NVU) can be conceptualized as a functional entity consisting of neurons, astrocytes, pericytes, and endothelial and smooth muscle cells that operate in concert to affect blood flow to a very circumscribed area. Although we are currently in a "golden era" of bioengineering, there are, as yet, no living NVUs-on-a-chip modules available and the development of a neural chip that would mimic NVUs is a seemingly lofty goal. The sexually dimorphic nucleus of the preoptic area (SDN-POA) is a tiny brain structure (between 0.001~0.007 mm 3 in rats) with an assessable biological function (i.e., male sexual behavior). The present effort was undertaken to determine whether there are identifiable NVUs in the SDN-POA by assessing its vasculature relative to its known neural components. First, a thorough and systematic review of thousands of histologic and immunofluorescent images from 201 weanling and adult rats was undertaken to define the characteristics of the vessels supplying the SDN-POA: its primary supply artery/arteriole and capillaries are physically inseparable from their neural elements. A subsequent immunofluorescent study targeting α-smooth muscle actin confirmed the identity of an artery/arteriole supplying the SDN-POA. In reality, the predominant components of the SDN-POA are calbindin D28k-positive neurons that are comingled with tyrosine hydroxylase-positive projections. Finally, a schematic of an SDN-POA NVU is proposed as a working model of the basic building block of the CNS. Such modules could serve the study of neurovascular mechanisms and potentially inform the development of next generation bioengineered neural transplants, i.e., the construct of an NVU neural chip.

Characterization of NvLWamide-like neurons reveals stereotypy in Nematostella nerve net development.

PubMed

Havrilak, Jamie A; Faltine-Gonzalez, Dylan; Wen, Yiling; Fodera, Daniella; Simpson, Ayanna C; Magie, Craig R; Layden, Michael J

2017-11-15

The organization of cnidarian nerve nets is traditionally described as diffuse with randomly arranged neurites that show minimal reproducibility between animals. However, most observations of nerve nets are conducted using cross-reactive antibodies that broadly label neurons, which potentially masks stereotyped patterns produced by individual neuronal subtypes. Additionally, many cnidarians species have overt structures such as a nerve ring, suggesting higher levels of organization and stereotypy exist, but mechanisms that generated that stereotypy are unknown. We previously demonstrated that NvLWamide-like is expressed in a small subset of the Nematostella nerve net and speculated that observing a few neurons within the developing nerve net would provide a better indication of potential stereotypy. Here we document NvLWamide-like expression more systematically. NvLWamide-like is initially expressed in the typical neurogenic salt and pepper pattern within the ectoderm at the gastrula stage, and expression expands to include endodermal salt and pepper expression at the planula larval stage. Expression persists in both ectoderm and endoderm in adults. We characterized our NvLWamide-like::mCherry transgenic reporter line to visualize neural architecture and found that NvLWamide-like is expressed in six neural subtypes identifiable by neural morphology and location. Upon completing development the numbers of neurons in each neural subtype are minimally variable between animals and the projection patterns of each subtype are consistent. Furthermore, between the juvenile polyp and adult stages the number of neurons for each subtype increases. We conclude that development of the Nematostella nerve net is stereotyped between individuals. Our data also imply that one aspect of generating adult cnidarian nervous systems is to modify the basic structural architecture generated in the juvenile by increasing neural number proportionally with size. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Brain activations during judgments of positive self-conscious emotion and positive basic emotion: pride and joy.

PubMed

Takahashi, Hidehiko; Matsuura, Masato; Koeda, Michihiko; Yahata, Noriaki; Suhara, Tetsuya; Kato, Motoichiro; Okubo, Yoshiro

2008-04-01

We aimed to investigate the neural correlates associated with judgments of a positive self-conscious emotion, pride, and elucidate the difference between pride and a basic positive emotion, joy, at the neural basis level using functional magnetic resonance imaging. Study of the neural basis associated with pride might contribute to a better understanding of the pride-related behaviors observed in neuropsychiatric disorders. Sixteen healthy volunteers were studied. The participants read sentences expressing joy or pride contents during the scans. Pride conditions activated the right posterior superior temporal sulcus and left temporal pole, the regions implicated in the neural substrate of social cognition or theory of mind. However, against our prediction, we did not find brain activation in the medial prefrontal cortex, a region responsible for inferring others' intention or self-reflection. Joy condition produced activations in the ventral striatum and insula/operculum, the key nodes of processing of hedonic or appetitive stimuli. Our results support the idea that pride is a self-conscious emotion, requiring the ability to detect the intention of others. At the same time, judgment of pride might require less self-reflection compared with those of negative self-conscious emotions such as guilt or embarrassment.

Capitalizing on Basic Brain Processes in Developmental Algebra--Part 3

ERIC Educational Resources Information Center

Laughbaum, Edward D.

2011-01-01

In Part Three, the author reviews the basic ideas presented in Parts One and Two while arguing why the traditional equation-solving developmental algebra curricula is not a good choice for implementing neural response strategies presented in the first two parts. He continues by showing that the developmental algebra student audience is simply…

Neural control of muscle

NASA Technical Reports Server (NTRS)

Max, S. R.; Markelonis, G. J.

1983-01-01

Cholinergic innervation regulates the physiological and biochemical properties of skeletal muscle. The mechanisms that appear to be involved in this regulation include soluble, neurally-derived polypeptides, transmitter-evoked muscle activity and the neurotransmitter, acetylcholine, itself. Despite extensive research, the interacting neural mechanisms that control such macromolecules as acetylcholinesterase, the acetylcholine receptor and glucose 6-phosphate dehydrogenase remain unclear. It may be that more simplified in vitro model systems coupled with recent dramatic advances in the molecular biology of neurally-regulated proteins will begin to allow researchers to unravel the mechanisms controlling the expression and maintenance of these macromolecules.

Postoperative pain—from mechanisms to treatment

PubMed Central

Pogatzki-Zahn, Esther M.; Segelcke, Daniel; Schug, Stephan A.

2017-01-01

Abstract Introduction: Pain management after surgery continues to be suboptimal; there are several reasons including lack of translation of results from basic science studies and scientific clinical evidence into clinical praxis. Objectives: This review presents and discusses basic science findings and scientific evidence generated within the last 2 decades in the field of acute postoperative pain. Methods: In the first part of the review, we give an overview about studies that have investigated the pathophysiology of postoperative pain by using rodent models of incisional pain up to July 2016. The second focus of the review lies on treatment recommendations based on guidelines and clinical evidence, eg, by using the fourth edition of the “Acute Pain Management: Scientific Evidence” of the Australian and New Zealand College of Anaesthetists and Faculty of Pain Medicine. Results: Preclinical studies in rodent models characterized responses of primary afferent nociceptors and dorsal horn neurons as one neural basis for pain behavior including resting pain, hyperalgesia, movement-evoked pain or anxiety- and depression-like behaviors after surgery. Furthermore, the role of certain receptors, mediators, and neurotransmitters involved in peripheral and central sensitization after incision were identified; many of these are very specific, relate to some modalities only, and are unique for incisional pain. Future treatment should focus on these targets to develop therapeutic agents that are effective for the treatment of postoperative pain as well as have few side effects. Furthermore, basic science findings translate well into results from clinical studies. Scientific evidence is able to point towards useful (and less useful) elements of multimodal analgesia able to reduce opioid consumption, improve pain management, and enhance recovery. Conclusion: Understanding basic mechanisms of postoperative pain to identify effective treatment strategies may improve patients' outcome after surgery. PMID:29392204

Associative Learning in Invertebrates

PubMed Central

Hawkins, Robert D.; Byrne, John H.

2015-01-01

This work reviews research on neural mechanisms of two types of associative learning in the marine mollusk Aplysia, classical conditioning of the gill- and siphon-withdrawal reflex and operant conditioning of feeding behavior. Basic classical conditioning is caused in part by activity-dependent facilitation at sensory neuron–motor neuron (SN–MN) synapses and involves a hybrid combination of activity-dependent presynaptic facilitation and Hebbian potentiation, which are coordinated by trans-synaptic signaling. Classical conditioning also shows several higher-order features, which might be explained by the known circuit connections in Aplysia. Operant conditioning is caused in part by a different type of mechanism, an intrinsic increase in excitability of an identified neuron in the central pattern generator (CPG) for feeding. However, for both classical and operant conditioning, adenylyl cyclase is a molecular site of convergence of the two signals that are associated. Learning in other invertebrate preparations also involves many of the same mechanisms, which may contribute to learning in vertebrates as well. PMID:25877219

Tissue stiffening coordinates morphogenesis by triggering collective cell migration in vivo.

PubMed

Barriga, Elias H; Franze, Kristian; Charras, Guillaume; Mayor, Roberto

2018-02-22

Collective cell migration is essential for morphogenesis, tissue remodelling and cancer invasion. In vivo, groups of cells move in an orchestrated way through tissues. This movement involves mechanical as well as molecular interactions between cells and their environment. While the role of molecular signals in collective cell migration is comparatively well understood, how tissue mechanics influence collective cell migration in vivo remains unknown. Here we investigated the importance of mechanical cues in the collective migration of the Xenopus laevis neural crest cells, an embryonic cell population whose migratory behaviour has been likened to cancer invasion. We found that, during morphogenesis, the head mesoderm underlying the cephalic neural crest stiffens. This stiffening initiates an epithelial-to-mesenchymal transition in neural crest cells and triggers their collective migration. To detect changes in their mechanical environment, neural crest cells use mechanosensation mediated by the integrin-vinculin-talin complex. By performing mechanical and molecular manipulations, we show that mesoderm stiffening is necessary and sufficient to trigger neural crest migration. Finally, we demonstrate that convergent extension of the mesoderm, which starts during gastrulation, leads to increased mesoderm stiffness by increasing the cell density underneath the neural crest. These results show that convergent extension of the mesoderm has a role as a mechanical coordinator of morphogenesis, and reveal a link between two apparently unconnected processes-gastrulation and neural crest migration-via changes in tissue mechanics. Overall, we demonstrate that changes in substrate stiffness can trigger collective cell migration by promoting epithelial-to-mesenchymal transition in vivo. More broadly, our results raise the idea that tissue mechanics combines with molecular effectors to coordinate morphogenesis.

RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS

PubMed Central

Purcell, Braden A.; Palmeri, Thomas J.

2016-01-01

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception. PMID:28392584

Mechanics of neurulation: From classical to current perspectives on the physical mechanics that shape, fold, and form the neural tube.

PubMed

Vijayraghavan, Deepthi S; Davidson, Lance A

2017-01-30

Neural tube defects arise from mechanical failures in the process of neurulation. At the most fundamental level, formation of the neural tube relies on coordinated, complex tissue movements that mechanically transform the flat neural epithelium into a lumenized epithelial tube (Davidson, 2012). The nature of this mechanical transformation has mystified embryologists, geneticists, and clinicians for more than 100 years. Early embryologists pondered the physical mechanisms that guide this transformation. Detailed observations of cell and tissue movements as well as experimental embryological manipulations allowed researchers to generate and test elementary hypotheses of the intrinsic and extrinsic forces acting on the neural tissue. Current research has turned toward understanding the molecular mechanisms underlying neurulation. Genetic and molecular perturbation have identified a multitude of subcellular components that correlate with cell behaviors and tissue movements during neural tube formation. In this review, we focus on methods and conceptual frameworks that have been applied to the study of amphibian neurulation that can be used to determine how molecular and physical mechanisms are integrated and responsible for neurulation. We will describe how qualitative descriptions and quantitative measurements of strain, force generation, and tissue material properties as well as simulations can be used to understand how embryos use morphogenetic programs to drive neurulation. Birth Defects Research 109:153-168, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Top-down predictions in the cognitive brain

PubMed Central

Kveraga, Kestutis; Ghuman, Avniel S.; Bar, Moshe

2007-01-01

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, it is proposed tat the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. This review concentrates on visual recognition as the model system for developing and testing ideas about the role and mechanisms of top-down predictions in the brain. We cover relevant behavioral, computational and neural aspects. These ideas are then extended to other domains. The basic elements of this proposal include analogical mapping, associative representations and the generation of predictions. Connections to a host of cognitive processes will be made and implications to several mental disorders will be proposed. PMID:17923222

Affect is a form of cognition: A neurobiological analysis

PubMed Central

Duncan, Seth; Barrett, Lisa Feldman

2008-01-01

In this paper, we suggest that affect meets the traditional definition of “cognition” such that the affect–cognition distinction is phenomenological, rather than ontological. We review how the affect–cognition distinction is not respected in the human brain, and discuss the neural mechanisms by which affect influences sensory processing. As a result of this sensory modulation, affect performs several basic “cognitive” functions. Affect appears to be necessary for normal conscious experience, language fluency, and memory. Finally, we suggest that understanding the differences between affect and cognition will require systematic study of how the phenomenological distinction characterising the two comes about, and why such a distinction is functional. PMID:18509504

Control of octopus arm extension by a peripheral motor program.

PubMed

Sumbre, G; Gutfreund, Y; Fiorito, G; Flash, T; Hochner, B

2001-09-07

For goal-directed arm movements, the nervous system generates a sequence of motor commands that bring the arm toward the target. Control of the octopus arm is especially complex because the arm can be moved in any direction, with a virtually infinite number of degrees of freedom. Here we show that arm extensions can be evoked mechanically or electrically in arms whose connection with the brain has been severed. These extensions show kinematic features that are almost identical to normal behavior, suggesting that the basic motor program for voluntary movement is embedded within the neural circuitry of the arm itself. Such peripheral motor programs represent considerable simplification in the motor control of this highly redundant appendage.

Neural Mechanisms of Recognizing Camouflaged Objects: A Human fMRI Study

DTIC Science & Technology

2015-07-30

Unlimited Final Report: Neural Mechanisms of Recognizing Camouflaged Objects: A Human fMRI Study The views, opinions and/or findings contained in this...27709-2211 Visual search, Camouflage, Functional magnetic resonance imaging ( fMRI ), Perceptual learning REPORT DOCUMENTATION PAGE 11. SPONSOR...ABSTRACT Number of Papers published in peer-reviewed journals: Final Report: Neural Mechanisms of Recognizing Camouflaged Objects: A Human fMRI Study

Basic disturbances of information processing in psychosis prediction.

PubMed

Bodatsch, Mitja; Klosterkötter, Joachim; Müller, Ralf; Ruhrmann, Stephan

2013-01-01

The basic symptoms (BS) approach provides a valid instrument in predicting psychosis onset and represents moreover a significant heuristic framework for research. The term "basic symptoms" denotes subtle changes of cognition and perception in the earliest and prodromal stages of psychosis development. BS are thought to correspond to disturbances of neural information processing. Following the heuristic implications of the BS approach, the present paper aims at exploring disturbances of information processing, revealed by functional magnetic resonance imaging (fMRI) and electro-encephalographic as characteristics of the at-risk state of psychosis. Furthermore, since high-risk studies employing ultra-high-risk criteria revealed non-conversion rates commonly exceeding 50%, thus warranting approaches that increase specificity, the potential contribution of neural information processing disturbances to psychosis prediction is reviewed. In summary, the at-risk state seems to be associated with information processing disturbances. Moreover, fMRI investigations suggested that disturbances of language processing domains might be a characteristic of the prodromal state. Neurophysiological studies revealed that disturbances of sensory processing may assist psychosis prediction in allowing for a quantification of risk in terms of magnitude and time. The latter finding represents a significant advancement since an estimation of the time to event has not yet been achieved by clinical approaches. Some evidence suggests a close relationship between self-experienced BS and neural information processing. With regard to future research, the relationship between neural information processing disturbances and different clinical risk concepts warrants further investigations. Thereby, a possible time sequence in the prodromal phase might be of particular interest.

Histaminergic Mechanisms for Modulation of Memory Systems

PubMed Central

Köhler, Cristiano André; da Silva, Weber Cláudio; Benetti, Fernando; Bonini, Juliana Sartori

2011-01-01

Encoding for several memory types requires neural changes and the activity of distinct regions across the brain. These areas receive broad projections originating in nuclei located in the brainstem which are capable of modulating the activity of a particular area. The histaminergic system is one of the major modulatory systems, and it regulates basic homeostatic and higher functions including arousal, circadian, and feeding rhythms, and cognition. There is now evidence that histamine can modulate learning in different types of behavioral tasks, but the exact course of modulation and its mechanisms are controversial. In the present paper we review the involvement of the histaminergic system and the effects histaminergic receptor agonists/antagonists have on the performance of tasks associated with the main memory types as well as evidence provided by studies with knockout models. Thus, we aim to summarize the possible effects histamine has on modulation of circuits involved in memory formation. PMID:21876818

Blaming the brain for obesity: Integration of hedonic and homeostatic mechanisms

PubMed Central

Berthoud, Hans-Rudolf; Münzberg, Heike; Morrison, Christopher D.

2017-01-01

The brain plays a key role in the controls of energy intake and expenditure and many genes associated with obesity are expressed in the central nervous system. Technological and conceptual advances in both basic and clinical neurosciences have expanded the traditional view of homeostatic regulation of body weight by mainly the hypothalamus to include hedonic controls of appetite by cortical and subcortical brain areas processing external sensory information, reward, cognition, and executive functions. Thus, hedonic controls interact with homeostatic controls to regulate body weight in a flexible and adaptive manner that takes environmental conditions into account. This new conceptual framework has several important implications for the treatment of obesity. Because much of this interactive neural processing is outside awareness, cognitive restraint in a world of plenty is made difficult and prevention and treatment of obesity should be more rationally directed to the complex and often redundant mechanisms underlying this interaction. PMID:28192106

Transient Modulations of Neural Responses to Heartbeats Covary with Bodily Self-Consciousness.

PubMed

Park, Hyeong-Dong; Bernasconi, Fosco; Bello-Ruiz, Javier; Pfeiffer, Christian; Salomon, Roy; Blanke, Olaf

2016-08-10

Recent research has investigated self-consciousness associated with the multisensory processing of bodily signals (e.g., somatosensory, visual, vestibular signals), a notion referred to as bodily self-consciousness, and these studies have shown that the manipulation of bodily inputs induces changes in bodily self-consciousness such as self-identification. Another line of research has highlighted the importance of signals from the inside of the body (e.g., visceral signals) and proposed that neural representations of internal bodily signals underlie self-consciousness, which to date has been based on philosophical inquiry, clinical case studies, and behavioral studies. Here, we investigated the relationship of bodily self-consciousness with the neural processing of internal bodily signals. By combining electrical neuroimaging, analysis of peripheral physiological signals, and virtual reality technology in humans, we show that transient modulations of neural responses to heartbeats in the posterior cingulate cortex covary with changes in bodily self-consciousness induced by the full-body illusion. Additional analyses excluded that measured basic cardiorespiratory parameters or interoceptive sensitivity traits could account for this finding. These neurophysiological data link experimentally the cortical mapping of the internal body to self-consciousness. What are the brain mechanisms of self-consciousness? Prominent views propose that the neural processing associated with signals from the internal organs (such as the heart and the lung) plays a critical role in self-consciousness. Although this hypothesis dates back to influential views in philosophy and psychology (e.g., William James), definitive experimental evidence supporting this idea is lacking despite its recent impact in neuroscience. In the present study, we show that posterior cingulate activities responding to heartbeat signals covary with changes in participants' conscious self-identification with a body that were manipulated experimentally using virtual reality technology. Our finding provides important neural evidence about the long-standing proposal that self-consciousness is linked to the cortical processing of internal bodily signals. Copyright © 2016 the authors 0270-6474/16/368453-08$15.00/0.

The Frog Vestibular System as a Model for Lesion-Induced Plasticity: Basic Neural Principles and Implications for Posture Control

PubMed Central

Lambert, François M.; Straka, Hans

2011-01-01

Studies of behavioral consequences after unilateral labyrinthectomy have a long tradition in the quest of determining rules and limitations of the central nervous system (CNS) to exert plastic changes that assist the recuperation from the loss of sensory inputs. Frogs were among the first animal models to illustrate general principles of regenerative capacity and reorganizational neural flexibility after a vestibular lesion. The continuous successful use of the latter animals is in part based on the easy access and identifiability of nerve branches to inner ear organs for surgical intervention, the possibility to employ whole brain preparations for in vitro studies and the limited degree of freedom of postural reflexes for quantification of behavioral impairments and subsequent improvements. Major discoveries that increased the knowledge of post-lesional reactive mechanisms in the CNS include alterations in vestibular commissural signal processing and activation of cooperative changes in excitatory and inhibitory inputs to disfacilitated neurons. Moreover, the observed increase of synaptic efficacy in propriospinal circuits illustrates the importance of limb proprioceptive inputs for postural recovery. Accumulated evidence suggests that the lesion-induced neural plasticity is not a goal-directed process that aims toward a meaningful restoration of vestibular reflexes but rather attempts a survival of those neurons that have lost their excitatory inputs. Accordingly, the reaction mechanism causes an improvement of some components but also a deterioration of other aspects as seen by spatio-temporally inappropriate vestibulo-motor responses, similar to the consequences of plasticity processes in various sensory systems and species. The generality of the findings indicate that frogs continue to form a highly amenable vertebrate model system for exploring molecular and physiological events during cellular and network reorganization after a loss of vestibular function. PMID:22518109

Neural networks for vertical microcode compaction

NASA Astrophysics Data System (ADS)

Chu, Pong P.

1992-09-01

Neural networks provide an alternative way to solve complex optimization problems. Instead of performing a program of instructions sequentially as in a traditional computer, neural network model explores many competing hypotheses simultaneously using its massively parallel net. The paper shows how to use the neural network approach to perform vertical micro-code compaction for a micro-programmed control unit. The compaction procedure includes two basic steps. The first step determines the compatibility classes and the second step selects a minimal subset to cover the control signals. Since the selection process is an NP- complete problem, to find an optimal solution is impractical. In this study, we employ a customized neural network to obtain the minimal subset. We first formalize this problem, and then define an `energy function' and map it to a two-layer fully connected neural network. The modified network has two types of neurons and can always obtain a valid solution.

Acid-sensing ion channels: trafficking and synaptic function.

PubMed

Zha, Xiang-ming

2013-01-02

Extracellular acidification occurs in the brain with elevated neural activity, increased metabolism, and neuronal injury. This reduction in pH can have profound effects on brain function because pH regulates essentially every single biochemical reaction. Therefore, it is not surprising to see that Nature evolves a family of proteins, the acid-sensing ion channels (ASICs), to sense extracellular pH reduction. ASICs are proton-gated cation channels that are mainly expressed in the nervous system. In recent years, a growing body of literature has shown that acidosis, through activating ASICs, contributes to multiple diseases, including ischemia, multiple sclerosis, and seizures. In addition, ASICs play a key role in fear and anxiety related psychiatric disorders. Several recent reviews have summarized the importance and therapeutic potential of ASICs in neurological diseases, as well as the structure-function relationship of ASICs. However, there is little focused coverage on either the basic biology of ASICs or their contribution to neural plasticity. This review will center on these topics, with an emphasis on the synaptic role of ASICs and molecular mechanisms regulating the spatial distribution and function of these ion channels.

Revolutionary Impact of Nanodrug Delivery on Neuroscience

PubMed Central

Khanbabaie, Reza; Jahanshahi, Mohsen

2012-01-01

Brain research is the most expanding interdisciplinary research that is using the state of the art techniques to overcome limitations in order to conduct more accurate and effective experiments. Drug delivery to the target site in the central nervous system (CNS) is one of the most difficult steps in neuroscience researches and therapies. Taking advantage of the nanoscale structure of neural cells (both neurons and glia); nanodrug delivery (second generation of biotechnological products) has a potential revolutionary impact into the basic understanding, visualization and therapeutic applications of neuroscience. Current review article firstly provides an overview of preparation and characterization, purification and separation, loading and delivering of nanodrugs. Different types of nanoparticle bioproducts and a number of methods for their fabrication and delivery systems including (carbon) nanotubes are explained. In the second part, neuroscience and nervous system drugs are deeply investigated. Different mechanisms in which nanoparticles enhance the uptake and clearance of molecules form cerebrospinal fluid (CSF) are discussed. The focus is on nanodrugs that are being used or have potential to improve neural researches, diagnosis and therapy of neurodegenerative disorders. PMID:23730260

Depression and treatment response: dynamic interplay of signaling pathways and altered neural processes

PubMed Central

Duric, Vanja

2014-01-01

Since the 1960s, when the first tricyclic and monoamine oxidase inhibitor antidepressant drugs were introduced, most of the ensuing agents were designed to target similar brain pathways that elevate serotonin and/or norepinephrine signaling. Fifty years later, the main goal of the current depression research is to develop faster-acting, more effective therapeutic agents with fewer side effects, as currently available antidepressants are plagued by delayed therapeutic onset and low response rates. Clinical and basic science research studies have made significant progress towards deciphering the pathophysiological events within the brain involved in development, maintenance, and treatment of major depressive disorder. Imaging and postmortem brain studies in depressed human subjects, in combination with animal behavioral models of depression, have identified a number of different cellular events, intracellular signaling pathways, proteins, and target genes that are modulated by stress and are potentially vital mediators of antidepressant action. In this review, we focus on several neural mechanisms, primarily within the hippocampus and prefrontal cortex, which have recently been implicated in depression and treatment response. PMID:22585060

Structural and synaptic plasticity in stress-related disorders

PubMed Central

Christoffel, Daniel J.; Golden, Sam A.; Russo, Scott J.

2011-01-01

Stress can have a lasting impact on the structure and function of brain circuitry that results in long-lasting changes in the behavior of an organism. Synaptic plasticity is the mechanism by which information is stored and maintained within individual synapses, neurons, and neuronal circuits to guide the behavior of an organism. Although these mechanisms allow the organism to adapt to its constantly evolving environment, not all of these adaptations are beneficial. Under prolonged bouts of physical or psychological stress, these mechanisms become dysregulated, and the connectivity between brain regions becomes unbalanced, resulting in pathological behaviors. In this review, we highlight the effects of stress on the structure and function of neurons within the mesocorticolimbic brain systems known to regulate mood and motivation. We then discuss the implications of these spine adaptations on neuronal activity and pathological behaviors implicated in mood disorders. Finally, we end by discussing recent brain imaging studies in human depression within the context of these basic findings to provide insight into the underlying mechanisms leading to neural dysfunction in depression. PMID:21967517

Coherent Ising machines—optical neural networks operating at the quantum limit

NASA Astrophysics Data System (ADS)

Yamamoto, Yoshihisa; Aihara, Kazuyuki; Leleu, Timothee; Kawarabayashi, Ken-ichi; Kako, Satoshi; Fejer, Martin; Inoue, Kyo; Takesue, Hiroki

2017-12-01

In this article, we will introduce the basic concept and the quantum feature of a novel computing system, coherent Ising machines, and describe their theoretical and experimental performance. We start with the discussion how to construct such physical devices as the quantum analog of classical neuron and synapse, and end with the performance comparison against various classical neural networks implemented in CPU and supercomputers.

Sensory Entrainment Mechanisms in Auditory Perception: Neural Synchronization Cortico-Striatal Activation.

PubMed

Sameiro-Barbosa, Catia M; Geiser, Eveline

2016-01-01

The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system.

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

PubMed Central

Dick, Thomas E.; Molkov, Yaroslav I.; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J.; Doyle, John; Scheff, Jeremy D.; Calvano, Steve E.; Androulakis, Ioannis P.; An, Gary; Vodovotz, Yoram

2012-01-01

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma. PMID:22783197

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling.

PubMed

Dick, Thomas E; Molkov, Yaroslav I; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J; Doyle, John; Scheff, Jeremy D; Calvano, Steve E; Androulakis, Ioannis P; An, Gary; Vodovotz, Yoram

2012-01-01

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

3D culture models of Alzheimer's disease: a road map to a "cure-in-a-dish".

PubMed

Choi, Se Hoon; Kim, Young Hye; Quinti, Luisa; Tanzi, Rudolph E; Kim, Doo Yeon

2016-12-09

Alzheimer's disease (AD) transgenic mice have been used as a standard AD model for basic mechanistic studies and drug discovery. These mouse models showed symbolic AD pathologies including β-amyloid (Aβ) plaques, gliosis and memory deficits but failed to fully recapitulate AD pathogenic cascades including robust phospho tau (p-tau) accumulation, clear neurofibrillary tangles (NFTs) and neurodegeneration, solely driven by familial AD (FAD) mutation(s). Recent advances in human stem cell and three-dimensional (3D) culture technologies made it possible to generate novel 3D neural cell culture models that recapitulate AD pathologies including robust Aβ deposition and Aβ-driven NFT-like tau pathology. These new 3D human cell culture models of AD hold a promise for a novel platform that can be used for mechanism studies in human brain-like environment and high-throughput drug screening (HTS). In this review, we will summarize the current progress in recapitulating AD pathogenic cascades in human neural cell culture models using AD patient-derived induced pluripotent stem cells (iPSCs) or genetically modified human stem cell lines. We will also explain how new 3D culture technologies were applied to accelerate Aβ and p-tau pathologies in human neural cell cultures, as compared the standard two-dimensional (2D) culture conditions. Finally, we will discuss a potential impact of the human 3D human neural cell culture models on the AD drug-development process. These revolutionary 3D culture models of AD will contribute to accelerate the discovery of novel AD drugs.

Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

PubMed

Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

2016-01-01

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

Implantable neurotechnologies: a review of integrated circuit neural amplifiers

PubMed Central

Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V.

2016-01-01

Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification. PMID:26798055

Central and Peripheral Regulation of Food Intake and Physical Activity: Pathways and Genes

PubMed Central

Lenard, Natalie R.; Berthoud, Hans-Rudolf

2009-01-01

A changing environment and lifestyle on the background of evolutionary engraved and perinatally imprinted physiological response patterns is the foremost explanation for the current obesity epidemic. However, it is not clear what the mechanisms are by which the modern environment overrides the physiological controls of appetite and homeostatic body-weight regulation. Food intake and energy expenditure are controlled by complex, redundant, and distributed neural systems involving thousands of genes and reflecting the fundamental biological importance of adequate nutrient supply and energy balance. There has been much progress in identifying the important role of hypothalamus and caudal brainstem in the various hormonal and neural mechanisms by which the brain informs itself about availability of ingested and stored nutrients and, in turn, generates behavioral, autonomic, and endocrine output. Some of the genes involved in this “homeostatic” regulator are crucial for energy balance as manifested in the well-known monogenic obesity models. However, it can be clearly demonstrated that much larger portions of the nervous system of animals and humans, including the cortex, basal ganglia, and the limbic system, are concerned with the procurement of food as a basic and evolutionarily conserved survival mechanism to defend the lower limits of adiposity. By forming representations and reward expectancies through processes of learning and memory, these systems evolved to engage powerful emotions for guaranteed supply with, and ingestion of, beneficial foods from a sparse and often hostile environment. They are now simply overwhelmed with an abundance of food and food cues no longer contested by predators and interrupted by famines. The anatomy, chemistry, and functions of these elaborate neural systems and their interactions with the “homeostatic” regulator in the hypothalamus are poorly understood, and many of the genes involved are either unknown or not well characterized. This is regrettable because these systems are directly and primarily involved in the interactions of the modern environment and lifestyle with the human body. They are no less “physiological” than metabolic-regulatory mechanisms that have attracted most of the research during the past 15 years. PMID:19190620

Social visual engagement in infants and toddlers with autism: Early developmental transitions and a model of pathogenesis

PubMed Central

Klin, Ami; Shultz, Sarah; Jones, Warren

2014-01-01

Efforts to determine and understand the causes of autism are currently hampered by a large disconnect between recent molecular genetics findings that are associated with the condition and the core behavioral symptoms that define the condition. In this perspective piece, we propose a systems biology framework to bridge that gap between genes and symptoms. The framework focuses on basic mechanisms of socialization that are highly-conserved in evolution and are early-emerging in development. By conceiving of these basic mechanisms of socialization as quantitative endophenotypes, we hope to connect genes and behavior in autism through integrative studies of neurodevelopmental, behavioral, and epigenetic changes. These changes both lead to and are led by the accomplishment of specific social adaptive tasks in a typical infant's life. However, based on recent research that indicates that infants later diagnosed with autism fail to accomplish at least some of these tasks, we suggest that a narrow developmental period, spanning critical transitions from reflexive, subcortically-controlled visual behavior to interactional, cortically-controlled and social visual behavior be prioritized for future study. Mapping epigenetic, neural, and behavioral changes that both drive and are driven by these early transitions may shed a bright light on the pathogenesis of autism. PMID:25445180

Social visual engagement in infants and toddlers with autism: early developmental transitions and a model of pathogenesis.

PubMed

Klin, Ami; Shultz, Sarah; Jones, Warren

2015-03-01

Efforts to determine and understand the causes of autism are currently hampered by a large disconnect between recent molecular genetics findings that are associated with the condition and the core behavioral symptoms that define the condition. In this perspective piece, we propose a systems biology framework to bridge that gap between genes and symptoms. The framework focuses on basic mechanisms of socialization that are highly-conserved in evolution and are early-emerging in development. By conceiving of these basic mechanisms of socialization as quantitative endophenotypes, we hope to connect genes and behavior in autism through integrative studies of neurodevelopmental, behavioral, and epigenetic changes. These changes both lead to and are led by the accomplishment of specific social adaptive tasks in a typical infant's life. However, based on recent research that indicates that infants later diagnosed with autism fail to accomplish at least some of these tasks, we suggest that a narrow developmental period, spanning critical transitions from reflexive, subcortically-controlled visual behavior to interactional, cortically-controlled and social visual behavior be prioritized for future study. Mapping epigenetic, neural, and behavioral changes that both drive and are driven by these early transitions may shed a bright light on the pathogenesis of autism. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural activity during natural viewing of Sesame Street statistically predicts test scores in early childhood.

PubMed

Cantlon, Jessica F; Li, Rosa

2013-01-01

It is not currently possible to measure the real-world thought process that a child has while observing an actual school lesson. However, if it could be done, children's neural processes would presumably be predictive of what they know. Such neural measures would shed new light on children's real-world thought. Toward that goal, this study examines neural processes that are evoked naturalistically, during educational television viewing. Children and adults all watched the same Sesame Street video during functional magnetic resonance imaging (fMRI). Whole-brain intersubject correlations between the neural timeseries from each child and a group of adults were used to derive maps of "neural maturity" for children. Neural maturity in the intraparietal sulcus (IPS), a region with a known role in basic numerical cognition, predicted children's formal mathematics abilities. In contrast, neural maturity in Broca's area correlated with children's verbal abilities, consistent with prior language research. Our data show that children's neural responses while watching complex real-world stimuli predict their cognitive abilities in a content-specific manner. This more ecologically natural paradigm, combined with the novel measure of "neural maturity," provides a new method for studying real-world mathematics development in the brain.

bHLH-O proteins balance the self-renewal and differentiation of Drosophila neural stem cells by regulating Earmuff expression.

PubMed

Li, Xiaosu; Chen, Rui; Zhu, Sijun

2017-11-15

Balancing self-renewal and differentiation of stem cells requires differential expression of self-renewing factors in two daughter cells generated from the asymmetric division of the stem cells. In Drosophila type II neural stem cell (or neuroblast, NB) lineages, the expression of the basic helix-loop-helix-Orange (bHLH-O) family proteins, including Deadpan (Dpn) and E(spl) proteins, is required for maintaining the self-renewal and identity of type II NBs, whereas the absence of these self-renewing factors is essential for the differentiation of intermediate neural progenitors (INPs) generated from type II NBs. Here, we demonstrate that Dpn maintains type II NBs by suppressing the expression of Earmuff (Erm). We provide evidence that Dpn and E(spl) proteins suppress Erm by directly binding to C-sites and N-boxes in the cis-regulatory region of erm. Conversely, the absence of bHLH-O proteins in INPs allows activation of erm and Erm-mediated maturation of INPs. Our results further suggest that Pointed P1 (PntP1) mediates the dedifferentiation of INPs resulting from the loss of Erm or overexpression of Dpn or E(spl) proteins. Taken together, these findings reveal mechanisms underlying the regulation of the maintenance of type II NBs and differentiation of INPs through the differential expression of bHLH-O family proteins. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural stem cells and neuro/gliogenesis in the central nervous system: understanding the structural and functional plasticity of the developing, mature, and diseased brain.

PubMed

Yamaguchi, Masahiro; Seki, Tatsunori; Imayoshi, Itaru; Tamamaki, Nobuaki; Hayashi, Yoshitaka; Tatebayashi, Yoshitaka; Hitoshi, Seiji

2016-05-01

Neurons and glia in the central nervous system (CNS) originate from neural stem cells (NSCs). Knowledge of the mechanisms of neuro/gliogenesis from NSCs is fundamental to our understanding of how complex brain architecture and function develop. NSCs are present not only in the developing brain but also in the mature brain in adults. Adult neurogenesis likely provides remarkable plasticity to the mature brain. In addition, recent progress in basic research in mental disorders suggests an etiological link with impaired neuro/gliogenesis in particular brain regions. Here, we review the recent progress and discuss future directions in stem cell and neuro/gliogenesis biology by introducing several topics presented at a joint meeting of the Japanese Association of Anatomists and the Physiological Society of Japan in 2015. Collectively, these topics indicated that neuro/gliogenesis from NSCs is a common event occurring in many brain regions at various ages in animals. Given that significant structural and functional changes in cells and neural networks are accompanied by neuro/gliogenesis from NSCs and the integration of newly generated cells into the network, stem cell and neuro/gliogenesis biology provides a good platform from which to develop an integrated understanding of the structural and functional plasticity that underlies the development of the CNS, its remodeling in adulthood, and the recovery from diseases that affect it.

Boys vs. girls: Gender differences in the neural development of trust and reciprocity depend on social context.

PubMed

Lemmers-Jansen, Imke L J; Krabbendam, Lydia; Veltman, Dick J; Fett, Anne-Kathrin J

2017-06-01

Trust and cooperation increase from adolescence to adulthood, but studies on gender differences in this development are rare. We investigated gender and age-related differences in trust and reciprocity and associated neural mechanisms in 43 individuals (16-27 years, 22 male). Participants played two multi-round trust games with a cooperative and an unfair partner. Males showed more basic trust towards unknown others than females. Both genders increased trust during cooperative interactions, with no differences in average trust. Age was unrelated to trust during cooperation. During unfair interactions males decreased their trust more with age than females. ROI analysis showed age-related increases in activation in the temporo-parietal junction (TPJ) and dorsolateral prefrontal cortex (dlPFC) during cooperative investments, and increased age-related caudate activation during both cooperative and unfair repayments. Gender differences in brain activation were only observed during cooperative repayments, with males activating the TPJ more than females, and females activating the caudate more. The findings suggest relatively mature processes of trust and reciprocity in the investigated age range. Gender differences only occur in unfair contexts, becoming more pronounced with age. Largely similar neural activation in males and females and few age effects suggest that similar, mature cognitive strategies are employed. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Research progress on Drosophila visual cognition in China.

PubMed

Guo, AiKe; Zhang, Ke; Peng, YueQin; Xi, Wang

2010-03-01

Visual cognition, as one of the fundamental aspects of cognitive neuroscience, is generally associated with high-order brain functions in animals and human. Drosophila, as a model organism, shares certain features of visual cognition in common with mammals at the genetic, molecular, cellular, and even higher behavioral levels. From learning and memory to decision making, Drosophila covers a broad spectrum of higher cognitive behaviors beyond what we had expected. Armed with powerful tools of genetic manipulation in Drosophila, an increasing number of studies have been conducted in order to elucidate the neural circuit mechanisms underlying these cognitive behaviors from a genes-brain-behavior perspective. The goal of this review is to integrate the most important studies on visual cognition in Drosophila carried out in mainland China during the last decade into a body of knowledge encompassing both the basic neural operations and circuitry of higher brain function in Drosophila. Here, we consider a series of the higher cognitive behaviors beyond learning and memory, such as visual pattern recognition, feature and context generalization, different feature memory traces, salience-based decision, attention-like behavior, and cross-modal leaning and memory. We discuss the possible general gain-gating mechanism implementing by dopamine - mushroom body circuit in fly's visual cognition. We hope that our brief review on this aspect will inspire further study on visual cognition in flies, or even beyond.

How we hear what is not there: A neural mechanism for the missing fundamental illusion

NASA Astrophysics Data System (ADS)

Chialvo, Dante R.

2003-12-01

How the brain estimates the pitch of a complex sound remains unsolved. Complex sounds are composed of more than one tone. When two tones occur together, a third lower pitched tone is often heard. This is referred to as the "missing fundamental illusion" because the perceived pitch is a frequency (fundamental) for which there is no actual source vibration. This phenomenon exemplifies a larger variety of problems related to how pitch is extracted from complex tones, music and speech, and thus has been extensively used to test theories of pitch perception. A noisy nonlinear process is presented here as a candidate neural mechanism to explain the majority of reported phenomenology and provide specific quantitative predictions. The two basic premises of this model are as follows: (I) The individual tones composing the complex tones add linearly producing peaks of constructive interference whose amplitude is always insufficient to fire the neuron (II): The spike threshold is reached only with noise, which naturally selects the maximum constructive interferences. The spacing of these maxima, and consequently the spikes, occurs at a rate identical to the perceived pitch for the complex tone. Comparison with psychophysical and physiological data reveals a remarkable quantitative agreement not dependent on adjustable parameters. In addition, results from numerical simulations across different models are consistent, suggesting relevance to other sensory modalities.

Intrinsic and Extrinsic Neuromodulation of Olfactory Processing

PubMed Central

Lizbinski, Kristyn M.; Dacks, Andrew M.

2018-01-01

Neuromodulation is a ubiquitous feature of neural systems, allowing flexible, context specific control over network dynamics. Neuromodulation was first described in invertebrate motor systems and early work established a basic dichotomy for neuromodulation as having either an intrinsic origin (i.e., neurons that participate in network coding) or an extrinsic origin (i.e., neurons from independent networks). In this conceptual dichotomy, intrinsic sources of neuromodulation provide a “memory” by adjusting network dynamics based upon previous and ongoing activation of the network itself, while extrinsic neuromodulators provide the context of ongoing activity of other neural networks. Although this dichotomy has been thoroughly considered in motor systems, it has received far less attention in sensory systems. In this review, we discuss intrinsic and extrinsic modulation in the context of olfactory processing in invertebrate and vertebrate model systems. We begin by discussing presynaptic modulation of olfactory sensory neurons by local interneurons (LNs) as a mechanism for gain control based on ongoing network activation. We then discuss the cell-class specific effects of serotonergic centrifugal neurons on olfactory processing. Finally, we briefly discuss the integration of intrinsic and extrinsic neuromodulation (metamodulation) as an effective mechanism for exerting global control over olfactory network dynamics. The heterogeneous nature of neuromodulation is a recurring theme throughout this review as the effects of both intrinsic and extrinsic modulation are generally non-uniform. PMID:29375314

The Influence of Personality on Neural Mechanisms of Observational Fear and Reward Learning

ERIC Educational Resources Information Center

Hooker, Christine I.; Verosky, Sara C.; Miyakawa, Asako; Knight, Robert T.; D'Esposito, Mark

2008-01-01

Fear and reward learning can occur through direct experience or observation. Both channels can enhance survival or create maladaptive behavior. We used fMRI to isolate neural mechanisms of observational fear and reward learning and investigate whether neural response varied according to individual differences in neuroticism and extraversion.…

NMDA Receptor Signaling Is Important for Neural Tube Formation and for Preventing Antiepileptic Drug-Induced Neural Tube Defects.

PubMed

Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N

2018-05-16

Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.

Wittgenstein running: neural mechanisms of collective intentionality and we-mode.

PubMed

Becchio, Cristina; Bertone, Cesare

2004-03-01

In this paper we discuss the problem of the neural conditions of shared attitudes and intentions: which neural mechanisms underlie "we-mode" processes or serve as precursors to such processes? Neurophysiological and neuropsychological evidence suggests that in different areas of the brain neural representations are shared by several individuals. This situation, on the one hand, creates a potential problem for correct attribution. On the other hand, it may provide the conditions for shared attitudes and intentions.

Neural network tracking and extension of positive tracking periods

NASA Technical Reports Server (NTRS)

Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

2004-01-01

Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

Neural network tracking and extension of positive tracking periods

NASA Astrophysics Data System (ADS)

Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

2004-04-01

Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes

PubMed Central

Costa, Tommaso; Cauda, Franco; Crini, Manuella; Tatu, Mona-Karina; Celeghin, Alessia; de Gelder, Beatrice

2014-01-01

The different temporal dynamics of emotions are critical to understand their evolutionary role in the regulation of interactions with the surrounding environment. Here, we investigated the temporal dynamics underlying the perception of four basic emotions from complex scenes varying in valence and arousal (fear, disgust, happiness and sadness) with the millisecond time resolution of Electroencephalography (EEG). Event-related potentials were computed and each emotion showed a specific temporal profile, as revealed by distinct time segments of significant differences from the neutral scenes. Fear perception elicited significant activity at the earliest time segments, followed by disgust, happiness and sadness. Moreover, fear, disgust and happiness were characterized by two time segments of significant activity, whereas sadness showed only one long-latency time segment of activity. Multidimensional scaling was used to assess the correspondence between neural temporal dynamics and the subjective experience elicited by the four emotions in a subsequent behavioral task. We found a high coherence between these two classes of data, indicating that psychological categories defining emotions have a close correspondence at the brain level in terms of neural temporal dynamics. Finally, we localized the brain regions of time-dependent activity for each emotion and time segment with the low-resolution brain electromagnetic tomography. Fear and disgust showed widely distributed activations, predominantly in the right hemisphere. Happiness activated a number of areas mostly in the left hemisphere, whereas sadness showed a limited number of active areas at late latency. The present findings indicate that the neural signature of basic emotions can emerge as the byproduct of dynamic spatiotemporal brain networks as investigated with millisecond-range resolution, rather than in time-independent areas involved uniquely in the processing one specific emotion. PMID:24214921

Individual differences in neural mechanisms of selective auditory attention in preschoolers from lower socioeconomic status backgrounds: an event-related potentials study.

PubMed

Isbell, Elif; Wray, Amanda Hampton; Neville, Helen J

2016-11-01

Selective attention, the ability to enhance the processing of particular input while suppressing the information from other concurrent sources, has been postulated to be a foundational skill for learning and academic achievement. The neural mechanisms of this foundational ability are both vulnerable and enhanceable in children from lower socioeconomic status (SES) families. In the current study, we assessed individual differences in neural mechanisms of this malleable brain function in children from lower SES families. Specifically, we investigated the extent to which individual differences in neural mechanisms of selective auditory attention accounted for variability in nonverbal cognitive abilities in lower SES preschoolers. We recorded event-related potentials (ERPs) during a dichotic listening task and administered nonverbal IQ tasks to 124 lower SES children (77 females) between the ages of 40 and 67 months. The attention effect, i.e., the difference in ERP mean amplitudes elicited by identical probes embedded in stories when attended versus unattended, was significantly correlated with nonverbal IQ scores. Larger, more positive attention effects over the anterior and central electrode locations were associated with superior nonverbal IQ performance. Our findings provide initial evidence for prominent individual differences in neural indices of selective attention in lower SES children. Furthermore, our results indicate a noteworthy relationship between neural mechanisms of selective attention and nonverbal IQ performance in lower SES preschoolers. These findings provide the basis for future research to identify the factors that contribute to such individual differences in neural mechanisms of selective attention. © 2015 John Wiley & Sons Ltd.

Evolution of central pattern generators and rhythmic behaviours

PubMed Central

Katz, Paul S.

2016-01-01

Comparisons of rhythmic movements and the central pattern generators (CPGs) that control them uncover principles about the evolution of behaviour and neural circuits. Over the course of evolutionary history, gradual evolution of behaviours and their neural circuitry within any lineage of animals has been a predominant occurrence. Small changes in gene regulation can lead to divergence of circuit organization and corresponding changes in behaviour. However, some behavioural divergence has resulted from large-scale rewiring of the neural network. Divergence of CPG circuits has also occurred without a corresponding change in behaviour. When analogous rhythmic behaviours have evolved independently, it has generally been with different neural mechanisms. Repeated evolution of particular rhythmic behaviours has occurred within some lineages due to parallel evolution or latent CPGs. Particular motor pattern generating mechanisms have also evolved independently in separate lineages. The evolution of CPGs and rhythmic behaviours shows that although most behaviours and neural circuits are highly conserved, the nature of the behaviour does not dictate the neural mechanism and that the presence of homologous neural components does not determine the behaviour. This suggests that although behaviour is generated by neural circuits, natural selection can act separately on these two levels of biological organization. PMID:26598733

Evolution of central pattern generators and rhythmic behaviours.

PubMed

Katz, Paul S

2016-01-05

Comparisons of rhythmic movements and the central pattern generators (CPGs) that control them uncover principles about the evolution of behaviour and neural circuits. Over the course of evolutionary history, gradual evolution of behaviours and their neural circuitry within any lineage of animals has been a predominant occurrence. Small changes in gene regulation can lead to divergence of circuit organization and corresponding changes in behaviour. However, some behavioural divergence has resulted from large-scale rewiring of the neural network. Divergence of CPG circuits has also occurred without a corresponding change in behaviour. When analogous rhythmic behaviours have evolved independently, it has generally been with different neural mechanisms. Repeated evolution of particular rhythmic behaviours has occurred within some lineages due to parallel evolution or latent CPGs. Particular motor pattern generating mechanisms have also evolved independently in separate lineages. The evolution of CPGs and rhythmic behaviours shows that although most behaviours and neural circuits are highly conserved, the nature of the behaviour does not dictate the neural mechanism and that the presence of homologous neural components does not determine the behaviour. This suggests that although behaviour is generated by neural circuits, natural selection can act separately on these two levels of biological organization. © 2015 The Author(s).

Structures, Not Strings: Linguistics as Part of the Cognitive Sciences.

PubMed

Everaert, Martin B H; Huybregts, Marinus A C; Chomsky, Noam; Berwick, Robert C; Bolhuis, Johan J

2015-12-01

There are many questions one can ask about human language: its distinctive properties, neural representation, characteristic uses including use in communicative contexts, variation, growth in the individual, and origin. Every such inquiry is guided by some concept of what 'language' is. Sharpening the core question--what is language?--and paying close attention to the basic property of the language faculty and its biological foundations makes it clear how linguistics is firmly positioned within the cognitive sciences. Here we will show how recent developments in generative grammar, taking language as a computational cognitive mechanism seriously, allow us to address issues left unexplained in the increasingly popular surface-oriented approaches to language. Copyright © 2015 Elsevier Ltd. All rights reserved.

Single-digit arithmetic processing—anatomical evidence from statistical voxel-based lesion analysis

PubMed Central

Mihulowicz, Urszula; Willmes, Klaus; Karnath, Hans-Otto; Klein, Elise

2014-01-01

Different specific mechanisms have been suggested for solving single-digit arithmetic operations. However, the neural correlates underlying basic arithmetic (multiplication, addition, subtraction) are still under debate. In the present study, we systematically assessed single-digit arithmetic in a group of acute stroke patients (n = 45) with circumscribed left- or right-hemispheric brain lesions. Lesion sites significantly related to impaired performance were found only in the left-hemisphere damaged (LHD) group. Deficits in multiplication and addition were related to subcortical/white matter brain regions differing from those for subtraction tasks, corroborating the notion of distinct processing pathways for different arithmetic tasks. Additionally, our results further point to the importance of investigating fiber pathways in numerical cognition. PMID:24847238

Emotional Response Deficits in Schizophrenia: Insights From Affective Science

PubMed Central

Kring, Ann M.; Moran, Erin K.

2008-01-01

Our understanding of the emotional features of schizophrenia has benefited greatly from the adoption of methods and theory from the field of affective science. This article covers basic concepts and methods from affective science on the psychological and neural mechanisms contributing to emotions and reviews the ways in which this research has advanced our understanding of emotional response deficits in schizophrenia. We review naturalistic studies and elicitation studies that evoke emotion responses among participants, including emotion expression, experience, and autonomic physiology. We also consider how these emotion response measures correspond to schizophrenia symptoms, and we focus particular attention on the issue of sex differences in emotional responding and how this may influence our understanding emotional functioning among individuals with schizophrenia. PMID:18579556

Biophotons as neural communication signals demonstrated by in situ biophoton autography.

PubMed

Sun, Yan; Wang, Chao; Dai, Jiapei

2010-03-01

Cell to cell communication by biophotons has been demonstrated in plants, bacteria, animal neutrophil granulocytes and kidney cells. Whether such signal communication exists in neural cells is unclear. By developing a new biophoton detection method, called in situ biophoton autography (IBA), we have investigated biophotonic activities in rat spinal nerve roots in vitro. We found that different spectral light stimulation (infrared, red, yellow, blue, green and white) at one end of the spinal sensory or motor nerve roots resulted in a significant increase in the biophotonic activity at the other end. Such effects could be significantly inhibited by procaine (a regional anaesthetic for neural conduction block) or classic metabolic inhibitors, suggesting that light stimulation can generate biophotons that conduct along the neural fibers, probably as neural communication signals. The mechanism of biophotonic conduction along neural fibers may be mediated by protein-protein biophotonic interactions. This study may provide a better understanding of the fundamental mechanisms of neural communication, the functions of the nervous system, such as vision, learning and memory, as well as the mechanisms of human neurological diseases.

Treatment of neural anosmia by topical application of basic fibroblast growth factor-gelatin hydrogel in the nasal cavity: an experimental study in mice.

PubMed

Nota, Jumpei; Takahashi, Hirotaka; Hakuba, Nobuhiro; Hato, Naohito; Gyo, Kiyofumi

2013-04-01

A new treatment of neural anosmia. To investigate the effects of basic fibroblast growth factor (bFGF)-gelatin hydrogel on recovery of neural anosmia in mice. Anosmia was induced by intraperitoneal injection of 3-methylindole, 200 mg/kg. One week later, the animals underwent 1 of the following 3 procedures bilaterally: (1) group A: single-shot intranasal drip infusion of phosphate-buffered saline, (2) group B: single-shot intranasal drip infusion of bFGF, and (3) group C: placement of bFGF-gelatin hydrogel in the nasal cavity. The olfactory function of the animal was evaluated by the odor-detection test (ODT) 2 and 4 weeks later. Following the testing, the animal was killed, the thickness of the olfactory epithelium was measured, and the number of olfactory marker protein (OMP)-positive cells was counted. Research installation. Mice. The placement of bFGF-gelatin hydrogel in the nasal cavity. An ODT, thickness of olfactory epithelium, the number of OMP-positive cells The ODT proved that neural anosmia recovered in group C but not in groups A and B. Histologically, olfactory epithelium became thicker and the number of OMP-positive cells increased in group C, while such functional and histologic recovery was poor in groups A and B. These findings suggested that placement of bFGF-gelatin hydrogel in the nasal cavity was an efficient way to facilitate recovery of neural anosmia. As a gelatin hydrogel degrades slowly in the body, bFGF is gradually released around the site of the lesion; thus, it constantly exerts its effects on neural regeneration.

Can responses to basic non-numerical visual features explain neural numerosity responses?

PubMed

Harvey, Ben M; Dumoulin, Serge O

2017-04-01

Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs. Copyright © 2017 Elsevier Inc. All rights reserved.

Parsing learning in networks using brain-machine interfaces.

PubMed

Orsborn, Amy L; Pesaran, Bijan

2017-10-01

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drugs and aggression.

PubMed

Rasia-Filho, Alberto A; Giovenardi, Márcia; de Almeida, Rosa M M

2008-01-01

Aggression is conceived as a social behavior that, in conjunct with motor and visceral displays, is related with acts for obtaining a specific goal or is directed against threatening stimuli with the intention of causing harm, either for attack or defense. Here it is reviewed basic concepts and aspects for the classification of aggression, the behavioral displays regarded as aggressive in animal models, the basic neural circuits that are involved to them and the pharmacological approaches involving some neurotransmitters (5-HT, dopamine and GABA) and drugs that can be used to identify the neural basis of aggression and to modulate its expression. Drug patents are referred in the text. Data are based on experiments developed mainly with rodents; however, some research hypotheses that may well give some insights for the clinical sciences in men were also included.

Cognitive and neural components of the phenomenology of agency.

PubMed

Morsella, Ezequiel; Berger, Christopher C; Krieger, Stepehen C

2011-06-01

A primary aspect of the self is the sense of agency – the sense that one is causing an action. In the spirit of recent reductionistic approaches to other complex, multifaceted phenomena (e.g., working memory; cf. Johnson &Johnson, 2009), we attempt to unravel the sense of agency by investigating its most basic components, without invoking high-level conceptual or 'central executive' processes. After considering the high-level components of agency, we examine the cognitive and neural underpinnings of its low-level components, which include basic consciousness and subjective urges (e.g., the urge to breathe when holding one's breath). Regarding urges, a quantitative review revealed that certain inter-representational dynamics (conflicts between action plans, as when holding one's breath) reliably engender fundamental aspects both of the phenomenology of agency and of 'something countering the will of the self'. The neural correlates of such dynamics, for both primordial urges (e.g., air hunger) and urges elicited in laboratory interference tasks, are entertained. In addition, we discuss the implications of this unique perspective for the study of disorders involving agency.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder

PubMed Central

Liddell, Belinda J.; Jobson, Laura

2016-01-01

A significant body of literature documents the neural mechanisms involved in the development and maintenance of posttraumatic stress disorder (PTSD). However, there is very little empirical work considering the influence of culture on these underlying mechanisms. Accumulating cultural neuroscience research clearly indicates that cultural differences in self-representation modulate many of the same neural processes proposed to be aberrant in PTSD. The objective of this review paper is to consider how culture may impact on the neural mechanisms underlying PTSD. We first outline five key affective and cognitive functions and their underlying neural correlates that have been identified as being disrupted in PTSD: (1) fear dysregulation; (2) attentional biases to threat; (3) emotion and autobiographical memory; (4) self-referential processing; and (5) attachment and interpersonal processing. Second, we consider prominent cultural theories and review the empirical research that has demonstrated the influence of cultural variations in self-representation on the neural substrates of these same five affective and cognitive functions. Finally, we propose a conceptual model that suggests that these five processes have major relevance to considering how culture may influence the neural processes underpinning PTSD. Highlights of the article Cultural variations in individualistic-collectivistic self-representation modulate many of the same neural and psychological processes disrupted in PTSD. These commonly affected processes include fear perception and regulation mechanisms, attentional biases (to threat), emotional and autobiographical memory systems, self-referential processing and attachment systems. A conceptual model is proposed whereby culture is considered integral to the development and maintenance of PTSD and its neural substrates. PMID:27302635

Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding.

PubMed

Packard, Pau A; Rodríguez-Fornells, Antoni; Bunzeck, Nico; Nicolás, Berta; de Diego-Balaguer, Ruth; Fuentemilla, Lluís

2017-01-11

As the stream of experience unfolds, our memory system rapidly transforms current inputs into long-lasting meaningful memories. A putative neural mechanism that strongly influences how input elements are transformed into meaningful memory codes relies on the ability to integrate them with existing structures of knowledge or schemas. However, it is not yet clear whether schema-related integration neural mechanisms occur during online encoding. In the current investigation, we examined the encoding-dependent nature of this phenomenon in humans. We showed that actively integrating words with congruent semantic information provided by a category cue enhances memory for words and increases false recall. The memory effect of such active integration with congruent information was robust, even with an interference task occurring right after each encoding word list. In addition, via electroencephalography, we show in 2 separate studies that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. That the neural signals of successful encoding of congruent and incongruent information followed similarly ∼200 ms later suggests that this earlier neural response contributed to memory formation. We propose that the encoding of events that are congruent with readily available contextual semantics can trigger an accelerated onset of the neural mechanisms, supporting the integration of semantic information with the event input. This faster onset would result in a long-lasting and meaningful memory trace for the event but, at the same time, make it difficult to distinguish it from plausible but never encoded events (i.e., related false memories). Conceptual or schema congruence has a strong influence on long-term memory. However, the question of whether schema-related integration neural mechanisms occur during online encoding has yet to be clarified. We investigated the neural mechanisms reflecting how the active integration of words with congruent semantic categories enhances memory for words and increases false recall of semantically related words. We analyzed event-related potentials during encoding and showed that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. Our findings indicate that congruent events can trigger an accelerated onset of neural encoding mechanisms supporting the integration of semantic information with the event input. Copyright © 2017 the authors 0270-6474/17/370291-11$15.00/0.

Artificial neural network intelligent method for prediction

NASA Astrophysics Data System (ADS)

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

2017-09-01

Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

Neural networks within multi-core optic fibers

PubMed Central

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-01-01

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks. PMID:27383911

Neural networks within multi-core optic fibers.

PubMed

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-07-07

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Unique Applications for Artificial Neural Networks. Phase 1

DTIC Science & Technology

1991-08-08

significance. For the VRP, a problem that has received considerable attention in the literature, the new NGO-VRP methodology generates better solutions...represent the stop assignments of each route. The effect of the genetic recombinations is to make simple local exchanges to the relative positions of the...technique for representing a computer-based associative memory [Arbib, 1987]. In our routing system, the basic job of the neural network system is to accept

A Mechanism for Graded, Dynamically Routable Current Propagation in Pulse-Gated Synfire Chains and Implications for Information Coding

PubMed Central

Sornborger, Andrew T.; Wang, Zhuo; Tao, Louis

2015-01-01

Neural oscillations can enhance feature recognition [1], modulate interactions between neurons [2], and improve learning and memory [3]. Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks [4–6]. Unanswered questions are: 1) What is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. We also show that the mechanism is structurally robust in that it may be implemented using biologically realistic pulses. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. We show that the mechanism naturally leads to a framework wherein neural information coding and processing can be considered as a product of linear maps under the active control of a pulse generator. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Using our framework, we construct example neural circuits to 1) maintain a short-term memory, 2) compute time-windowed Fourier transforms, and 3) perform spatial rotations. We postulate that such circuits, with automatic and stereotyped control and processing of information, are the neural correlates of Crick and Koch’s zombie modes. PMID:26227067

Enhancement of electrical signaling in neural networks on graphene films.

PubMed

Tang, Mingliang; Song, Qin; Li, Ning; Jiang, Ziyun; Huang, Rong; Cheng, Guosheng

2013-09-01

One of the key challenges for neural tissue engineering is to exploit supporting materials with robust functionalities not only to govern cell-specific behaviors, but also to form functional neural network. The unique electrical and mechanical properties of graphene imply it as a promising candidate for neural interfaces, but little is known about the details of neural network formation on graphene as a scaffold material for tissue engineering. Therapeutic regenerative strategies aim to guide and enhance the intrinsic capacity of the neurons to reorganize by promoting plasticity mechanisms in a controllable manner. Here, we investigated the impact of graphene on the formation and performance in the assembly of neural networks in neural stem cell (NSC) culture. Using calcium imaging and electrophysiological recordings, we demonstrate the capabilities of graphene to support the growth of functional neural circuits, and improve neural performance and electrical signaling in the network. These results offer a better understanding of interactions between graphene and NSCs, also they clearly present the great potentials of graphene as neural interface in tissue engineering. Copyright © 2013 Elsevier Ltd. All rights reserved.

The neural substrates associated with attentional resources and difficulty of concurrent processing of the two verbal tasks.

PubMed

Mizuno, Kei; Tanaka, Masaaki; Tanabe, Hiroki C; Sadato, Norihiro; Watanabe, Yasuyoshi

2012-07-01

The kana pick-out test has been widely used in Japan to evaluate the ability to divide attention in both adult and pediatric patients. However, the neural substrates underlying the ability to divide attention using the kana pick-out test, which requires participants to pick out individual letters (vowels) in a story while also reading for comprehension, thus requiring simultaneous allocation of attention to both activities, are still unclear. Moreover, outside of the clinical area, neuroimaging studies focused on the mechanisms of divided attention during complex story comprehension are rare. Thus, the purpose of the present study, to clarify the neural substrates of kana pick-out test, improves our current understanding of the basic neural mechanisms of dual task performance in verbal memory function. We compared patterns of activation in the brain obtained during performance of the individual tasks of vowel identification and story comprehension, to levels of activation when participants performed the two tasks simultaneously during the kana pick-out test. We found that activations of the left dorsal inferior frontal gyrus and superior parietal lobule increase in functional connectivity to a greater extent during the dual task condition compared to the two single task conditions. In contrast, activations of the left fusiform gyrus and middle temporal gyrus, which are significantly involved in picking out letters and complex sentences during story comprehension, respectively, were reduced in the dual task condition compared to during the two single task conditions. These results suggest that increased activations of the dorsal inferior frontal gyrus and superior parietal lobule during dual task performance may be associated with the capacity for attentional resources, and reduced activations of the left fusiform gyrus and middle temporal gyrus may reflect the difficulty of concurrent processing of the two tasks. In addition, the increase in synchronization between the left dorsal inferior frontal gyrus and superior parietal lobule in the dual task condition may induce effective communication between these brain regions and contribute to more attentional processing than in the single task condition, due to greater and more complex demands on voluntary attentional resources. Copyright © 2012 Elsevier Ltd. All rights reserved.

Patterned control of human locomotion

PubMed Central

Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

2012-01-01

There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi-segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co-ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost. PMID:22411012

Patterned control of human locomotion.

PubMed

Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

2012-05-15

There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi-segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co-ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost.

Moving Toward Integrative, Multidimensional Research in Modern Psychiatry: Lessons Learned From Fragile X Syndrome.

PubMed

Fung, Lawrence K; Reiss, Allan L

2016-07-15

The field of psychiatry is approaching a major inflection point. The basic science behind cognition, emotion, behavior, and social processes has been advancing rapidly in the past 20 years. However, clinical research supporting the classification system in psychiatry has not kept up with these scientific advances. To begin organizing the basic science of psychiatry in a comprehensive manner, we begin by selecting fragile X syndrome, a neurogenetic disease with cognitive-behavioral manifestations, to illustrate key concepts in an integrative, multidimensional model. Specifically, we describe key genetic and molecular mechanisms (e.g., gamma-aminobutyric acidergic dysfunction and metabotropic glutamate receptor 5-associated long-term depression) relevant to the pathophysiology of fragile X syndrome as well as neural correlates of cognitive-behavioral symptoms. We then describe what we have learned from fragile X syndrome that may be applicable to other psychiatric disorders. We conclude this review by discussing current and future opportunities in diagnosing and treating psychiatric diseases. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Acquisition of Joint Attention by a Developmental Learning Model based on Interactions between a Robot and a Caregiver

NASA Astrophysics Data System (ADS)

Nagai, Yukie; Asada, Minoru; Hosoda, Koh

This paper presents a developmental learning model for joint attention between a robot and a human caregiver. The basic idea of the proposed model comes from the insight of the cognitive developmental science that the development can help the task learning. The model consists of a learning mechanism based on evaluation and two kinds of developmental mechanisms: a robot's development and a caregiver's one. The former means that the sensing and the actuating capabilities of the robot change from immaturity to maturity. On the other hand, the latter is defined as a process that the caregiver changes the task from easy situation to difficult one. These two developments are triggered by the learning progress. The experimental results show that the proposed model can accelerate the learning of joint attention owing to the caregiver's development. Furthermore, it is observed that the robot's development can improve the final task performance by reducing the internal representation in the learned neural network. The mechanisms that bring these effects to the learning are analyzed in line with the cognitive developmental science.

Empathy as a driver of prosocial behaviour: highly conserved neurobehavioural mechanisms across species

PubMed Central

Decety, Jean; Bartal, Inbal Ben-Ami; Uzefovsky, Florina; Knafo-Noam, Ariel

2016-01-01

Empathy reflects the natural ability to perceive and be sensitive to the emotional states of others, coupled with a motivation to care for their well-being. It has evolved in the context of parental care for offspring, as well as within kinship bonds, to help facilitate group living. In this paper, we integrate the perspectives of evolution, animal behaviour, developmental psychology, and social and clinical neuroscience to elucidate our understanding of the proximate mechanisms underlying empathy. We focus, in particular, on processing of signals of distress and need, and their relation to prosocial behaviour. The ability to empathize, both in animals and humans, mediates prosocial behaviour when sensitivity to others' distress is paired with a drive towards their welfare. Disruption or atypical development of the neural circuits that process distress cues and integrate them with decision value leads to callous disregard for others, as is the case in psychopathy. The realization that basic forms of empathy exist in non-human animals is crucial for gaining new insights into the underlying neurobiological and genetic mechanisms of empathy, enabling translation towards therapeutic and pharmacological interventions. PMID:26644596

In vivo imaging of neuronal calcium during electrode implantation: Spatial and temporal mapping of damage and recovery.

PubMed

Eles, James R; Vazquez, Alberto L; Kozai, Takashi D Y; Cui, X Tracy

2018-08-01

Implantable electrode devices enable long-term electrophysiological recordings for brain-machine interfaces and basic neuroscience research. Implantation of these devices, however, leads to neuronal damage and progressive neural degeneration that can lead to device failure. The present study uses in vivo two-photon microscopy to study the calcium activity and morphology of neurons before, during, and one month after electrode implantation to determine how implantation trauma injures neurons. We show that implantation leads to prolonged, elevated calcium levels in neurons within 150 μm of the electrode interface. These neurons show signs of mechanical distortion and mechanoporation after implantation, suggesting that calcium influx is related to mechanical trauma. Further, calcium-laden neurites develop signs of axonal injury at 1-3 h post-insert. Over the first month after implantation, physiological neuronal calcium activity increases, suggesting that neurons may be recovering. By defining the mechanisms of neuron damage after electrode implantation, our results suggest new directions for therapies to improve electrode longevity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Brainstem mechanisms underlying the cough reflex and its regulation.

PubMed

Mutolo, Donatella

2017-09-01

Cough is a very important airway protective reflex. Cough-related inputs are conveyed to the caudal nucleus tractus solitarii (cNTS) that projects to the brainstem respiratory network. The latter is reconfigured to generate the cough motor pattern. A high degree of modulation is exerted on second-order neurons and the brainstem respiratory network by sensory inputs and higher brain areas. Two medullary structures proved to have key functions in cough production and to be strategic sites of action for centrally active drugs: the cNTS and the caudal ventral respiratory group (cVRG). Drugs microinjected into these medullary structures caused downregulation or upregulation of the cough reflex. The results suggest that inhibition and disinhibition are prominent regulatory mechanisms of this reflex and that both the cNTS and the cVRG are essential in the generation of the entire cough motor pattern. Studies on the basic neural mechanisms subserving the cough reflex may provide hints for novel therapeutic approaches. Different proposals for further investigations are advanced. Copyright © 2017 Elsevier B.V. All rights reserved.

Hitting a Moving Target: Basic Mechanisms of Recovery from Acquired Developmental Brain Injury

PubMed Central

Giza, Christopher C.; Kolb, Bryan; Harris, Neil G.; Asarnow, Robert F.; Prins, Mayumi L.

2009-01-01

Acquired brain injuries represent a major cause of disability in the pediatric population. Understanding responses to developmental acquired brain injuries requires knowledge of the neurobiology of normal development, age-at-injury effects and experience-dependent neuroplasticity. In the developing brain, full recovery cannot be considered as a return to the premorbid baseline, since ongoing maturation means that cerebral functioning in normal individuals will continue to advance. Thus, the recovering immature brain has to ‘hit a moving target’ to achieve full functional recovery, defined as parity with age-matched uninjured peers. This review will discuss the consequences of developmental injuries such as focal lesions, diffuse hypoxia and traumatic brain injury (TBI). Underlying cellular and physiological mechanisms relevant to age-at-injury effects will be described in considerable detail, including but not limited to alterations in neurotransmission, connectivity/network functioning, the extracellular matrix, response to oxidative stress and changes in cerebral metabolism. Finally, mechanisms of experience-dependent plasticity will be reviewed in conjunction with their effects on neural repair and recovery. PMID:19956795

Neural Vascular Mechanism for the Cerebral Blood Flow Autoregulation after Hemorrhagic Stroke.

PubMed

Xiao, Ming; Li, Qiang; Feng, Hua; Zhang, Le; Chen, Yujie

2017-01-01

During the initial stages of hemorrhagic stroke, including intracerebral hemorrhage and subarachnoid hemorrhage, the reflex mechanisms are activated to protect cerebral perfusion, but secondary dysfunction of cerebral flow autoregulation will eventually reduce global cerebral blood flow and the delivery of metabolic substrates, leading to generalized cerebral ischemia, hypoxia, and ultimately, neuronal cell death. Cerebral blood flow is controlled by various regulatory mechanisms, including prevailing arterial pressure, intracranial pressure, arterial blood gases, neural activity, and metabolic demand. Evoked by the concept of vascular neural network, the unveiled neural vascular mechanism gains more and more attentions. Astrocyte, neuron, pericyte, endothelium, and so forth are formed as a communicate network to regulate with each other as well as the cerebral blood flow. However, the signaling molecules responsible for this communication between these new players and blood vessels are yet to be definitively confirmed. Recent evidence suggested the pivotal role of transcriptional mechanism, including but not limited to miRNA, lncRNA, exosome, and so forth, for the cerebral blood flow autoregulation. In the present review, we sought to summarize the hemodynamic changes and underline neural vascular mechanism for cerebral blood flow autoregulation in stroke-prone state and after hemorrhagic stroke and hopefully provide more systematic and innovative research interests for the pathophysiology and therapeutic strategies of hemorrhagic stroke.

Neural mechanisms of the mind, Aristotle, Zadeh, and fMRI.

PubMed

Perlovsky, Leonid I

2010-05-01

Processes in the mind: perception, cognition, concepts, instincts, emotions, and higher cognitive abilities for abstract thinking, beautiful music are considered here within a neural modeling fields (NMFs) paradigm. Its fundamental mathematical mechanism is a process "from vague-fuzzy to crisp," called dynamic logic (DL). This paper discusses why this paradigm is necessary mathematically, and relates it to a psychological description of the mind. Surprisingly, the process from "vague to crisp" corresponds to Aristotelian understanding of mental functioning. Recent functional magnetic resonance imaging (fMRI) measurements confirmed this process in neural mechanisms of perception.

Folate receptor 1 is necessary for neural plate cell apical constriction during Xenopus neural tube formation

PubMed Central

Balashova, Olga A.; Visina, Olesya

2017-01-01

Folate supplementation prevents up to 70% of neural tube defects (NTDs), which result from a failure of neural tube closure during embryogenesis. The elucidation of the mechanisms underlying folate action has been challenging. This study introduces Xenopus laevis as a model to determine the cellular and molecular mechanisms involved in folate action during neural tube formation. We show that knockdown of folate receptor 1 (Folr1; also known as FRα) impairs neural tube formation and leads to NTDs. Folr1 knockdown in neural plate cells only is necessary and sufficient to induce NTDs. Folr1-deficient neural plate cells fail to constrict, resulting in widening of the neural plate midline and defective neural tube closure. Pharmacological inhibition of folate action by methotrexate during neurulation induces NTDs by inhibiting folate interaction with its uptake systems. Our findings support a model in which the folate receptor interacts with cell adhesion molecules, thus regulating the apical cell membrane remodeling and cytoskeletal dynamics necessary for neural plate folding. Further studies in this organism could unveil novel cellular and molecular events mediated by folate and lead to new ways of preventing NTDs. PMID:28255006

The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder.

PubMed

Liddell, Belinda J; Jobson, Laura

2016-01-01

A significant body of literature documents the neural mechanisms involved in the development and maintenance of posttraumatic stress disorder (PTSD). However, there is very little empirical work considering the influence of culture on these underlying mechanisms. Accumulating cultural neuroscience research clearly indicates that cultural differences in self-representation modulate many of the same neural processes proposed to be aberrant in PTSD. The objective of this review paper is to consider how culture may impact on the neural mechanisms underlying PTSD. We first outline five key affective and cognitive functions and their underlying neural correlates that have been identified as being disrupted in PTSD: (1) fear dysregulation; (2) attentional biases to threat; (3) emotion and autobiographical memory; (4) self-referential processing; and (5) attachment and interpersonal processing. Second, we consider prominent cultural theories and review the empirical research that has demonstrated the influence of cultural variations in self-representation on the neural substrates of these same five affective and cognitive functions. Finally, we propose a conceptual model that suggests that these five processes have major relevance to considering how culture may influence the neural processes underpinning PTSD.

The Mammalian Diving Response: An Enigmatic Reflex to Preserve Life?

PubMed Central

2013-01-01

The mammalian diving response is a remarkable behavior that overrides basic homeostatic reflexes. It is most studied in large aquatic mammals but is seen in all vertebrates. Pelagic mammals have developed several physiological adaptations to conserve intrinsic oxygen stores, but the apnea, bradycardia, and vasoconstriction is shared with those terrestrial and is neurally mediated. The adaptations of aquatic mammals are reviewed here as well as the neural control of cardiorespiratory physiology during diving in rodents. PMID:23997188

[Progress of researches on mechanism of acupuncture therapy underlying improvement of acute cerebral hemorrhage].

PubMed

Wang, Fan; Wang, Hai-qiao; Dong, Gui-rong

2011-04-01

In the present paper, the authors review the progress of researches on the mechanism of acupuncture therapy underlying improvement of acute cerebral hemorrhage from experimental studies and research methods. The effects of acupuncture intervention mainly involve (1) lessening inflammatory reactions, (2) reducing impairment of free radicals and excitatory amino acids on cerebral neurons, (3) balancing release of vascular bioactive substances to increase regional cerebral blood flow, and (4) promoting repair and regeneration of the neural tissue, etc. In regard to the research methods, many new biological techniques such as biological molecular approaches, neuro-cellular chemical methods, reverse transcription-polymerase chain reaction (RT-PCR) or quantitative real time-PCR, situ hybridization, western blotting, electron microscope, etc., have been extensively applied to researches on the underlying mechanism of acupuncture therapy for cerebral infarction. In addition, the authors also pointed out that in spite of achieving some bigger progresses in experimental studies, most of the results basically reflect static, isolated and regional changes rather than dynamic and whole body changes. For this reason, more vivo research techniques and noninvasive research methods are highly recommended to be used in the future research on the underlying mechanisms of acupuncture therapy for acute cerebral ischemia.

Identification and characterization of secondary neural tube-derived embryonic neural stem cells in vitro.

PubMed

Shaker, Mohammed R; Kim, Joo Yeon; Kim, Hyun; Sun, Woong

2015-05-15

Secondary neurulation is an embryonic progress that gives rise to the secondary neural tube, the precursor of the lower spinal cord region. The secondary neural tube is derived from aggregated Sox2-expressing neural cells at the dorsal region of the tail bud, which eventually forms rosette or tube-like structures to give rise to neural tissues in the tail bud. We addressed whether the embryonic tail contains neural stem cells (NSCs), namely secondary NSCs (sNSCs), with the potential for self-renewal in vitro. Using in vitro neurosphere assays, neurospheres readily formed at the rosette and neural-tube levels, but less frequently at the tail bud tip level. Furthermore, we identified that sNSC-generated neurospheres were significantly smaller in size compared with cortical neurospheres. Interestingly, various cell cycle analyses revealed that this difference was not due to a reduction in the proliferation rate of NSCs, but rather the neuronal commitment of sNSCs, as sNSC-derived neurospheres contain more committed neuronal progenitor cells, even in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). These results suggest that the higher tendency for sNSCs to spontaneously differentiate into progenitor cells may explain the limited expansion of the secondary neural tube during embryonic development.

Dissociative States and Neural Complexity

ERIC Educational Resources Information Center

Bob, Petr; Svetlak, Miroslav

2011-01-01

Recent findings indicate that neural mechanisms of consciousness are related to integration of distributed neural assemblies. This neural integration is particularly vulnerable to past stressful experiences that can lead to disintegration and dissociation of consciousness. These findings suggest that dissociation could be described as a level of…

Neural synchronization as a hypothetical explanation of the psychoanalytic unconscious.

PubMed

Ceylan, Mehmet Emin; Dönmez, Aslıhan; Ünsalver, Barış Önen; Evrensel, Alper

2016-02-01

Cognitive scientists have tried to explain the neural mechanisms of unconscious mental states such as coma, epileptic seizures, and anesthesia-induced unconsciousness. However these types of unconscious states are different from the psychoanalytic unconscious. In this review, we aim to present our hypothesis about the neural correlates underlying psychoanalytic unconscious. To fulfill this aim, we firstly review the previous explanations about the neural correlates of conscious and unconscious mental states, such as brain oscillations, synchronicity of neural networks, and cognitive binding. By doing so, we hope to lay a neuroscientific ground for our hypothesis about neural correlates of psychoanalytic unconscious; parallel but unsynchronized neural networks between different layers of consciousness and unconsciousness. Next, we propose a neuroscientific mechanism about how the repressed mental events reach the conscious awareness; the lock of neural synchronization between two mental layers of conscious and unconscious. At the last section, we will discuss the data about schizophrenia as a clinical example of our proposed hypothesis. Copyright © 2015 Elsevier Inc. All rights reserved.

On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

PubMed

Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-10-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Stimulation of dopamine D₁ receptor improves learning capacity in cooperating cleaner fish.

PubMed

Messias, João P M; Santos, Teresa P; Pinto, Maria; Soares, Marta C

2016-01-27

Accurate contextual decision-making strategies are important in social environments. Specific areas in the brain are tasked to process these complex interactions and generate correct follow-up responses. The dorsolateral and dorsomedial parts of the telencephalon in the teleost fish brain are neural substrates modulated by the neurotransmitter dopamine (DA), and are part of an important neural circuitry that drives animal behaviour from the most basic actions such as learning to search for food, to properly choosing partners and managing decisions based on context. The Indo-Pacific cleaner wrasse Labroides dimidiatus is a highly social teleost fish species with a complex network of interactions with its 'client' reef fish. We asked if changes in DA signalling would affect individual learning ability by presenting cleaner fish two ecologically different tasks that simulated a natural situation requiring accurate decision-making. We demonstrate that there is an involvement of the DA system and D1 receptor pathways on cleaners' natural abilities to learn both tasks. Our results add significantly to the growing literature on the physiological mechanisms that underlie and facilitate the expression of cooperative abilities. © 2016 The Author(s).

Quantitative prediction of perceptual decisions during near-threshold fear detection

NASA Astrophysics Data System (ADS)

Pessoa, Luiz; Padmala, Srikanth

2005-04-01

A fundamental goal of cognitive neuroscience is to explain how mental decisions originate from basic neural mechanisms. The goal of the present study was to investigate the neural correlates of perceptual decisions in the context of emotional perception. To probe this question, we investigated how fluctuations in functional MRI (fMRI) signals were correlated with behavioral choice during a near-threshold fear detection task. fMRI signals predicted behavioral choice independently of stimulus properties and task accuracy in a network of brain regions linked to emotional processing: posterior cingulate cortex, medial prefrontal cortex, right inferior frontal gyrus, and left insula. We quantified the link between fMRI signals and behavioral choice in a whole-brain analysis by determining choice probabilities by means of signal-detection theory methods. Our results demonstrate that voxel-wise fMRI signals can reliably predict behavioral choice in a quantitative fashion (choice probabilities ranged from 0.63 to 0.78) at levels comparable to neuronal data. We suggest that the conscious decision that a fearful face has been seen is represented across a network of interconnected brain regions that prepare the organism to appropriately handle emotionally challenging stimuli and that regulate the associated emotional response. decision making | emotion | functional MRI

On Initial Brain Activity Mapping of Associative Memory Code in the Hippocampus

PubMed Central

Tsien, Joe Z.; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Lei Wang, Phillip; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-01-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. PMID:23838072

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network.

PubMed

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

2016-11-10

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

Neural Plasticity and Proliferation in the Generation of Antidepressant Effects: Hippocampal Implication

PubMed Central

Pilar-Cuéllar, Fuencisla; Vidal, Rebeca; Díaz, Alvaro; Castro, Elena; dos Anjos, Severiano; Pascual-Brazo, Jesús; Linge, Raquel; Vargas, Veronica; Blanco, Helena; Martínez-Villayandre, Beatriz; Pazos, Ángel; Valdizán, Elsa M.

2013-01-01

It is widely accepted that changes underlying depression and antidepressant-like effects involve not only alterations in the levels of neurotransmitters as monoamines and their receptors in the brain, but also structural and functional changes far beyond. During the last two decades, emerging theories are providing new explanations about the neurobiology of depression and the mechanism of action of antidepressant strategies based on cellular changes at the CNS level. The neurotrophic/plasticity hypothesis of depression, proposed more than a decade ago, is now supported by multiple basic and clinical studies focused on the role of intracellular-signalling cascades that govern neural proliferation and plasticity. Herein, we review the state-of-the-art of the changes in these signalling pathways which appear to underlie both depressive disorders and antidepressant actions. We will especially focus on the hippocampal cellularity and plasticity modulation by serotonin, trophic factors as brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor (VEGF) through intracellular signalling pathways—cAMP, Wnt/β-catenin, and mTOR. Connecting the classic monoaminergic hypothesis with proliferation/neuroplasticity-related evidence is an appealing and comprehensive attempt for improving our knowledge about the neurobiological events leading to depression and associated to antidepressant therapies. PMID:23862076

Compensatory recruitment of neural resources in chronic alcoholism.

PubMed

Chanraud, Sandra; Sullivan, Edith V

2014-01-01

Functional recovery occurs with sustained sobriety, but the neural mechanisms enabling recovery are only now emerging. Theories about promising mechanisms involve concepts of neuroadaptation, where excessive alcohol consumption results in untoward structural and functional brain changes which are subsequently candidates for reversal with sobriety. Views on functional adaptation in chronic alcoholism have expanded with results from neuroimaging studies. Here, we first describe and define the concept of neuroadaptation according to emerging theories based on the growing literature in aging-related cognitive functioning. Then we describe findings as they apply to chronic alcoholism and factors that could influence compensation, such as functional brain reserve and the integrity of brain structure. Finally, we review brain plasticity based on physiologic mechanisms that could underlie mechanisms of neural compensation. Where possible, we provide operational criteria to define functional and neural compensation. © 2014 Elsevier B.V. All rights reserved.

Music and speech listening enhance the recovery of early sensory processing after stroke.

PubMed

Särkämö, Teppo; Pihko, Elina; Laitinen, Sari; Forsblom, Anita; Soinila, Seppo; Mikkonen, Mikko; Autti, Taina; Silvennoinen, Heli M; Erkkilä, Jaakko; Laine, Matti; Peretz, Isabelle; Hietanen, Marja; Tervaniemi, Mari

2010-12-01

Our surrounding auditory environment has a dramatic influence on the development of basic auditory and cognitive skills, but little is known about how it influences the recovery of these skills after neural damage. Here, we studied the long-term effects of daily music and speech listening on auditory sensory memory after middle cerebral artery (MCA) stroke. In the acute recovery phase, 60 patients who had middle cerebral artery stroke were randomly assigned to a music listening group, an audio book listening group, or a control group. Auditory sensory memory, as indexed by the magnetic MMN (MMNm) response to changes in sound frequency and duration, was measured 1 week (baseline), 3 months, and 6 months after the stroke with whole-head magnetoencephalography recordings. Fifty-four patients completed the study. Results showed that the amplitude of the frequency MMNm increased significantly more in both music and audio book groups than in the control group during the 6-month poststroke period. In contrast, the duration MMNm amplitude increased more in the audio book group than in the other groups. Moreover, changes in the frequency MMNm amplitude correlated significantly with the behavioral improvement of verbal memory and focused attention induced by music listening. These findings demonstrate that merely listening to music and speech after neural damage can induce long-term plastic changes in early sensory processing, which, in turn, may facilitate the recovery of higher cognitive functions. The neural mechanisms potentially underlying this effect are discussed.

Somatoform Pain: A developmental theory and translational research review

PubMed Central

Landa, Alla; Peterson, Bradley S.; Fallon, Brian A.

2013-01-01

Somatoform pain is a highly prevalent, debilitating condition and a tremendous public health problem. Effective treatments for somatoform pain are urgently needed. The etiology of this condition is, however, still unknown. On the basis of a review of recent basic and clinical research, we propose one potential mechanisms of symptom formation in somatoform pain and a developmental theory of its pathogenesis. The emerging evidence from animal and human studies in developmental neurobiology, cognitive-affective neuroscience, psychoneuroimmunology, genetics, epigenetics, and clinical and treatment studies of somatoform pain all point to the existence of a shared physical and social pain neural system. Research findings also show that non-optimal early experiences interact with genetic predispositions to influence the development of this shared system and ability to regulate it in an effective way. Interpersonal affect regulation between infant and caregiver is crucial for the optimal development of these brain circuits. The aberrant development of this shared neural system during infancy, childhood and adolescence, therefore, may ultimately lead to an increased sensitivity to physical and social pain and to problems with their regulation in adulthood. The authors critically review translational research findings that support this theory and discuss its clinical and research implications. Specifically, the proposed theory and reviewed research suggest that psychotherapeutic and/or pharmacologic interventions that foster the development of affect regulation capacities in an interpersonal context will also serve to more effectively modulate aberrantly activated neural pain circuits and thus be of particular benefit in the treatment of somatoform pain. PMID:22929064

β-Catenin Up-regulates Atoh1 Expression in Neural Progenitor Cells by Interaction with an Atoh1 3′ Enhancer*

PubMed Central

Shi, Fuxin; Cheng, Yen-fu; Wang, Xiaohui L.; Edge, Albert S. B.

2010-01-01

Atoh1, a basic helix-loop-helix transcription factor, plays a critical role in the differentiation of several epithelial and neural cell types. We found that β-catenin, the key mediator of the canonical Wnt pathway, increased expression of Atoh1 in mouse neuroblastoma cells and neural progenitor cells, and baseline Atoh1 expression was decreased by siRNA directed at β-catenin. The up-regulation of Atoh1 was caused by an interaction of β-catenin with the Atoh1 enhancer that could be demonstrated by chromatin immunoprecipitation. We found that two putative Tcf-Lef sites in the 3′ enhancer of the Atoh1 gene displayed an affinity for β-catenin and were critical for the activation of Atoh1 transcription because mutation of either site decreased expression of a reporter gene downstream of the enhancer. Tcf-Lef co-activators were found in the complex that bound to these sites in the DNA together with β-catenin. Inhibition of Notch signaling, which has previously been shown to induce bHLH transcription factor expression, increased β-catenin expression in progenitor cells of the nervous system. Because this could be a mechanism for up-regulation of Atoh1 after inhibition of Notch, we tested whether siRNA to β-catenin prevented the increase in Atoh1 and found that β-catenin expression was required for increased expression of Atoh1 after Notch inhibition. PMID:19864427

New Insights on Neurobiological Mechanisms underlying Alcohol Addiction

PubMed Central

Cui, Changhai; Noronha, Antonio; Morikawa, Hitoshi; Alvarez, Veronica A.; Stuber, Garret D.; Szumlinski, Karen K.; Kash, Thomas L.; Roberto, Marisa; Wilcox, Mark V.

2012-01-01

Alcohol dependence/addiction is mediated by complex neural mechanisms that involve multiple brain circuits and neuroadaptive changes in a variety of neurotransmitter and neuropeptide systems. Although recent studies have provided substantial information on the neurobiological mechanisms that drive alcohol drinking behavior, significant challenges remain in understanding how alcohol-induced neuroadaptations occur and how different neurocircuits and pathways cross-talk. This review article highlights recent progress in understanding neural mechanisms of alcohol addiction from the perspectives of the development and maintenance of alcohol dependence. It provides insights on cross talks of different mechanisms and reviews the latest studies on metaplasticity, structural plasticity, interface of reward and stress pathways, and cross-talk of different neural signaling systems involved in binge-like drinking and alcohol dependence. PMID:23159531

[Reading and writing Japanese: Kanji versus Kana].

PubMed

Kawamura, Mitsuru

2006-11-01

In my talk, I reviewed studies on the neural substrates of Kanji vs. Kana, two types of Japanese characters, written since the 1980s. More Specifically, I reviewed the development of the studies on (1) Kanji and Kana in pure alexia/agraphia, (2) alexia with agraphia of Kanji and (3) 'musical letters' vs. 'literary letters', and reported new findings from those studies. In the 1980s, we frequently studied patients with partial callosal lesions and those with pure alexia, and many of the studies were on the neural substrates of Kanji vs. Kana. Later, we discovered cases of alexia with agraphia of Kanji caused by lesions in the posterior part of the left inferior temporal gyrus, leading us to understand the neural substrates of Kanji and Kana in more detail. In addition to the reading and writing of 'literary letters', we studied the neural mechanisms of the reading and writing of 'musical letters', i.e. musical scores. Our study showed that the neural mechanisms of reading and writing musical scores were similar to those of reading and writing 'literary letters' in professional musicians, although those neural mechanisms varied slightly.

Electrical coupling in ensembles of nonexcitable cells: modeling the spatial map of single cell potentials.

PubMed

Cervera, Javier; Manzanares, Jose Antonio; Mafe, Salvador

2015-02-19

We analyze the coupling of model nonexcitable (non-neural) cells assuming that the cell membrane potential is the basic individual property. We obtain this potential on the basis of the inward and outward rectifying voltage-gated channels characteristic of cell membranes. We concentrate on the electrical coupling of a cell ensemble rather than on the biochemical and mechanical characteristics of the individual cells, obtain the map of single cell potentials using simple assumptions, and suggest procedures to collectively modify this spatial map. The response of the cell ensemble to an external perturbation and the consequences of cell isolation, heterogeneity, and ensemble size are also analyzed. The results suggest that simple coupling mechanisms can be significant for the biophysical chemistry of model biomolecular ensembles. In particular, the spatiotemporal map of single cell potentials should be relevant for the uptake and distribution of charged nanoparticles over model cell ensembles and the collective properties of droplet networks incorporating protein ion channels inserted in lipid bilayers.

Toward a general psychological model of tension and suspense

PubMed Central

Lehne, Moritz; Koelsch, Stefan

2015-01-01

Tension and suspense are powerful emotional experiences that occur in a wide variety of contexts (e.g., in music, film, literature, and everyday life). The omnipresence of tension and suspense suggests that they build on very basic cognitive and affective mechanisms. However, the psychological underpinnings of tension experiences remain largely unexplained, and tension and suspense are rarely discussed from a general, domain-independent perspective. In this paper, we argue that tension experiences in different contexts (e.g., musical tension or suspense in a movie) build on the same underlying psychological processes. We discuss key components of tension experiences and propose a domain-independent model of tension and suspense. According to this model, tension experiences originate from states of conflict, instability, dissonance, or uncertainty that trigger predictive processes directed at future events of emotional significance. We also discuss possible neural mechanisms underlying tension and suspense. The model provides a theoretical framework that can inform future empirical research on tension phenomena. PMID:25717309

Cognitive neuroscience of cognitive retraining for addiction medicine: From mediating mechanisms to questions of efficacy.

PubMed

Gladwin, Thomas E; Wiers, Corinde E; Wiers, Reinout W

2016-01-01

Cognitive retraining or cognitive bias modification (CBM) involves having subjects repeatedly perform a computerized task designed to reduce the impact of automatic processes that lead to harmful behavior. We first discuss the theory underlying CBM and provide a brief overview of important research progress in its application to addiction. We then focus on cognitive- and neural-mediating mechanisms. We consider recent criticism of both CBM and its theoretical foundations. Evaluations of CBM could benefit from considering theory-driven factors that may determine variations in efficacy, such as motivation. Concerning theory, while there is certainly room for fundamental advances in current models, we argue that the basic view of impulsive behavior and its control remains a useful and productive heuristic. Finally, we briefly discuss some interesting new directions for CBM research: enhancement of training via transcranial direct current stimulation, online training, and gamification, i.e., the use of gameplay elements to increase motivation. © 2016 Elsevier B.V. All rights reserved.

Oscillatory mechanisms of process binding in memory.

PubMed

Klimesch, Wolfgang; Freunberger, Roman; Sauseng, Paul

2010-06-01

A central topic in cognitive neuroscience is the question, which processes underlie large scale communication within and between different neural networks. The basic assumption is that oscillatory phase synchronization plays an important role for process binding--the transient linking of different cognitive processes--which may be considered a special type of large scale communication. We investigate this question for memory processes on the basis of different types of oscillatory synchronization mechanisms. The reviewed findings suggest that theta and alpha phase coupling (and phase reorganization) reflect control processes in two large memory systems, a working memory and a complex knowledge system that comprises semantic long-term memory. It is suggested that alpha phase synchronization may be interpreted in terms of processes that coordinate top-down control (a process guided by expectancy to focus on relevant search areas) and access to memory traces (a process leading to the activation of a memory trace). An analogous interpretation is suggested for theta oscillations and the controlled access to episodic memories. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Toward a general psychological model of tension and suspense.

PubMed

Lehne, Moritz; Koelsch, Stefan

2015-01-01

Tension and suspense are powerful emotional experiences that occur in a wide variety of contexts (e.g., in music, film, literature, and everyday life). The omnipresence of tension and suspense suggests that they build on very basic cognitive and affective mechanisms. However, the psychological underpinnings of tension experiences remain largely unexplained, and tension and suspense are rarely discussed from a general, domain-independent perspective. In this paper, we argue that tension experiences in different contexts (e.g., musical tension or suspense in a movie) build on the same underlying psychological processes. We discuss key components of tension experiences and propose a domain-independent model of tension and suspense. According to this model, tension experiences originate from states of conflict, instability, dissonance, or uncertainty that trigger predictive processes directed at future events of emotional significance. We also discuss possible neural mechanisms underlying tension and suspense. The model provides a theoretical framework that can inform future empirical research on tension phenomena.

Integrating the automatic and the controlled: Strategies in Semantic Priming in an Attractor Network with Latching Dynamics

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2014-01-01

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we have introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. PMID:24890261

Putting together phylogenetic and ontogenetic perspectives on empathy.

PubMed

Decety, Jean; Svetlova, Margarita

2012-01-01

The ontogeny of human empathy is better understood with reference to the evolutionary history of the social brain. Empathy has deep evolutionary, biochemical, and neurological underpinnings. Even the most advanced forms of empathy in humans are built on more basic forms and remain connected to core mechanisms associated with affective communication, social attachment, and parental care. In this paper, we argue that it is essential to consider empathy within a neurodevelopmental framework that recognizes both the continuities and changes in socioemotional understanding from infancy to adulthood. We bring together neuroevolutionary and developmental perspectives on the information processing and neural mechanisms underlying empathy and caring, and show that they are grounded in multiple interacting systems and processes. Moreover, empathy in humans is assisted by other abstract and domain-general high-level cognitive abilities such as executive functions, mentalizing and language, as well as the ability to differentiate another's mental states from one's own, which expand the range of behaviors that can be driven by empathy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neurophysiological Studies of Auditory Verbal Hallucinations

PubMed Central

Ford, Judith M.; Dierks, Thomas; Fisher, Derek J.; Herrmann, Christoph S.; Hubl, Daniela; Kindler, Jochen; Koenig, Thomas; Mathalon, Daniel H.; Spencer, Kevin M.; Strik, Werner; van Lutterveld, Remko

2012-01-01

We discuss 3 neurophysiological approaches to study auditory verbal hallucinations (AVH). First, we describe “state” (or symptom capture) studies where periods with and without hallucinations are compared “within” a patient. These studies take 2 forms: passive studies, where brain activity during these states is compared, and probe studies, where brain responses to sounds during these states are compared. EEG (electroencephalography) and MEG (magnetoencephalography) data point to frontal and temporal lobe activity, the latter resulting in competition with external sounds for auditory resources. Second, we discuss “trait” studies where EEG and MEG responses to sounds are recorded from patients who hallucinate and those who do not. They suggest a tendency to hallucinate is associated with competition for auditory processing resources. Third, we discuss studies addressing possible mechanisms of AVH, including spontaneous neural activity, abnormal self-monitoring, and dysfunctional interregional communication. While most studies show differences in EEG and MEG responses between patients and controls, far fewer show symptom relationships. We conclude that efforts to understand the pathophysiology of AVH using EEG and MEG have been hindered by poor anatomical resolution of the EEG and MEG measures, poor assessment of symptoms, poor understanding of the phenomenon, poor models of the phenomenon, decoupling of the symptoms from the neurophysiology due to medications and comorbidites, and the possibility that the schizophrenia diagnosis breeds truer than the symptoms it comprises. These problems are common to studies of other psychiatric symptoms and should be considered when attempting to understand the basic neural mechanisms responsible for them. PMID:22368236

A case for human systems neuroscience.

PubMed

Gardner, J L

2015-06-18

Can the human brain itself serve as a model for a systems neuroscience approach to understanding the human brain? After all, how the brain is able to create the richness and complexity of human behavior is still largely mysterious. What better choice to study that complexity than to study it in humans? However, measurements of brain activity typically need to be made non-invasively which puts severe constraints on what can be learned about the internal workings of the brain. Our approach has been to use a combination of psychophysics in which we can use human behavioral flexibility to make quantitative measurements of behavior and link those through computational models to measurements of cortical activity through magnetic resonance imaging. In particular, we have tested various computational hypotheses about what neural mechanisms could account for behavioral enhancement with spatial attention (Pestilli et al., 2011). Resting both on quantitative measurements and considerations of what is known through animal models, we concluded that weighting of sensory signals by the magnitude of their response is a neural mechanism for efficient selection of sensory signals and consequent improvements in behavioral performance with attention. While animal models have many technical advantages over studying the brain in humans, we believe that human systems neuroscience should endeavor to validate, replicate and extend basic knowledge learned from animal model systems and thus form a bridge to understanding how the brain creates the complex and rich cognitive capacities of humans. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Spatiotemporal properties of microsaccades: Model predictions and experimental tests

NASA Astrophysics Data System (ADS)

Zhou, Jian-Fang; Yuan, Wu-Jie; Zhou, Zhao

2016-10-01

Microsaccades are involuntary and very small eye movements during fixation. Recently, the microsaccade-related neural dynamics have been extensively investigated both in experiments and by constructing neural network models. Experimentally, microsaccades also exhibit many behavioral properties. It’s well known that the behavior properties imply the underlying neural dynamical mechanisms, and so are determined by neural dynamics. The behavioral properties resulted from neural responses to microsaccades, however, are not yet understood and are rarely studied theoretically. Linking neural dynamics to behavior is one of the central goals of neuroscience. In this paper, we provide behavior predictions on spatiotemporal properties of microsaccades according to microsaccade-induced neural dynamics in a cascading network model, which includes both retinal adaptation and short-term depression (STD) at thalamocortical synapses. We also successfully give experimental tests in the statistical sense. Our results provide the first behavior description of microsaccades based on neural dynamics induced by behaving activity, and so firstly link neural dynamics to behavior of microsaccades. These results indicate strongly that the cascading adaptations play an important role in the study of microsaccades. Our work may be useful for further investigations of the microsaccadic behavioral properties and of the underlying neural dynamical mechanisms responsible for the behavioral properties.

Speed control: cogs and gears that drive the circadian clock.

PubMed

Zheng, Xiangzhong; Sehgal, Amita

2012-09-01

In most organisms, an intrinsic circadian (~24-h) timekeeping system drives rhythms of physiology and behavior. Within cells that contain a circadian clock, specific transcriptional activators and repressors reciprocally regulate each other to generate a basic molecular oscillator. A mismatch of the period generated by this oscillator with the external environment creates circadian disruption, which can have adverse effects on neural function. Although several clock genes have been extensively characterized, a fundamental question remains: how do these genes work together to generate a ~24-h period? Period-altering mutations in clock genes can affect any of multiple regulated steps in the molecular oscillator. In this review, we examine the regulatory mechanisms that contribute to setting the pace of the circadian oscillator. Copyright © 2012 Elsevier Ltd. All rights reserved.

Intergroup differences in the sharing of emotive states: neural evidence of an empathy gap.

PubMed

Gutsell, Jennifer N; Inzlicht, Michael

2012-06-01

Empathy facilitates prosocial behavior and social understanding. Here, however, we suggest that the most basic mechanism of empathy--the intuitive sharing of other's emotional and motivational states--is limited to those we like. Measuring electroencephalographic (EEG) alpha oscillations as people observed ingroup vs outgroup members, we found that participants showed similar activation patterns when feeling sad as when they observed ingroup members feeling sad. In contrast, participants did not show these same activation patterns when observing outgroup members and even less so the more they were prejudiced. These findings provide evidence from brain activity for an ingroup bias in empathy: empathy may be restricted to close others and, without active effort, may not extend to outgroups, potentially making them likely targets for prejudice and discrimination.

In vivo robotics: the automation of neuroscience and other intact-system biological fields

PubMed Central

Kodandaramaiah, Suhasa B.; Boyden, Edward S.; Forest, Craig R.

2013-01-01

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to impact neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience, and present a concrete example with our recent automation of in vivo whole cell patch clamp electrophysiology of neurons in the living mouse brain. PMID:23841584

The Development of Eating Behavior - Biology and Context

PubMed Central

Gahagan, Sheila

2012-01-01

Eating is necessary for survival, gives great pleasure and can be perturbed leading to undernutrition, overnutrition and eating disorders. The development of feeding in humans relies on complex interplay between homeostatic mechanisms; neural reward systems; and child motor, sensory and socio-emotional capability. Furthermore, parenting, social influences and the food environment influence the development of eating behavior. The rapid expansion of new knowledge in this field, from basic science to clinical and community-based research, is expected to lead to urgently needed research in support of effective, evidence-based prevention and treatment strategies for undernutrition, overnutrition and eating disorders in early childhood. Using a biopsychosocial approach, this review covers current knowledge of the development of eating behavior from the brain to the individual child, taking into account important contextual influences. PMID:22472944

Cognitive Neuroscience Approaches to Understanding Behavior Change in Alcohol Use Disorder Treatments.

PubMed

Naqvi, Nasir H; Morgenstern, Jon

2015-01-01

Researchers have begun to apply cognitive neuroscience concepts and methods to study behavior change mechanisms in alcohol use disorder (AUD) treatments. This review begins with an examination of the current state of treatment mechanisms research using clinical and social psychological approaches. It then summarizes what is currently understood about the pathophysiology of addiction from a cognitive neuroscience perspective. Finally, it reviews recent efforts to use cognitive neuroscience approaches to understand the neural mechanisms of behavior change in AUD, including studies that use neural functioning to predict relapse and abstinence; studies examining neural mechanisms that operate in current evidence-based behavioral interventions for AUD; as well as research on novel behavioral interventions that are being derived from our emerging understanding of the neural and cognitive mechanisms of behavior change in AUD. The article highlights how the regulation of subcortical regions involved in alcohol incentive motivation by prefrontal cortical regions involved in cognitive control may be a core mechanism that plays a role in these varied forms of behavior change in AUD. We also lay out a multilevel framework for integrating cognitive neuroscience approaches with more traditional methods for examining AUD treatment mechanisms.

The Neural Basis of Recollection Rejection: Increases in Hippocampal-Prefrontal Connectivity in the Absence of a Shared Recall-to-Reject and Target Recollection Network.

PubMed

Bowman, Caitlin R; Dennis, Nancy A

2016-08-01

Recollection rejection or "recall-to-reject" is a mechanism that has been posited to help maintain accurate memory by preventing the occurrence of false memories. Recollection rejection occurs when the presentation of a new item during recognition triggers recall of an associated target, a mismatch in features between the new and old items is registered, and the lure is correctly rejected. Critically, this characterization of recollection rejection involves a recall signal that is conceptually similar to recollection as elicited by a target. However, previous neuroimaging studies have not evaluated the extent to which recollection rejection and target recollection rely on a common neural signal but have instead focused on recollection rejection as a postretrieval monitoring process. This study utilized a false memory paradigm in conjunction with an adapted remember-know-new response paradigm that separated "new" responses based on recollection rejection from those that were based on a lack of familiarity with the item. This procedure allowed for parallel recollection rejection and target recollection contrasts to be computed. Results revealed that, contrary to predictions from theoretical and behavioral literature, there was virtually no evidence of a common retrieval mechanism supporting recollection rejection and target recollection. Instead of the typical target recollection network, recollection rejection recruited a network of lateral prefrontal and bilateral parietal regions that is consistent with the retrieval monitoring network identified in previous neuroimaging studies of recollection rejection. However, a functional connectivity analysis revealed a component of the frontoparietal rejection network that showed increased coupling with the right hippocampus during recollection rejection responses. As such, we demonstrate a possible link between PFC monitoring network and basic retrieval mechanisms within the hippocampus that was not revealed with univariate analyses alone.

Polyhydroxyalkanoate/carbon nanotube nanocomposites: flexible electrically conducting elastomers for neural applications.

PubMed

Vallejo-Giraldo, Catalina; Pugliese, Eugenia; Larrañaga, Aitor; Fernandez-Yague, Marc A; Britton, James J; Trotier, Alexandre; Tadayyon, Ghazal; Kelly, Adriona; Rago, Ilaria; Sarasua, Jose-Ramon; Dowd, Eilís; Quinlan, Leo R; Pandit, Abhay; Biggs, Manus Jp

2016-10-01

Medium chain length-polyhydroxyalkanoate/multi-walled carbon nanotube (MWCNTs) nanocomposites with a range of mechanical and electrochemical properties were fabricated via assisted dispersion and solvent casting, and their suitability as neural interface biomaterials was investigated. Mechanical and electrical properties of medium chain length-polyhydroxyalkanoate/MWCNTs nanocomposite films were evaluated by tensile test and electrical impedance spectroscopy, respectively. Primary rat mesencephalic cells were seeded on the composites and quantitative immunostaining of relevant neural biomarkers, and electrical stimulation studies were performed. Incorporation of MWCNTs to the polymeric matrix modulated the mechanical and electrical properties of resulting composites, and promoted differential cell viability, morphology and function as a function of MWCNT concentration. This study demonstrates the feasibility of a green thermoplastic MWCNTs nanocomposite for potential use in neural interfacing applications.

Reduced neural baroreflex sensitivity is related to enhanced endothelial function in patients with end-stage liver disease.

PubMed

Sárközi, Adrienn; Cseh, Domonkos; Gerlei, Zsuzsanna; Kollai, Márk

2018-02-01

Reduced baroreflex sensitivity (BRS) is a frequent complication in end-stage liver disease, but the underlying mechanism is unknown. We investigated the mechanical and neural components of BRS. Increased nitric oxide (NO) production has been reported in end-stage liver failure. Based on earlier experiments, we hypothesised that enhanced endothelial function might affect baroreflex function. Therefore, we explored the relation between endothelial function and the components of BRS. We enrolled 24 patients and 23 controls. BRS was determined by the spontaneous sequence method. Mechanical component was characterised by the distensibility coefficient (DC) of common carotid artery. Neural component was estimated as the ratio of integrated BRS and DC. Endothelial function was quantified by flow-mediated dilation (FMD) of the brachial artery. Integrated BRS was reduced in patients [7.00 (5.80-9.25) vs. 11.1 (8.50-14.80) ms/mmHg]. The mechanical component was not different in the two groups, whereas neural component showed significant reduction in patients (3.54 ± 1.20 vs. 4.48 ± 1.43 ms/10 -3 ). FMD was higher in patients (9.81 ± 3.77 vs. 5.59 ± 1.36%). FMD and neural BRS were directly related in controls (r = 0.62), but inversely related in patients (r = -0.49). Baroreflex impairment in end-stage liver disease might be explained by deterioration of the neural component, while the mechanical component appears to be preserved. Endothelial NO may enhance BRS in health; however, central endothelial overproduction of NO likely contributes to the reduction of neural component of BRS in patients awaiting liver transplantation.

Optogenetic stimulation of myelination (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yang, In Hong; Lee, Hae Ung; Thakor, Nitish V.

2016-03-01

Myelination is governed by axon-glia interaction which is modulated by neural activity. Currently, the effects of subcellular activation of neurons which induce neural activity upon myelination are not well understood. To identify if subcellular neuronal stimulation can enhance myelination, we developed a novel system for focal stimulation of neural activity with optogenetic in a compartmentalized microfluidic platform. In our systems, stimulation for neurons in restricted subcellular parts, such as cell bodies and axons promoted oligodendrocyte differentiation and the myelination of axons the just as much as whole cell activation of neurons did. The number of premature O4 positive oligodendrocytes was reduced and the numbers of mature and myelin basic protein-positive oligodendrocytes was increased both by subcellular optogenetic stimulation.

Interfacing Neural Network Components and Nucleic Acids

PubMed Central

Lissek, Thomas

2017-01-01

Translating neural activity into nucleic acid modifications in a controlled manner harbors unique advantages for basic neurobiology and bioengineering. It would allow for a new generation of biological computers that store output in ultra-compact and long-lived DNA and enable the investigation of animal nervous systems at unprecedented scales. Furthermore, by exploiting the ability of DNA to precisely influence neuronal activity and structure, it could be possible to more effectively create cellular therapy approaches for psychiatric diseases that are currently difficult to treat. PMID:29255707

A neural network for the prediction of performance parameters of transformer cores

NASA Astrophysics Data System (ADS)

Nussbaum, C.; Booth, T.; Ilo, A.; Pfützner, H.

1996-07-01

The paper shows that Artificial Neural Networks (ANNs) may offer new possibilities for the prediction of transformer core performance parameters, i.e. no-load power losses and excitation. Basically this technique enables simulations with respect to different construction parameters most notably the characteristics of corner designs, i.e. the overlap length, the air gap length, and the number of steps. However, without additional physical knowledge incorporated into the ANN extrapolation beyond the training data limits restricts the predictive performance.

Modeling and possible implementation of self-learning equivalence-convolutional neural structures for auto-encoding-decoding and clusterization of images

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2017-08-01

Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and image clustering are discussed. The SLECNS architectures and their spatially invariant equivalent models (SI EMs) using the corresponding matrix-matrix procedures with basic operations of continuous logic and non-linear processing are proposed. These SI EMs have several advantages, such as the ability to recognize image fragments with better efficiency and strong cross correlation. The proposed clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively processing algorithms and to k-average method. The experimental results confirmed that larger images and 2D binary fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an image with dimension of 256x256 (a reference array) and fragments with dimensions of 7x7 and 21x21 for clustering is carried out. The experiments, using the software environment Mathcad, showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. We show that the implementation of SLECNS based on known equivalentors or traditional correlators is possible if they are based on proposed equivalental two-dimensional functions of image similarity. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalental weighing of input patterns. Real model experiments in Mathcad are demonstrated, which confirm that non-linear processing on equivalent functions allows you to determine the neuron winners and adjust the weight matrix. Experimental results have shown that such models can be successfully used for auto- and hetero-associative recognition. They can also be used to explain some mechanisms known as "focus" and "competing gain-inhibition concept". The SLECNS architecture and hardware implementations of its basic nodes based on multi-channel convolvers and correlators with time integration are proposed. The parameters and performance of such architectures are estimated.

Neural Mechanisms of Emotion.

ERIC Educational Resources Information Center

Derryberry, Douglas; Tucker, Don M.

1992-01-01

Views neural mechanisms of emotion from an evolutionary perspective, seeing them distributed across the brainstem, limbic, paralimbic, and neocortical regions. Discusses descending and ascending connections among these levels in relation to three types of emotional processes: peripheral effects on patterned bodily responses, central effects on…

Determining the Neural Substrate for Encoding a Memory of Human Pain and the Influence of Anxiety

PubMed Central

Kong, Yazhuo; Tracey, Irene

2017-01-01

To convert a painful stimulus into a briefly maintainable construct when the painful stimulus is no longer accessible is essential to guide human behavior and avoid dangerous situations. Because of the aversive nature of pain, this encoding process might be influenced by emotional aspects and could thus vary across individuals, but we have yet to understand both the basic underlying neural mechanisms as well as potential interindividual differences. Using fMRI in combination with a delayed-discrimination task in healthy volunteers of both sexes, we discovered that brain regions involved in this working memory encoding process were dissociable according to whether the to-be-remembered stimulus was painful or not, with the medial thalamus and the rostral anterior cingulate cortex encoding painful and the primary somatosensory cortex encoding nonpainful stimuli. Encoding of painful stimuli furthermore significantly enhanced functional connectivity between the thalamus and medial prefrontal cortex (mPFC). With regards to emotional aspects influencing encoding processes, we observed that more anxious participants showed significant performance advantages when encoding painful stimuli. Importantly, only during the encoding of pain, the interindividual differences in anxiety were associated with the strength of coupling between medial thalamus and mPFC, which was furthermore related to activity in the amygdala. These results indicate not only that there is a distinct signature for the encoding of a painful experience in humans, but also that this encoding process involves a strong affective component. SIGNIFICANCE STATEMENT To convert the sensation of pain into a briefly maintainable construct is essential to guide human behavior and avoid dangerous situations. Although this working memory encoding process is implicitly contained in the majority of studies, the underlying neural mechanisms remain unclear. Using fMRI in a delayed-discrimination task, we found that the encoding of pain engaged the activation of the medial thalamus and the functional connectivity between the thalamus and medial prefrontal cortex. These fMRI data were directly and indirectly related to participants' self-reported trait and state anxiety. Our findings indicate that the mechanisms responsible for the encoding of noxious stimuli differ from those for the encoding of innocuous stimuli, and that these mechanisms are shaped by an individual's anxiety levels. PMID:29097595

Determining the Neural Substrate for Encoding a Memory of Human Pain and the Influence of Anxiety.

PubMed

Tseng, Ming-Tsung; Kong, Yazhuo; Eippert, Falk; Tracey, Irene

2017-12-06

To convert a painful stimulus into a briefly maintainable construct when the painful stimulus is no longer accessible is essential to guide human behavior and avoid dangerous situations. Because of the aversive nature of pain, this encoding process might be influenced by emotional aspects and could thus vary across individuals, but we have yet to understand both the basic underlying neural mechanisms as well as potential interindividual differences. Using fMRI in combination with a delayed-discrimination task in healthy volunteers of both sexes, we discovered that brain regions involved in this working memory encoding process were dissociable according to whether the to-be-remembered stimulus was painful or not, with the medial thalamus and the rostral anterior cingulate cortex encoding painful and the primary somatosensory cortex encoding nonpainful stimuli. Encoding of painful stimuli furthermore significantly enhanced functional connectivity between the thalamus and medial prefrontal cortex (mPFC). With regards to emotional aspects influencing encoding processes, we observed that more anxious participants showed significant performance advantages when encoding painful stimuli. Importantly, only during the encoding of pain, the interindividual differences in anxiety were associated with the strength of coupling between medial thalamus and mPFC, which was furthermore related to activity in the amygdala. These results indicate not only that there is a distinct signature for the encoding of a painful experience in humans, but also that this encoding process involves a strong affective component. SIGNIFICANCE STATEMENT To convert the sensation of pain into a briefly maintainable construct is essential to guide human behavior and avoid dangerous situations. Although this working memory encoding process is implicitly contained in the majority of studies, the underlying neural mechanisms remain unclear. Using fMRI in a delayed-discrimination task, we found that the encoding of pain engaged the activation of the medial thalamus and the functional connectivity between the thalamus and medial prefrontal cortex. These fMRI data were directly and indirectly related to participants' self-reported trait and state anxiety. Our findings indicate that the mechanisms responsible for the encoding of noxious stimuli differ from those for the encoding of innocuous stimuli, and that these mechanisms are shaped by an individual's anxiety levels. Copyright © 2017 Tseng et al.

Brain imaging in the context of food perception and eating.

PubMed

Hollmann, Maurice; Pleger, Burkhard; Villringer, Arno; Horstmann, Annette

2013-02-01

Eating behavior depends heavily on brain function. In recent years, brain imaging has proved to be a powerful tool to elucidate brain function and brain structure in the context of eating. In this review, we summarize recent findings in the fast growing body of literature in the field and provide an overview of technical aspects as well as the basic brain mechanisms identified with imaging. Furthermore, we highlight findings linking neural processing of eating-related stimuli with obesity. The consumption of food is based on a complex interplay between homeostatic and hedonic mechanisms. Several hormones influence brain activity to regulate food intake and interact with the brain's reward circuitry, which is partly mediated by dopamine signaling. Additionally, it was shown that food stimuli trigger cognitive control mechanisms that incorporate internal goals into food choice. The brain mechanisms observed in this context are strongly influenced by genetic factors, sex and personality traits. Overall, a complex picture arises from brain-imaging findings, because a multitude of factors influence human food choice. Although several key mechanisms have been identified, there is no comprehensive model that is able to explain the behavioral observations to date. Especially a careful characterization of patients according to genotypes and phenotypes could help to better understand the current and future findings in neuroimaging studies.

Simulation electromagnetic scattering on bodies through integral equation and neural networks methods

NASA Astrophysics Data System (ADS)

Lvovich, I. Ya; Preobrazhenskiy, A. P.; Choporov, O. N.

2018-05-01

The paper deals with the issue of electromagnetic scattering on a perfectly conducting diffractive body of a complex shape. Performance calculation of the body scattering is carried out through the integral equation method. Fredholm equation of the second time was used for calculating electric current density. While solving the integral equation through the moments method, the authors have properly described the core singularity. The authors determined piecewise constant functions as basic functions. The chosen equation was solved through the moments method. Within the Kirchhoff integral approach it is possible to define the scattered electromagnetic field, in some way related to obtained electrical currents. The observation angles sector belongs to the area of the front hemisphere of the diffractive body. To improve characteristics of the diffractive body, the authors used a neural network. All the neurons contained a logsigmoid activation function and weighted sums as discriminant functions. The paper presents the matrix of weighting factors of the connectionist model, as well as the results of the optimized dimensions of the diffractive body. The paper also presents some basic steps in calculation technique of the diffractive bodies, based on the combination of integral equation and neural networks methods.

Nervous system regulation of the cancer genome

PubMed Central

Cole, Steven W.

2012-01-01

Genomics-based analyses have provided deep insight into the basic biology of cancer and are now clarifying the molecular pathways by which psychological and social factors can regulate tumor cell gene expression and genome evolution. This review summarizes basic and clinical research on neural and endocrine regulation of the cancer genome and its interactions with the surrounding tumor microenvironment, including the specific types of genes subject to neural and endocrine regulation, the signal transduction pathways that mediate such effects, and therapeutic approaches that might be deployed to mitigate their impact. Beta-adrenergic signaling from the sympathetic nervous system has been found to up-regulated a diverse array of genes that contribute to tumor progression and metastasis, whereas glucocorticoid-regulated genes can inhibit DNA repair and promote cancer cell survival and resistance to chemotherapy. Relationships between socio-environmental risk factors, neural and endocrine signaling to the tumor microenvironment, and transcriptional responses by cancer cells and surrounding stromal cells are providing new mechanistic insights into the social epidemiology of cancer, new therapeutic approaches for protecting the health of cancer patients, and new molecular biomarkers for assessing the impact of behavioral and pharmacologic interventions. PMID:23207104

Distributed affective space represents multiple emotion categories across the human brain

PubMed Central

Saarimäki, Heini; Ejtehadian, Lara Farzaneh; Jääskeläinen, Iiro P; Vuilleumier, Patrik; Sams, Mikko; Nummenmaa, Lauri

2018-01-01

Abstract The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here, we used pattern classification and hierarchical clustering to characterize the organization of a wide array of emotion categories in the human brain. We induced 14 emotions (6 ‘basic’, e.g. fear and anger; and 8 ‘non-basic’, e.g. shame and gratitude) and a neutral state using guided mental imagery while participants' brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the haemodynamic signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate gyri and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that different basic and non-basic emotions have distinguishable neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits. Regionally differentiated engagement of these circuits defines the unique neural activity pattern and the corresponding subjective feeling associated with each emotion. PMID:29618125

Mdm2 mediates FMRP- and Gp1 mGluR-dependent protein translation and neural network activity.

PubMed

Liu, Dai-Chi; Seimetz, Joseph; Lee, Kwan Young; Kalsotra, Auinash; Chung, Hee Jung; Lu, Hua; Tsai, Nien-Pei

2017-10-15

Activating Group 1 (Gp1) metabotropic glutamate receptors (mGluRs), including mGluR1 and mGluR5, elicits translation-dependent neural plasticity mechanisms that are crucial to animal behavior and circuit development. Dysregulated Gp1 mGluR signaling has been observed in numerous neurological and psychiatric disorders. However, the molecular pathways underlying Gp1 mGluR-dependent plasticity mechanisms are complex and have been elusive. In this study, we identified a novel mechanism through which Gp1 mGluR mediates protein translation and neural plasticity. Using a multi-electrode array (MEA) recording system, we showed that activating Gp1 mGluR elevates neural network activity, as demonstrated by increased spontaneous spike frequency and burst activity. Importantly, we validated that elevating neural network activity requires protein translation and is dependent on fragile X mental retardation protein (FMRP), the protein that is deficient in the most common inherited form of mental retardation and autism, fragile X syndrome (FXS). In an effort to determine the mechanism by which FMRP mediates protein translation and neural network activity, we demonstrated that a ubiquitin E3 ligase, murine double minute-2 (Mdm2), is required for Gp1 mGluR-induced translation and neural network activity. Our data showed that Mdm2 acts as a translation suppressor, and FMRP is required for its ubiquitination and down-regulation upon Gp1 mGluR activation. These data revealed a novel mechanism by which Gp1 mGluR and FMRP mediate protein translation and neural network activity, potentially through de-repressing Mdm2. Our results also introduce an alternative way for understanding altered protein translation and brain circuit excitability associated with Gp1 mGluR in neurological diseases such as FXS. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evolving neural networks with genetic algorithms to study the string landscape

NASA Astrophysics Data System (ADS)

Ruehle, Fabian

2017-08-01

We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.

Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders.

PubMed

O'Donnell, Cian; Gonçalves, J Tiago; Portera-Cailliau, Carlos; Sejnowski, Terrence J

2017-10-11

A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca 2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits.

Robustness analysis of uncertain dynamical neural networks with multiple time delays.

PubMed

Senan, Sibel

2015-10-01

This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders

PubMed Central

Gonçalves, J Tiago; Portera-Cailliau, Carlos

2017-01-01

A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits. PMID:29019321

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

PubMed

Caballero, Javier A; Lepora, Nathan F; Gurney, Kevin N

2015-01-01

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain

PubMed Central

Caballero, Javier A.; Lepora, Nathan F.; Gurney, Kevin N.

2015-01-01

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks. PMID:25923907

The search for the neurobiological basis of vulnerability to drug abuse: using microarrays to investigate the role of stress and individual differences.

PubMed

Kabbaj, M; Evans, S; Watson, S J; Akil, H

2004-01-01

Basic neurobiological studies have led to great progress in our understanding of the mechanisms of action of drugs of abuse. Much has been learned about the brain response from the moment a psychoactive drug enters the organism onwards, including the psychological, neurobiological and peripheral effects of repeated drug administration, withdrawal and re-exposure. However, to relate this knowledge to the human experience requires further research on the antecedents of drug-taking behavior and the factors that predispose particular individuals to drug seeking and drug abuse. Thus, it is important to address several issues at the fundamental level: (1) Why are some individuals more vulnerable to drugs of abuse more than others? Is there a broader dimension or dimensions of emotional reactivity that contribute to this difference in vulnerability? (2) What is the effect of psychosocial stress on drug-seeking and drug-taking behavior, and are the effects distinct across individuals? (3) Since both drug-taking behavior and stress have sustained and pervasive effects on the brain, can we use microarrays to discern the "neural signature" or "neural phenotype" associated with these processes, and can we distinguish this signature across individuals with differing propensities to taking drugs? In the present paper, we summarize some of our early attempts at addressing these questions. We rely on animal studies aimed at characterizing the emotional and stress reactivity of rats with different propensities to self-administer drugs (high responders and low responders); we briefly describe the effect of a psychosocial stressor on these animals; we then detail a study using microarray technology aimed at investigating the "neural phenotype" associated with social defeat stress in the high vs. low responder animals. This "discovery" approach is used as a starting place for identifying novel mechanisms that might alter the vulnerability of different individuals to drug-seeking behavior. The power and limits of this approach, and its future directions, are discussed within this general framework.

Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks.

PubMed

Arbabi, Vahid; Pouran, Behdad; Campoli, Gianni; Weinans, Harrie; Zadpoor, Amir A

2016-03-21

One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data. Copyright © 2016 Elsevier Ltd. All rights reserved.

[The mechanism and function of hippocampal neural oscillation].

PubMed

Lu, Ning; Xing, Dan-Qin; Sheng, Tao; Lu, Wei

2017-10-25

Neural oscillation is rhythmic or repetitive neural activity in the central nervous system that is usually generated by oscillatory activity of neuronal ensembles, reflecting regular and synchronized activities within these cell populations. According to several oscillatory bands covering frequencies from approximately 0.5 Hz to >100 Hz, neural oscillations are usually classified as delta oscillation (0.5-3 Hz), theta oscillation (4-12 Hz), beta oscillation (12-30 Hz), gamma oscillation (30-100 Hz) and sharp-wave ripples (>100 Hz ripples superimposed on 0.01-3 Hz sharp waves). Neural oscillation in different frequencies can be detected in different brain regions of human and animal during perception, motion and sleep, and plays an essential role in cognition, learning and memory process. In this review, we summarize recent findings on neural oscillations in hippocampus, as well as the mechanism and function of hippocampal theta oscillation, gamma oscillation and sharp-wave ripples. This review may yield new insights into the functions of neural oscillation in general.

Development of Si neural probe with piezoresistive force sensor for minimally invasive and precise monitoring of insertion forces

NASA Astrophysics Data System (ADS)

Harashima, Takuya; Morikawa, Takumi; Kino, Hisashi; Fukushima, Takafumi; Tanaka, Tetsu

2017-04-01

A Si neural probe is one of the most important tools for neurophysiology and brain science because of its various functions such as optical stimulation and drug delivery. However, the Si neural probe is not robust compared with a metal tetrode, and could be broken by mechanical stress caused by insertion to the brain. Therefore, the Si neural probe becomes more useful if it has a stress sensor that can measure mechanical forces applied to the probe so as not to be broken. In this paper, we proposed and fabricated the Si neural probe with a piezoresistive force sensor for minimally invasive and precise monitoring of insertion forces. The fabricated piezoresistive force sensor accurately measured forces and successfully detected insertion events without buckling or bending in the shank of the Si neural probe. This Si neural probe with a piezoresistive force sensor has become one of the most versatile tools for neurophysiology and brain science.

Notochord-derived Shh concentrates in close association with the apically positioned basal body in neural target cells and forms a dynamic gradient during neural patterning.

PubMed

Chamberlain, Chester E; Jeong, Juhee; Guo, Chaoshe; Allen, Benjamin L; McMahon, Andrew P

2008-03-01

Sonic hedgehog (Shh) ligand secreted by the notochord induces distinct ventral cell identities in the adjacent neural tube by a concentration-dependent mechanism. To study this process, we genetically engineered mice that produce bioactive, fluorescently labeled Shh from the endogenous locus. We show that Shh ligand concentrates in close association with the apically positioned basal body of neural target cells, forming a dynamic, punctate gradient in the ventral neural tube. Both ligand lipidation and target field response influence the gradient profile, but not the ability of Shh to concentrate around the basal body. Further, subcellular analysis suggests that Shh from the notochord might traffic into the neural target field by means of an apical-to-basal-oriented microtubule scaffold. This study, in which we directly observe, measure, localize and modify notochord-derived Shh ligand in the context of neural patterning, provides several new insights into mechanisms of Shh morphogen action.

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Applications of artificial neural nets in structural mechanics

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Hajela, Prabhat

1990-01-01

A brief introduction to the fundamental of Neural Nets is given, followed by two applications in structural optimization. In the first case, the feasibility of simulating with neural nets the many structural analyses performed during optimization iterations was studied. In the second case, the concept of using neural nets to capture design expertise was studied.

Applications of artificial neural nets in structural mechanics

NASA Technical Reports Server (NTRS)

Berke, L.; Hajela, P.

1992-01-01

A brief introduction to the fundamental of Neural Nets is given, followed by two applications in structural optimization. In the first case, the feasibility of simulating with neural nets the many structural analyses performed during optimization iterations was studied. In the second case, the concept of using neural nets to capture design expertise was studied.

From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction.

PubMed

Hellyer, Peter John; Clopath, Claudia; Kehagia, Angie A; Turkheimer, Federico E; Leech, Robert

2017-08-01

In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional "task negative" activity that compensates for "task positive", sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic "task-negative" patterns of activity (e.g., the default mode network).

Neural mechanisms of movement planning: motor cortex and beyond.

PubMed

Svoboda, Karel; Li, Nuo

2018-04-01

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes. Copyright © 2017. Published by Elsevier Ltd.

Simultaneous surface and depth neural activity recording with graphene transistor-based dual-modality probes.

PubMed

Du, Mingde; Xu, Xianchen; Yang, Long; Guo, Yichuan; Guan, Shouliang; Shi, Jidong; Wang, Jinfen; Fang, Ying

2018-05-15

Subdural surface and penetrating depth probes are widely applied to record neural activities from the cortical surface and intracortical locations of the brain, respectively. Simultaneous surface and depth neural activity recording is essential to understand the linkage between the two modalities. Here, we develop flexible dual-modality neural probes based on graphene transistors. The neural probes exhibit stable electrical performance even under 90° bending because of the excellent mechanical properties of graphene, and thus allow multi-site recording from the subdural surface of rat cortex. In addition, finite element analysis was carried out to investigate the mechanical interactions between probe and cortex tissue during intracortical implantation. Based on the simulation results, a sharp tip angle of π/6 was chosen to facilitate tissue penetration of the neural probes. Accordingly, the graphene transistor-based dual-modality neural probes have been successfully applied for simultaneous surface and depth recording of epileptiform activity of rat brain in vivo. Our results show that graphene transistor-based dual-modality neural probes can serve as a facile and versatile tool to study tempo-spatial patterns of neural activities. Copyright © 2018 Elsevier B.V. All rights reserved.

Neuromechanical considerations for incorporating rhythmic arm movement in the rehabilitation of walking

NASA Astrophysics Data System (ADS)

Klimstra, Marc D.; Thomas, Evan; Stoloff, Rebecca H.; Ferris, Daniel P.; Zehr, E. Paul

2009-06-01

We have extensively used arm cycling to study the neural control of rhythmic movements such as arm swing during walking. Recently rhythmic movement of the arms has also been shown to enhance and shape muscle activity in the legs. However, restricted information is available concerning the conditions necessary to maximally alter lumbar spinal cord excitability. Knowledge on the neuromechanics of a task can assist in the determination of the type, level, and timing of neural signals, yet arm swing during walking and arm cycling have not received a detailed neuromechanical comparison. The purpose of this research was to provide a combined neural and mechanical measurement approach that could be used to assist in the determination of the necessary and sufficient conditions for arm movement to assist in lower limb rehabilitation after stroke and spinal cord injury. Subjects performed three rhythmic arm movement tasks: (1) cycling (cycle); (2) swinging while standing (swing); and (3) swinging while treadmill walking (walk). We hypothesized that any difference in neural control between tasks (i.e., pattern of muscle activity) would reflect changes in the mechanical constraints unique to each task. Three-dimensional kinematics were collected simultaneously with force measurement at the hand and electromyography from the arms and trunk. All data were appropriately segmented to allow a comparison between and across conditions and were normalized and averaged to 100% movement cycle based on shoulder excursion. Separate mathematical principal components analysis of kinematic and neural variables was performed to determine common task features and muscle synergies. The results highlight important neural and mechanical features that distinguish differences between tasks. For example, there are considerable differences in the anatomical positions of the arms during each task, which relate to the moments experienced about the elbow and shoulder. Also, there are differences between tasks in elbow flexion/extension kinematics alongside differential muscle activation profiles. As well, mechanical assistance and constraints during all tasks could affect muscle recruitment and the functional role of muscles. Overall, despite neural and mechanical differences, the results are consistent with conserved common central motor control mechanisms operational for cycle, walk, and swing but appropriately sculpted to demands unique to each task. However, changing the mechanical parameters could affect the role of afferent feedback altering neural control and the coupling to the lower limbs.

Dynamic changes in neural circuit topology following mild mechanical injury in vitro.

PubMed

Patel, Tapan P; Ventre, Scott C; Meaney, David F

2012-01-01

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this article, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We estimated the imaging conditions needed to achieve accurate measures of network properties, and applied these methodologies to evaluate if mild mechanical injury to cortical neurons produces graded changes to either spontaneous network activity or altered network topology. We found that modest injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology. A calcium-activated neutral protease (calpain) was a key intermediary in these changes; blocking calpain activation restored the network nearly completely to its pre-injury state. Together, these findings show a more complex change in neural circuit behavior than previously reported for mild mechanical injury, and highlight at least one important early mechanism responsible for these changes.

Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

PubMed

Zhang, Jinao; Zhong, Yongmin; Gu, Chengfan

2018-05-30

Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. Graphical abstract Soft tissue deformation modelling with haptic feedback via neural dynamics-based reaction-diffusion mechanics.

Neural responses to macronutrients: hedonic and homeostatic mechanisms.

PubMed

Tulloch, Alastair J; Murray, Susan; Vaicekonyte, Regina; Avena, Nicole M

2015-05-01

The brain responds to macronutrients via intricate mechanisms. We review how the brain's neural systems implicated in homeostatic control of feeding and hedonic responses are influenced by the ingestion of specific types of food. We discuss how these neural systems are dysregulated in preclinical models of obesity. Findings from these studies can increase our understanding of overeating and, perhaps in some cases, the development of obesity. In addition, a greater understanding of the neural circuits affected by the consumption of specific macronutrients, and by obesity, might lead to new treatments and strategies for preventing unhealthy weight gain. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Genetic learning in rule-based and neural systems

NASA Technical Reports Server (NTRS)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Learning representations for the early detection of sepsis with deep neural networks.

PubMed

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multidisciplinary Design Optimization for Aeropropulsion Engines and Solid Modeling/Animation via the Integrated Forced Methods

NASA Technical Reports Server (NTRS)

2004-01-01

The grant closure report is organized in the following four chapters: Chapter describes the two research areas Design optimization and Solid mechanics. Ten journal publications are listed in the second chapter. Five highlights is the subject matter of chapter three. CHAPTER 1. The Design Optimization Test Bed CometBoards. CHAPTER 2. Solid Mechanics: Integrated Force Method of Analysis. CHAPTER 3. Five Highlights: Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft. Neural Network and Regression Soft Model Extended for PX-300 Aircraft Engine. Engine with Regression and Neural Network Approximators Designed. Cascade Optimization Strategy with Neural network and Regression Approximations Demonstrated on a Preliminary Aircraft Engine Design. Neural Network and Regression Approximations Used in Aircraft Design.

Neural Stability, Sparing, and Behavioral Recovery Following Brain Damage

ERIC Educational Resources Information Center

LeVere, T. E.

1975-01-01

The present article discusses the possibility that behavioral recovery following brain damage is not dependent on the functional reorganization of neural tissue but is rather the result of the continued normal operation of spared neural mechanisms. (Editor)

Spin switches for compact implementation of neuron and synapse

NASA Astrophysics Data System (ADS)

Quang Diep, Vinh; Sutton, Brian; Behin-Aein, Behtash; Datta, Supriyo

2014-06-01

Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to explore the possibility of a hardware neural network implementation using a spin switch (SS) as its basic building block. SS is a recently proposed device based on established technology with a transistor-like gain and input-output isolation. This allows neural networks to be constructed with purely passive interconnections without intervening clocks or amplifiers. The weights for the neural network are conveniently adjusted through analog voltages that can be stored in a non-volatile manner in an underlying CMOS layer using a floating gate low dropout voltage regulator. The operation of a multi-layer SS neural network designed for character recognition is demonstrated using a standard simulation model based on coupled Landau-Lifshitz-Gilbert equations, one for each magnet in the network.

RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

NASA Astrophysics Data System (ADS)

Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

2016-02-01

Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

Neural evidence that human emotions share core affective properties.

PubMed

Wilson-Mendenhall, Christine D; Barrett, Lisa Feldman; Barsalou, Lawrence W

2013-06-01

Research on the "emotional brain" remains centered around the idea that emotions like fear, happiness, and sadness result from specialized and distinct neural circuitry. Accumulating behavioral and physiological evidence suggests, instead, that emotions are grounded in core affect--a person's fluctuating level of pleasant or unpleasant arousal. A neuroimaging study revealed that participants' subjective ratings of valence (i.e., pleasure/displeasure) and of arousal evoked by various fear, happiness, and sadness experiences correlated with neural activity in specific brain regions (orbitofrontal cortex and amygdala, respectively). We observed these correlations across diverse instances within each emotion category, as well as across instances from all three categories. Consistent with a psychological construction approach to emotion, the results suggest that neural circuitry realizes more basic processes across discrete emotions. The implicated brain regions regulate the body to deal with the world, producing the affective changes at the core of emotions and many other psychological phenomena.

Neural Evidence that Human Emotions Share Core Affective Properties

PubMed Central

Wilson-Mendenhall, Christine D.; Barrett, Lisa Feldman; Barsalou, Lawrence W.

2014-01-01

Research on the “emotional brain” remains centered around the idea that emotions like fear, happiness, and sadness result from specialized and distinct neural circuitry. Accumulating behavioral and physiological evidence suggests, instead, that emotions are grounded in core affect – a person's fluctuating level of pleasant or unpleasant arousal. A neuroimaging study revealed that participants' subjective ratings of valence (i.e., pleasure/displeasure) and of arousal evoked by various fear, happiness, and sadness experiences correlated with neural activity in specific brain regions (orbitofrontal cortex and amygdala, respectively). We observed these correlations across diverse instances within each emotion category, as well as across instances from all three categories. Consistent with a psychological construction approach to emotion, the results suggest that neural circuitry realizes more basic processes across discrete emotions. The implicated brain regions regulate the body to deal with the world, producing the affective changes at the core of emotions and many other psychological phenomena. PMID:23603916

Homeostatic control of neural activity: from phenomenology to molecular design.

PubMed

Davis, Graeme W

2006-01-01

Homeostasis is a specialized form of regulation that precisely maintains the function of a system at a set point level of activity. Recently, homeostatic signaling has been suggested to control neural activity through the modulation of synaptic efficacy and membrane excitability ( Davis & Goodman 1998a, Turrigiano & Nelson 2000, Marder & Prinz 2002, Perez-Otano & Ehlers 2005 ). In this way, homeostatic signaling is thought to constrain neural plasticity and contribute to the stability of neural function over time. Using a restrictive definition of homeostasis, this review first evaluates the phenomenological and molecular evidence for homeostatic signaling in the nervous system. Then, basic principles underlying the design and molecular implementation of homeostatic signaling are reviewed on the basis of work in other, simplified biological systems such as bacterial chemotaxis and the heat shock response. Data from these systems are then discussed in the context of homeostatic signaling in the nervous system.

Numerical Analysis of Modeling Based on Improved Elman Neural Network

PubMed Central

Jie, Shao

2014-01-01

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

2002-01-01

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Illuminating Neural Circuits: From Molecules to MRI.

PubMed

Lee, Jin Hyung; Kreitzer, Anatol C; Singer, Annabelle C; Schiff, Nicholas D

2017-11-08

Neurological disease drives symptoms through pathological changes to circuit functions. Therefore, understanding circuit mechanisms that drive behavioral dysfunction is of critical importance for quantitative diagnosis and systematic treatment of neurological disease. Here, we describe key technologies that enable measurement and manipulation of neural activity and neural circuits. Applying these approaches led to the discovery of circuit mechanisms underlying pathological motor behavior, arousal regulation, and protein accumulation. Finally, we discuss how optogenetic functional magnetic resonance imaging reveals global scale circuit mechanisms, and how circuit manipulations could lead to new treatments of neurological diseases. Copyright © 2017 the authors 0270-6474/17/3710817-09$15.00/0.

Control of skeletal muscle perfusion at the onset of dynamic exercise

NASA Technical Reports Server (NTRS)

Delp, M. D.

1999-01-01

At the onset of exercise there is a rapid increase in skeletal muscle vascular conductance and blood flow. Several mechanisms involved in the regulation of muscle perfusion have been proposed to initiate this hyperemic response, including neural, metabolic, endothelial, myogenic, and muscle pump mechanisms. Investigators utilizing pharmacological blockade of cholinergic muscarinic receptors and sympathectomy have concluded that neither sympathetic cholinergic nor adrenergic neural mechanisms are involved in the initial hyperemia. Studies have also shown that the time course for vasoactive metabolite release, diffusion, accumulation, and action is too long to account for the rapid increase in vascular conductance at the initiation of exercise. Furthermore, there is little or no evidence to support an endothelium or myogenic mechanism as the initiating factor in the muscle hyperemia. Thus, the rise in muscle blood flow does not appear to be explained by known neural, metabolic, endothelial, or myogenic influences. However, the initial hyperemia is consistent with the mechanical effects of the muscle pump to increase the arteriovenous pressure gradient across muscle. Because skeletal muscle blood flow is regulated by multiple and redundant mechanisms, it is likely that neural, metabolic, and possibly endothelial factors become important modulators of mechanically induced exercise hyperemia following the first 5-10 s of exercise.

The Molecular and Cellular Mechanisms of Axon Guidance in Mossy Fiber Sprouting

PubMed Central

Koyama, Ryuta; Ikegaya, Yuji

2018-01-01

The question of whether mossy fiber sprouting is epileptogenic has not been resolved; both sprouting-induced recurrent excitatory and inhibitory circuit hypotheses have been experimentally (but not fully) supported. Therefore, whether mossy fiber sprouting is a potential therapeutic target for epilepsy remains under debate. Moreover, the axon guidance mechanisms of mossy fiber sprouting have attracted the interest of neuroscientists. Sprouting of mossy fibers exhibits several uncommon axonal growth features in the basically non-plastic adult brain. For example, robust branching of axonal collaterals arises from pre-existing primary mossy fiber axons. Understanding the branching mechanisms in adulthood may contribute to axonal regeneration therapies in neuroregenerative medicine in which robust axonal re-growth is essential. Additionally, because granule cells are produced throughout life in the neurogenic dentate gyrus, it is interesting to examine whether the mossy fibers of newly generated granule cells follow the pre-existing trajectories of sprouted mossy fibers in the epileptic brain. Understanding these axon guidance mechanisms may contribute to neuron transplantation therapies, for which the incorporation of transplanted neurons into pre-existing neural circuits is essential. Thus, clarifying the axon guidance mechanisms of mossy fiber sprouting could lead to an understanding of central nervous system (CNS) network reorganization and plasticity. Here, we review the molecular and cellular mechanisms of axon guidance in mossy fiber sprouting by discussing mainly in vitro studies. PMID:29896153

Current directions in non-invasive low intensity electric brain stimulation for depressive disorder.

PubMed

Schutter, Dennis J L G; Sack, Alexander T

2014-01-01

Non-invasive stimulation of the human brain to improve depressive symptoms is increasingly finding its way in clinical settings as a viable form of somatic treatment. Following successful modulation of neural excitability with subsequent antidepressant effects, neural polarization by administrating weak direct currents to the scalp has gained renewed interest. A new wave of basic and clinical studies seems to underscore the potential therapeutic value of direct current stimulation in the treatment of depression. Issues concerning the lack of mechanistic insights into the workings of modifying brain function through neural polarization and how this process translates to its antidepressant properties calls for additional research. The range of its clinical applicability has yet to be established.

High-speed all-optical DNA local sequence alignment based on a three-dimensional artificial neural network.

PubMed

Maleki, Ehsan; Babashah, Hossein; Koohi, Somayyeh; Kavehvash, Zahra

2017-07-01

This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DNA, whose output is fed to a novel three-dimensional artificial neural network for local DNA alignment. All-optical implementation of the proposed 3D artificial neural network is developed and its accuracy is verified in Zemax. Thanks to its parallel processing capability, the proposed structure performs local alignment of 4 million sequences of 150 base pairs in a few seconds, which is much faster than its electrical counterparts, such as the basic local alignment search tool.

Distinct Neural Mechanisms Mediate Olfactory Memory Formation at Different Timescales

ERIC Educational Resources Information Center

McNamara, Ann Marie; Magidson, Phillip D.; Linster, Christiane; Wilson, Donald A.; Cleland, Thomas A.

2008-01-01

Habituation is one of the oldest forms of learning, broadly expressed across sensory systems and taxa. Here, we demonstrate that olfactory habituation induced at different timescales (comprising different odor exposure and intertrial interval durations) is mediated by different neural mechanisms. First, the persistence of habituation memory is…

Neural Circuitry and Plasticity Mechanisms Underlying Delay Eyeblink Conditioning

ERIC Educational Resources Information Center

Freeman, John H.; Steinmetz, Adam B.

2011-01-01

Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of…

Conducting Polymers for Neural Prosthetic and Neural Interface Applications

PubMed Central

2015-01-01

Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

Vitamin D and remyelination in multiple sclerosis.

PubMed

Matías-Guíu, J; Oreja-Guevara, C; Matias-Guiu, J A; Gomez-Pinedo, U

2018-04-01

Several studies have found an association between multiple sclerosis and vitamin D (VD) deficiency, which suggests that VD may play a role in the immune response. However, few studies have addressed its role in remyelination. The VD receptor and the enzymes transforming VD into metabolites which activate the VD receptor are expressed in central nervous system (CNS) cells, which suggests a potential effect of VD on the CNS. Both in vitro and animal model studies have shown that VD may play a role in myelination by acting on factors that influence the microenvironment which promotes both proliferation and differentiation of neural stem cells into oligodendrocyte progenitor cells and oligodendrocytes. It remains unknown whether the mechanisms of internalisation of VD in the CNS are synergistic with or antagonistic to the mechanisms that facilitate the entry of VD metabolites into immune cells. VD seems to play a role in the CNS and our hypothesis is that VD is involved in remyelination. Understanding the basic mechanisms of VD in myelination is necessary to manage multiple sclerosis patients with VD deficiency. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

Empathy as a driver of prosocial behaviour: highly conserved neurobehavioural mechanisms across species.

PubMed

Decety, Jean; Bartal, Inbal Ben-Ami; Uzefovsky, Florina; Knafo-Noam, Ariel

2016-01-19

Empathy reflects the natural ability to perceive and be sensitive to the emotional states of others, coupled with a motivation to care for their well-being. It has evolved in the context of parental care for offspring, as well as within kinship bonds, to help facilitate group living. In this paper, we integrate the perspectives of evolution, animal behaviour, developmental psychology, and social and clinical neuroscience to elucidate our understanding of the proximate mechanisms underlying empathy. We focus, in particular, on processing of signals of distress and need, and their relation to prosocial behaviour. The ability to empathize, both in animals and humans, mediates prosocial behaviour when sensitivity to others' distress is paired with a drive towards their welfare. Disruption or atypical development of the neural circuits that process distress cues and integrate them with decision value leads to callous disregard for others, as is the case in psychopathy. The realization that basic forms of empathy exist in non-human animals is crucial for gaining new insights into the underlying neurobiological and genetic mechanisms of empathy, enabling translation towards therapeutic and pharmacological interventions. © 2015 The Author(s).

Low Temperature Performance of High-Speed Neural Network Circuits

NASA Technical Reports Server (NTRS)

Duong, T.; Tran, M.; Daud, T.; Thakoor, A.

1995-01-01

Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.

Neural predictors of individual differences in response to math tutoring in primary-grade school children

PubMed Central

Supekar, Kaustubh; Swigart, Anna G.; Tenison, Caitlin; Jolles, Dietsje D.; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-01-01

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8–9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures. PMID:23630286

Neural predictors of individual differences in response to math tutoring in primary-grade school children.

PubMed

Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-05-14

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures.

Got Rhythm? Better Inhibitory Control Is Linked with More Consistent Drumming and Enhanced Neural Tracking of the Musical Beat in Adult Percussionists and Nonpercussionists.

PubMed

Slater, Jessica; Ashley, Richard; Tierney, Adam; Kraus, Nina

2018-01-01

Musical rhythm engages motor and reward circuitry that is important for cognitive control, and there is evidence for enhanced inhibitory control in musicians. We recently revealed an inhibitory control advantage in percussionists compared with vocalists, highlighting the potential importance of rhythmic expertise in mediating this advantage. Previous research has shown that better inhibitory control is associated with less variable performance in simple sensorimotor synchronization tasks; however, this relationship has not been examined through the lens of rhythmic expertise. We hypothesize that the development of rhythm skills strengthens inhibitory control in two ways: by fine-tuning motor networks through the precise coordination of movements "in time" and by activating reward-based mechanisms, such as predictive processing and conflict monitoring, which are involved in tracking temporal structure in music. Here, we assess adult percussionists and nonpercussionists on inhibitory control, selective attention, basic drumming skills (self-paced, paced, and continuation drumming), and cortical evoked responses to an auditory stimulus presented on versus off the beat of music. Consistent with our hypotheses, we find that better inhibitory control is correlated with more consistent drumming and enhanced neural tracking of the musical beat. Drumming variability and the neural index of beat alignment each contribute unique predictive power to a regression model, explaining 57% of variance in inhibitory control. These outcomes present the first evidence that enhanced inhibitory control in musicians may be mediated by rhythmic expertise and provide a foundation for future research investigating the potential for rhythm-based training to strengthen cognitive function.

Cascade process modeling with mechanism-based hierarchical neural networks.

PubMed

Cong, Qiumei; Yu, Wen; Chai, Tianyou

2010-02-01

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

PA6 Stromal Cell Co-Culture Enhances SH-SY5Y and VSC4.1 Neuroblastoma Differentiation to Mature Phenotypes

PubMed Central

Ferguson, Ross; Subramanian, Vasanta

2016-01-01

Neuroblastoma cell lines such as SH-SY5Y have been used for modelling neurodegenerative diseases and for studying basic mechanisms in neuroscience. Since neuroblastoma cells proliferate and generally do not express markers of mature or functional neurons, we exploited a co-culture system with the stromal cell line PA6 to better induce differentiation to a more physiologically relevant status. We found that co-culture of the neuroblastoma cell lines in the presence of neural inducers such retinoic acid was able to generate a high proportion of quiescent neurons with very long neurites expressing differentiation markers. The co-culture system additionally cuts short the time taken to produce a more mature phenotype. We also show the application of this system to study proteins implicated in motor neuron disease. PMID:27391595

PA6 Stromal Cell Co-Culture Enhances SH-SY5Y and VSC4.1 Neuroblastoma Differentiation to Mature Phenotypes.

PubMed

Ferguson, Ross; Subramanian, Vasanta

2016-01-01

Neuroblastoma cell lines such as SH-SY5Y have been used for modelling neurodegenerative diseases and for studying basic mechanisms in neuroscience. Since neuroblastoma cells proliferate and generally do not express markers of mature or functional neurons, we exploited a co-culture system with the stromal cell line PA6 to better induce differentiation to a more physiologically relevant status. We found that co-culture of the neuroblastoma cell lines in the presence of neural inducers such retinoic acid was able to generate a high proportion of quiescent neurons with very long neurites expressing differentiation markers. The co-culture system additionally cuts short the time taken to produce a more mature phenotype. We also show the application of this system to study proteins implicated in motor neuron disease.

Basic mechanisms of MCD in animal models.

PubMed

Battaglia, Giorgio; Becker, Albert J; LoTurco, Joseph; Represa, Alfonso; Baraban, Scott C; Roper, Steven N; Vezzani, Annamaria

2009-09-01

Epilepsy-associated glioneuronal malformations (malformations of cortical development [MCD]) include focal cortical dysplasias (FCD) and highly differentiated glioneuronal tumors, most frequently gangliogliomas. The neuropathological findings are variable but suggest aberrant proliferation, migration, and differentiation of neural precursor cells as essential pathogenetic elements. Recent advances in animal models for MCDs allow new insights in the molecular pathogenesis of these epilepsy-associated lesions. Novel approaches, presented here, comprise RNA interference strategies to generate and study experimental models of subcortical band heterotopia and study functional aspects of aberrantly shaped and positioned neurons. Exciting analyses address impaired NMDA receptor expression in FCD animal models compared to human FCDs and excitatory imbalances in MCD animal models such as lissencephaly gene ablated mice as well as in utero irradiated rats. An improved understanding of relevant pathomechanisms will advance the development of targeted treatment strategies for epilepsy-associated malformations.

Designing Interventions Informed by Scientific Knowledge About Effects of Early Adversity: A Translational Neuroscience Agenda for Next Generation Addictions Research

PubMed Central

Fisher, Philip A.; Berkman, Elliot T.

2015-01-01

In spite of extensive scientific knowledge about the neurobiological systems and neural pathways underlying addictions, only limited progress has been made to reduce the population-level incidence of addictions by using prevention and treatment programs. In this area of research the translation of basic neuroscience of causal mechanisms to effective interventions has not been fully realized. In this article we describe how an understanding of the effects of early adverse experiences on brain and biological development may provide new opportunities to achieve impact at scale with respect to reduction of addictions. We propose four categories of new knowledge that translational neuroscience investigations of addictions should incorporate to be successful. We then describe a translational neuroscience-informed smoking cessation intervention based on this model. PMID:26985399

In vivo robotics: the automation of neuroscience and other intact-system biological fields.

PubMed

Kodandaramaiah, Suhasa B; Boyden, Edward S; Forest, Craig R

2013-12-01

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to influence neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience and present a concrete example with our recent automation of in vivo whole-cell patch clamp electrophysiology of neurons in the living mouse brain. © 2013 New York Academy of Sciences.

Neurological and developmental approaches to poor pitch perception and production

PubMed Central

Loui, Psyche; Demorest, Steven M.; Pfordresher, Peter Q.; Iyer, Janani

2014-01-01

Whereas much of research in music and neuroscience is aimed at understanding the mechanisms by which the human brain facilitates music, emerging interest in the neuromusic community aims to translate basic music research into clinical and educational applications. In the present workshop, we explore the problems of poor pitch perception and production from both neurological and developmental/educational perspectives. We begin by reviewing previous and novel findings on the neural regulation of pitch perception and production. We then discuss issues in measuring singing accuracy consistently between the laboratory and educational settings. We review the Seattle Singing Accuracy Protocol—a new assessment tool that we hope can be adopted by cognitive psychologists as well as music educators—and we conclude with some suggestions that the present interdisciplinary approach might offer for future research. PMID:25773643

Sleep and Development in Genetically Tractable Model Organisms

PubMed Central

Kayser, Matthew S.; Biron, David

2016-01-01

Sleep is widely recognized as essential, but without a clear singular function. Inadequate sleep impairs cognition, metabolism, immune function, and many other processes. Work in genetic model systems has greatly expanded our understanding of basic sleep neurobiology as well as introduced new concepts for why we sleep. Among these is an idea with its roots in human work nearly 50 years old: sleep in early life is crucial for normal brain maturation. Nearly all known species that sleep do so more while immature, and this increased sleep coincides with a period of exuberant synaptogenesis and massive neural circuit remodeling. Adequate sleep also appears critical for normal neurodevelopmental progression. This article describes recent findings regarding molecular and circuit mechanisms of sleep, with a focus on development and the insights garnered from models amenable to detailed genetic analyses. PMID:27183564

Bodily selves in relation: embodied simulation as second-person perspective on intersubjectivity

PubMed Central

Gallese, Vittorio

2014-01-01

This article addresses basic aspects of social cognition focusing on the pivotal role played by the lived body in the constitution of our experience of others. It is suggested that before studying intersubjectivity we should better qualify the notion of the self. A minimal notion of the self, the bodily self, defined in terms of its motor potentialities, is proposed. The discovery of mirror mechanisms for action, emotions and sensations led to the proposal of an embodied approach to intersubjectivity—embodied simulation (ES) theory. ES and the related notion of neural reuse provide a new empirically based perspective on intersubjectivity, viewed first and foremost as intercorporeality. ES challenges the notion that folk psychology is the sole account of interpersonal understanding. ES is discussed within a second-person perspective on mindreading. PMID:24778374

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Realistic thermodynamic and statistical-mechanical measures for neural synchronization.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2014-04-15

Synchronized brain rhythms, associated with diverse cognitive functions, have been observed in electrical recordings of brain activity. Neural synchronization may be well described by using the population-averaged global potential VG in computational neuroscience. The time-averaged fluctuation of VG plays the role of a "thermodynamic" order parameter O used for describing the synchrony-asynchrony transition in neural systems. Population spike synchronization may be well visualized in the raster plot of neural spikes. The degree of neural synchronization seen in the raster plot is well measured in terms of a "statistical-mechanical" spike-based measure Ms introduced by considering the occupation and the pacing patterns of spikes. The global potential VG is also used to give a reference global cycle for the calculation of Ms. Hence, VG becomes an important collective quantity because it is associated with calculation of both O and Ms. However, it is practically difficult to directly get VG in real experiments. To overcome this difficulty, instead of VG, we employ the instantaneous population spike rate (IPSR) which can be obtained in experiments, and develop realistic thermodynamic and statistical-mechanical measures, based on IPSR, to make practical characterization of the neural synchronization in both computational and experimental neuroscience. Particularly, more accurate characterization of weak sparse spike synchronization can be achieved in terms of realistic statistical-mechanical IPSR-based measure, in comparison with the conventional measure based on VG. Copyright © 2014. Published by Elsevier B.V.

A three dimensional in vitro glial scar model to investigate the local strain effects from micromotion around neural implants.

PubMed

Spencer, Kevin C; Sy, Jay C; Falcón-Banchs, Roberto; Cima, Michael J

2017-02-28

Glial scar formation remains a significant barrier to the long term success of neural probes. Micromotion coupled with mechanical mismatch between the probe and tissue is believed to be a key driver of the inflammatory response. In vitro glial scar models present an intermediate step prior to conventional in vivo histology experiments as they enable cell-device interactions to be tested on a shorter timescale, with the ability to conduct broader biochemical assays. No established in vitro models have incorporated methods to assess device performance with respect to mechanical factors. In this study, we describe an in vitro glial scar model that combines high-precision linear actuators to simulate axial micromotion around neural implants with a 3D primary neural cell culture in a collagen gel. Strain field measurements were conducted to visualize the local displacement within the gel in response to micromotion. Primary brain cell cultures were found to be mechanically responsive to micromotion after one week in culture. Astrocytes, as determined by immunohistochemical staining, were found to have significantly increased in cell areas and perimeters in response to micromotion compared to static control wells. These results demonstrate the importance of micromotion when considering the chronic response to neural implants. Going forward, this model provides advantages over existing in vitro models as it will enable critical mechanical design factors of neural implants to be evaluated prior to in vivo testing.

Signature neural networks: definition and application to multidimensional sorting problems.

PubMed

Latorre, Roberto; de Borja Rodriguez, Francisco; Varona, Pablo

2011-01-01

In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

Techniques for extracting single-trial activity patterns from large-scale neural recordings

PubMed Central

Churchland, Mark M; Yu, Byron M; Sahani, Maneesh; Shenoy, Krishna V

2008-01-01

Summary Large, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies – some employing simultaneous recording, some not – indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior. PMID:18093826

Peripheral oxygen-sensing cells directly modulate the output of an identified respiratory central pattern generating neuron.

PubMed

Bell, Harold J; Inoue, Takuya; Shum, Kelly; Luk, Collin; Syed, Naweed I

2007-06-01

Breathing is an essential homeostatic behavior regulated by central neuronal networks, often called central pattern generators (CPGs). Despite ongoing advances in our understanding of the neural control of breathing, the basic mechanisms by which peripheral input modulates the activities of the central respiratory CPG remain elusive. This lack of fundamental knowledge vis-à-vis the role of peripheral influences in the control of the respiratory CPG is due in large part to the complexity of mammalian respiratory control centres. We have therefore developed a simpler invertebrate model to study the basic cellular and synaptic mechanisms by which a peripheral chemosensory input affects the central respiratory CPG. Here we report on the identification and characterization of peripheral chemoreceptor cells (PCRCs) that relay hypoxia-sensitive chemosensory information to the known respiratory CPG neuron right pedal dorsal 1 in the mollusk Lymnaea stagnalis. Selective perfusion of these PCRCs with hypoxic saline triggered bursting activity in these neurons and when isolated in cell culture these cells also demonstrated hypoxic sensitivity that resulted in membrane depolarization and spiking activity. When cocultured with right pedal dorsal 1, the PCRCs developed synapses that exhibited a form of short-term synaptic plasticity in response to hypoxia. Finally, osphradial denervation in intact animals significantly perturbed respiratory activity compared with their sham counterparts. This study provides evidence for direct synaptic connectivity between a peripheral regulatory element and a central respiratory CPG neuron, revealing a potential locus for hypoxia-induced synaptic plasticity underlying breathing behavior.

Visual training improves perceptual grouping based on basic stimulus features.

PubMed

Kurylo, Daniel D; Waxman, Richard; Kidron, Rachel; Silverstein, Steven M

2017-10-01

Training on visual tasks improves performance on basic and higher order visual capacities. Such improvement has been linked to changes in connectivity among mediating neurons. We investigated whether training effects occur for perceptual grouping. It was hypothesized that repeated engagement of integration mechanisms would enhance grouping processes. Thirty-six participants underwent 15 sessions of training on a visual discrimination task that required perceptual grouping. Participants viewed 20 × 20 arrays of dots or Gabor patches and indicated whether the array appeared grouped as vertical or horizontal lines. Across trials stimuli became progressively disorganized, contingent upon successful discrimination. Four visual dimensions were examined, in which grouping was based on similarity in luminance, color, orientation, and motion. Psychophysical thresholds of grouping were assessed before and after training. Results indicate that performance in all four dimensions improved with training. Training on a control condition, which paralleled the discrimination task but without a grouping component, produced no improvement. In addition, training on only the luminance and orientation dimensions improved performance for those conditions as well as for grouping by color, on which training had not occurred. However, improvement from partial training did not generalize to motion. Results demonstrate that a training protocol emphasizing stimulus integration enhanced perceptual grouping. Results suggest that neural mechanisms mediating grouping by common luminance and/or orientation contribute to those mediating grouping by color but do not share resources for grouping by common motion. Results are consistent with theories of perceptual learning emphasizing plasticity in early visual processing regions.

The contribution of the left anterior ventrolateral temporal lobe to the retrieval of personal semantics.

PubMed

Grilli, Matthew D; Bercel, John J; Wank, Aubrey A; Rapcsak, Steven Z

2018-06-04

Autobiographical facts and personal trait knowledge are conceptualized as distinct types of personal semantics, but the cognitive and neural mechanisms that separate them remain underspecified. One distinction may be their level of specificity, with autobiographical facts reflecting idiosyncratic conceptual knowledge and personal traits representing basic level category knowledge about the self. Given the critical role of the left anterior ventrolateral temporal lobe (AVTL) in the storage and retrieval of semantic information about unique entities, we hypothesized that knowledge of autobiographical facts may depend on the integrity of this region to a greater extent than personal traits. To provide neuropsychological evidence relevant to this issue, we investigated personal semantics, semantic knowledge of non-personal unique entities, and episodic memory in two individuals with well-defined left (MK) versus right (DW) AVTL lesions. Relative to controls, MK demonstrated preserved personal trait knowledge but impaired "experience-far" (i.e., spatiotemporal independent) autobiographical fact knowledge, semantic memory for non-personal unique entities, and episodic memory. In contrast, both experience-far autobiographical facts and personal traits were spared in DW, whereas episodic memory and aspects of semantic memory for non-personal unique entities were impaired. These findings support the notion that autobiographical facts and personal traits have distinct cognitive features and neural mechanisms. They also suggest a common organizing principle for personal and non-personal semantics, namely the specificity of such knowledge to an entity, which is reflected in the contribution of the left AVTL to retrieval. Copyright © 2018 Elsevier Ltd. All rights reserved.

The response of L5 pyramidal neurons of the PFC to magnetic stimulation from a micro-coil.

PubMed

Lee, Seung Woo; Fried, Shelley I

2014-01-01

Magnetic stimulation of the nervous system, e.g. transcranial magnetic stimulation (TMS), has been used both to unravel basic structure and function of the nervous system as well as to treat neurological diseases, i.e. clinical depression. Despite progress in both areas, ongoing advancements have been limited by a lack of understanding of the mechanism by which magnetic stimulation alters neural activity. Here, we report responses of cortical neurons to magnetic stimulation arising from a sub-millimeter coil. Cell attached patch clamp was used to record neural activity of layer 5/6 pyramidal neurons of the prefrontal cortex (PFC) in the in vitro mouse brain slice preparation. The fields arising from the small coil were quite different from those arising during clinical TMS but nevertheless allowed the responses of cortical neurons to magnetic stimulation to be probed. For example, the focal nature of induced fields allowed the sensitivity of different regions within targeted pyramidal neurons, e.g. apical dendrite, soma and axon hillock, to be compared. We found that PFC pyramidal neurons were not sensitive to single pulses of stimulation regardless of coil location. However, regions of the apical dendrite and proximal axon were both sensitive to repetitive stimulation as long as the orientation of the induced electric field was aligned with the long axis of the neuron. These results suggest that neurons of the PFC are sensitive to weak magnetic fields and further, that this type of approach may be useful for unraveling some of the mechanisms underlying TMS.

Neural assembly computing.

PubMed

Ranhel, João

2012-06-01

Spiking neurons can realize several computational operations when firing cooperatively. This is a prevalent notion, although the mechanisms are not yet understood. A way by which neural assemblies compute is proposed in this paper. It is shown how neural coalitions represent things (and world states), memorize them, and control their hierarchical relations in order to perform algorithms. It is described how neural groups perform statistic logic functions as they form assemblies. Neural coalitions can reverberate, becoming bistable loops. Such bistable neural assemblies become short- or long-term memories that represent the event that triggers them. In addition, assemblies can branch and dismantle other neural groups generating new events that trigger other coalitions. Hence, such capabilities and the interaction among assemblies allow neural networks to create and control hierarchical cascades of causal activities, giving rise to parallel algorithms. Computing and algorithms are used here as in a nonstandard computation approach. In this sense, neural assembly computing (NAC) can be seen as a new class of spiking neural network machines. NAC can explain the following points: 1) how neuron groups represent things and states; 2) how they retain binary states in memories that do not require any plasticity mechanism; and 3) how branching, disbanding, and interaction among assemblies may result in algorithms and behavioral responses. Simulations were carried out and the results are in agreement with the hypothesis presented. A MATLAB code is available as a supplementary material.

Musical Emotions: Functions, Origins, Evolution

DTIC Science & Technology

2010-01-01

might be contentious) neural mechanisms added to our perception of originally mechanical properties of ear. I’ll add that Helmholtz did not touch the main...significant part of conceptual perception is an unconscious process ; for example, visual perception takes about 150 ms, which is a long time when measured...missing in terms of neural mechanisms? How do children learn which words and sentences correspond to which objects and situations? Many psychologists

Neural Mechanisms of Selective Visual Attention.

PubMed

Moore, Tirin; Zirnsak, Marc

2017-01-03

Selective visual attention describes the tendency of visual processing to be confined largely to stimuli that are relevant to behavior. It is among the most fundamental of cognitive functions, particularly in humans and other primates for whom vision is the dominant sense. We review recent progress in identifying the neural mechanisms of selective visual attention. We discuss evidence from studies of different varieties of selective attention and examine how these varieties alter the processing of stimuli by neurons within the visual system, current knowledge of their causal basis, and methods for assessing attentional dysfunctions. In addition, we identify some key questions that remain in identifying the neural mechanisms that give rise to the selective processing of visual information.

Using Neural Networks in Decision Making for a Reconfigurable Electro Mechanical Actuator (EMA)

NASA Technical Reports Server (NTRS)

Latino, Carl D.

2001-01-01

The objectives of this project were to demonstrate applicability and advantages of a neural network approach for evaluating the performance of an electro-mechanical actuator (EMA). The EMA in question was intended for the X-37 Advanced Technology Vehicle. It will have redundant components for safety and reliability. The neural networks for this application are to monitor the operation of the redundant electronics that control the actuator in real time and decide on the operating configuration. The system we proposed consists of the actuator, sensors, control circuitry and dedicated (embedded) processors. The main purpose of the study was to develop suitable hardware and neural network capable of allowing real time reconfiguration decisions to be made. This approach was to be compared to other methods such as fuzzy logic and knowledge based systems considered for the same application. Over the course of the project a more general objective was the identification of the other neural network applications and the education of interested NASA personnel on the topic of Neural Networks.

BOOK REVIEW: Theory of Neural Information Processing Systems

NASA Astrophysics Data System (ADS)

Galla, Tobias

2006-04-01

It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the existing textbook literature, so that this discussion will be very much appreciated. The book is of an exceptionally high quality in all aspects. In my view, the style of presentation and the inclusion of recent aspects of the topic alone make the book a welcomed addition to the existing literature. It is well structured and the material covered was chosen with care. While focusing on quantitative aspects of the subject, the authors adopt a comprehensive style of presentation, being precise, but not pedantic. The student who is not familiar with the field might find the breadth of the book overwhelming at first, but will soon appreciate its pedagogical value. All mathematical derivations are performed and explained step by step for the student to follow, and they are illustrated by many concrete examples and results from computer simulations in well-presented and clear figures. If a student wants to get his hands on the mathematical tools of neural networks theory then this book is a good place to learn from. A set of instructive and valuable exercises complements each chapter (hints are given, but maybe it would have been nice to provide additional brief sample solutions in an appendix). I very much enjoyed the outlook sections at the end of each of the five parts, putting the material covered into its historical context and providing further references. In summary, students of a quantitative discipline will find in this book a clear and self-contained introduction to the subject, lecturers might use it to design postgraduate courses, and finally it will provide a valuable reference for researchers working in the area. This book can be expected to be an asset for all types of readers, even if they already own a book on neural networks. Anyone with a serious interest in the theoretical aspects of the field would be making a mistake not to have a copy on their shelves.

Perceptual learning of degraded speech by minimizing prediction error.

PubMed

Sohoglu, Ediz; Davis, Matthew H

2016-03-22

Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech.

Perceptual learning of degraded speech by minimizing prediction error

PubMed Central

Sohoglu, Ediz

2016-01-01

Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech. PMID:26957596

Music modulation of pain perception and pain-related activity in the brain, brain stem, and spinal cord: a functional magnetic resonance imaging study.

PubMed

Dobek, Christine E; Beynon, Michaela E; Bosma, Rachael L; Stroman, Patrick W

2014-10-01

The oldest known method for relieving pain is music, and yet, to date, the underlying neural mechanisms have not been studied. Here, we investigate these neural mechanisms by applying a well-defined painful stimulus while participants listened to their favorite music or to no music. Neural responses in the brain, brain stem, and spinal cord were mapped with functional magnetic resonance imaging spanning the cortex, brain stem, and spinal cord. Subjective pain ratings were observed to be significantly lower when pain was administered with music than without music. The pain stimulus without music elicited neural activity in brain regions that are consistent with previous studies. Brain regions associated with pleasurable music listening included limbic, frontal, and auditory regions, when comparing music to non-music pain conditions. In addition, regions demonstrated activity indicative of descending pain modulation when contrasting the 2 conditions. These regions include the dorsolateral prefrontal cortex, periaqueductal gray matter, rostral ventromedial medulla, and dorsal gray matter of the spinal cord. This is the first imaging study to characterize the neural response of pain and how pain is mitigated by music, and it provides new insights into the neural mechanism of music-induced analgesia within the central nervous system. This article presents the first investigation of neural processes underlying music analgesia in human participants. Music modulates pain responses in the brain, brain stem, and spinal cord, and neural activity changes are consistent with engagement of the descending analgesia system. Copyright © 2014 American Pain Society. Published by Elsevier Inc. All rights reserved.

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

PubMed Central

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

2016-01-01

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration. PMID:28774035

Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

PubMed Central

2014-01-01

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility. PMID:25276860

Neural correlates of impaired emotion processing in manifest Huntington's disease.

PubMed

Dogan, Imis; Saß, Christian; Mirzazade, Shahram; Kleiman, Alexandra; Werner, Cornelius J; Pohl, Anna; Schiefer, Johannes; Binkofski, Ferdinand; Schulz, Jörg B; Shah, N Jon; Reetz, Kathrin

2014-05-01

The complex phenotype of Huntington's disease (HD) encompasses motor, psychiatric and cognitive dysfunctions, including early impairments in emotion recognition. In this first functional magnetic resonance imaging study, we investigated emotion-processing deficits in 14 manifest HD patients and matched controls. An emotion recognition task comprised short video clips displaying one of six basic facial expressions (sadness, happiness, disgust, fear, anger and neutral). Structural changes between patients and controls were assessed by means of voxel-based morphometry. Along with deficient recognition of negative emotions, patients exhibited predominantly lower neural response to stimuli of negative valences in the amygdala, hippocampus, striatum, insula, cingulate and prefrontal cortices, as well as in sensorimotor, temporal and visual areas. Most of the observed reduced activity patterns could not be explained merely by regional volume loss. Reduced activity in the thalamus during fear correlated with lower thalamic volumes. During the processing of sadness, patients exhibited enhanced amygdala and hippocampal activity along with reduced recruitment of the medial prefrontal cortex. Higher amygdala activity was related to more pronounced amygdala atrophy and disease burden. Overall, the observed emotion-related dysfunctions in the context of structural neurodegeneration suggest both disruptions of striatal-thalamo-cortical loops and potential compensation mechanism with greater disease severity in manifest HD.

Beautiful friendship: Social sharing of emotions improves subjective feelings and activates the neural reward circuitry

PubMed Central

Galli, Lisa; Schott, Björn H.; Wold, Andrew; van der Schalk, Job; Manstead, Antony S. R.; Scherer, Klaus; Walter, Henrik

2015-01-01

Humans have a strong tendency to affiliate with other people, especially in emotional situations. Here, we suggest that a critical mechanism underlying this tendency is that socially sharing emotional experiences is in itself perceived as hedonically positive and thereby contributes to the regulation of individual emotions. We investigated the effect of social sharing of emotions on subjective feelings and neural activity by having pairs of friends view emotional (negative and positive) and neutral pictures either alone or with the friend. While the two friends remained physically separated throughout the experiment—with one undergoing functional magnetic resonance imaging and the other performing the task in an adjacent room—they were made aware on a trial-by-trial basis whether they were seeing pictures simultaneously with their friend (shared) or alone (unshared). Ratings of subjective feelings were improved significantly when participants viewed emotional pictures together than alone, an effect that was accompanied by activity increase in ventral striatum and medial orbitofrontal cortex, two important components of the reward circuitry. Because these effects occurred without any communication or interaction between the friends, they point to an important proximate explanation for the basic human motivation to affiliate with others, particularly in emotional situations. PMID:25298009

A Computational Framework for Realistic Retina Modeling.

PubMed

Martínez-Cañada, Pablo; Morillas, Christian; Pino, Begoña; Ros, Eduardo; Pelayo, Francisco

2016-11-01

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

Mindful attention to breath regulates emotions via increased amygdala-prefrontal cortex connectivity.

PubMed

Doll, Anselm; Hölzel, Britta K; Mulej Bratec, Satja; Boucard, Christine C; Xie, Xiyao; Wohlschläger, Afra M; Sorg, Christian

2016-07-01

Mindfulness practice is beneficial for emotion regulation; however, the neural mechanisms underlying this effect are poorly understood. The current study focuses on effects of attention-to-breath (ATB) as a basic mindfulness practice on aversive emotions at behavioral and brain levels. A key finding across different emotion regulation strategies is the modulation of amygdala and prefrontal activity. It is unclear how ATB relevant brain areas in the prefrontal cortex integrate with amygdala activation during emotional stimulation. We proposed that, during emotional stimulation, ATB down-regulates activation in the amygdala and increases its integration with prefrontal regions. To address this hypothesis, 26 healthy controls were trained in mindfulness-based attention-to-breath meditation for two weeks and then stimulated with aversive pictures during both attention-to-breath and passive viewing while undergoing fMRI. Data were controlled for breathing frequency. Results indicate that (1) ATB was effective in regulating aversive emotions. (2) Left dorso-medial prefrontal cortex was associated with ATB in general. (3) A fronto-parietal network was additionally recruited during emotional stimulation. (4) ATB down regulated amygdala activation and increased amygdala-prefrontal integration, with such increased integration being associated with mindfulness ability. Results suggest amygdala-dorsal prefrontal cortex integration as a potential neural pathway of emotion regulation by mindfulness practice. Copyright © 2016 Elsevier Inc. All rights reserved.

Adaptive walking of a quadrupedal robot based on layered biological reflexes

NASA Astrophysics Data System (ADS)

Zhang, Xiuli; Mingcheng, E.; Zeng, Xiangyu; Zheng, Haojun

2012-07-01

A multiple-legged robot is traditionally controlled by using its dynamic model. But the dynamic-model-based approach fails to acquire satisfactory performances when the robot faces rough terrains and unknown environments. Referring animals' neural control mechanisms, a control model is built for a quadruped robot walking adaptively. The basic rhythmic motion of the robot is controlled by a well-designed rhythmic motion controller(RMC) comprising a central pattern generator(CPG) for hip joints and a rhythmic coupler (RC) for knee joints. CPG and RC have relationships of motion-mapping and rhythmic couple. Multiple sensory-motor models, abstracted from the neural reflexes of a cat, are employed. These reflex models are organized and thus interact with the CPG in three layers, to meet different requirements of complexity and response time to the tasks. On the basis of the RMC and layered biological reflexes, a quadruped robot is constructed, which can clear obstacles and walk uphill and downhill autonomously, and make a turn voluntarily in uncertain environments, interacting with the environment in a way similar to that of an animal. The paper provides a biologically inspired architecture, with which a robot can walk adaptively in uncertain environments in a simple and effective way, and achieve better performances.

Decoding the cortical transformations for visually guided reaching in 3D space.

PubMed

Blohm, Gunnar; Keith, Gerald P; Crawford, J Douglas

2009-06-01

To explore the possible cortical mechanisms underlying the 3-dimensional (3D) visuomotor transformation for reaching, we trained a 4-layer feed-forward artificial neural network to compute a reach vector (output) from the visual positions of both the hand and target viewed from different eye and head orientations (inputs). The emergent properties of the intermediate layers reflected several known neurophysiological findings, for example, gain field-like modulations and position-dependent shifting of receptive fields (RFs). We performed a reference frame analysis for each individual network unit, simulating standard electrophysiological experiments, that is, RF mapping (unit input), motor field mapping, and microstimulation effects (unit outputs). At the level of individual units (in both intermediate layers), the 3 different electrophysiological approaches identified different reference frames, demonstrating that these techniques reveal different neuronal properties and suggesting that a comparison across these techniques is required to understand the neural code of physiological networks. This analysis showed fixed input-output relationships within each layer and, more importantly, within each unit. These local reference frame transformation modules provide the basic elements for the global transformation; their parallel contributions are combined in a gain field-like fashion at the population level to implement both the linear and nonlinear elements of the 3D visuomotor transformation.

Beautiful friendship: Social sharing of emotions improves subjective feelings and activates the neural reward circuitry.

PubMed

Wagner, Ullrich; Galli, Lisa; Schott, Björn H; Wold, Andrew; van der Schalk, Job; Manstead, Antony S R; Scherer, Klaus; Walter, Henrik

2015-06-01

Humans have a strong tendency to affiliate with other people, especially in emotional situations. Here, we suggest that a critical mechanism underlying this tendency is that socially sharing emotional experiences is in itself perceived as hedonically positive and thereby contributes to the regulation of individual emotions. We investigated the effect of social sharing of emotions on subjective feelings and neural activity by having pairs of friends view emotional (negative and positive) and neutral pictures either alone or with the friend. While the two friends remained physically separated throughout the experiment-with one undergoing functional magnetic resonance imaging and the other performing the task in an adjacent room-they were made aware on a trial-by-trial basis whether they were seeing pictures simultaneously with their friend (shared) or alone (unshared). Ratings of subjective feelings were improved significantly when participants viewed emotional pictures together than alone, an effect that was accompanied by activity increase in ventral striatum and medial orbitofrontal cortex, two important components of the reward circuitry. Because these effects occurred without any communication or interaction between the friends, they point to an important proximate explanation for the basic human motivation to affiliate with others, particularly in emotional situations. © The Author (2014). Published by Oxford University Press.

fMRI Syntactic and Lexical Repetition Effects Reveal the Initial Stages of Learning a New Language.

PubMed

Weber, Kirsten; Christiansen, Morten H; Petersson, Karl Magnus; Indefrey, Peter; Hagoort, Peter

2016-06-29

When learning a new language, we build brain networks to process and represent the acquired words and syntax and integrate these with existing language representations. It is an open question whether the same or different neural mechanisms are involved in learning and processing a novel language compared with the native language(s). Here we investigated the neural repetition effects of repeating known and novel word orders while human subjects were in the early stages of learning a new language. Combining a miniature language with a syntactic priming paradigm, we examined the neural correlates of language learning on-line using functional magnetic resonance imaging. In left inferior frontal gyrus and posterior temporal cortex, the repetition of novel syntactic structures led to repetition enhancement, whereas repetition of known structures resulted in repetition suppression. Additional verb repetition led to an increase in the syntactic repetition enhancement effect in language-related brain regions. Similarly, the repetition of verbs led to repetition enhancement effects in areas related to lexical and semantic processing, an effect that continued to increase in a subset of these regions. Repetition enhancement might reflect a mechanism to build and strengthen a neural network to process novel syntactic structures and lexical items. By contrast, the observed repetition suppression points to overlapping neural mechanisms for native and new language constructions when these have sufficient structural similarities. Acquiring a second language entails learning how to interpret novel words and relations between words, and to integrate them with existing language knowledge. To investigate the brain mechanisms involved in this particularly human skill, we combined an artificial language learning task with a syntactic repetition paradigm. We show that the repetition of novel syntactic structures, as well as words in contexts, leads to repetition enhancement, whereas repetition of known structures results in repetition suppression. We thus propose that repetition enhancement might reflect a brain mechanism to build and strengthen a neural network to process novel syntactic regularities and novel words. Importantly, the results also indicate an overlap in neural mechanisms for native and new language constructions with sufficient structural similarities. Copyright © 2016 the authors 0270-6474/16/366872-09$15.00/0.

Sentential Negation Might Share Neurophysiological Mechanisms with Action Inhibition. Evidence from Frontal Theta Rhythm.

PubMed

de Vega, Manuel; Morera, Yurena; León, Inmaculada; Beltrán, David; Casado, Pilar; Martín-Loeches, Manuel

2016-06-01

According to the literature, negations such as "not" or "don't" reduce the accessibility in memory of the concepts under their scope. Moreover, negations applied to action contents (e.g., "don't write the letter") impede the activation of motor processes in the brain, inducing "disembodied" representations. These facts provide important information on the behavioral and neural consequences of negations. However, how negations themselves are processed in the brain is still poorly understood. In two electrophysiological experiments, we explored whether sentential negation shares neural mechanisms with action monitoring or inhibition. Human participants read action-related sentences in affirmative or negative form ("now you will cut the bread" vs "now you will not cut the bread") while performing a simultaneous Go/NoGo task. The analysis of the EEG rhythms revealed that theta oscillations were significantly reduced for NoGo trials in the context of negative sentences compared with affirmative sentences. Given the fact that theta oscillations are often considered as neural markers of response inhibition processes, their modulation by negative sentences strongly suggests that negation uses neural resources of response inhibition. We propose a new approach that views the syntactic operator of negation as relying on the neural machinery of high-order action-monitoring processes. Previous studies have shown that linguistic negation reduces the accessibility of the negated concepts and suppresses the activation of specific brain regions that operate in affirmative statements. Although these studies focus on the consequences of negation on cognitive and neural processes, the proper neural mechanisms of negation have not yet been explored. In the present EEG study, we tested the hypothesis that negation uses the neural network of action inhibition. Using a Go/NoGo task embedded in a sentence comprehension task, we found that negation in the context of NoGo trials modulates frontal theta rhythm, which is usually considered a signature of action inhibition and control mechanisms. Copyright © 2016 the authors 0270-6474/16/366002-09$15.00/0.

A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

PubMed

Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

2009-01-01

For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

ERIC Educational Resources Information Center

Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-01-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Neural Issues in the Control of Muscular Strength

ERIC Educational Resources Information Center

Kamen, Gary

2004-01-01

During the earliest stages of resistance exercise training, initial muscular strength gains occur too rapidly to be explained solely by muscle-based mechanisms. However, increases in surface-based EMG amplitude as well as motor unit discharge rate provide some insight to the existence of neural mechanisms in the earliest phases of resistance…

Neural Mechanisms of Interference Control and Time Discrimination in Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Vloet, Timo D.; Gilsbach, Susanne; Neufang, Susanne; Fink, Gereon R.; Herpertz-Dahlmann, Beate; Konrad, Kerstin

2010-01-01

Objective: Both executive functions and time perception are typically impaired in subjects with attention-deficit/hyperactivity disorder (ADHD). However, the exact neural mechanisms underlying these deficits remain to be investigated. Method: Fourteen subjects with ADHD and 14 age- and IQ-matched controls (aged 9 through 15 years) were assessed…

Neural circuitry and plasticity mechanisms underlying delay eyeblink conditioning

PubMed Central

Freeman, John H.; Steinmetz, Adam B.

2011-01-01

Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of parallel fiber synapses on Purkinje cells and long-term potentiation of mossy fiber synapses on neurons in the anterior interpositus nucleus. Conditioned stimulus and unconditioned stimulus inputs arise from the pontine nuclei and inferior olive, respectively, converging in the cerebellar cortex and deep nuclei. Projections from subcortical sensory nuclei to the pontine nuclei that are necessary for eyeblink conditioning are beginning to be identified, and recent studies indicate that there are dynamic interactions between sensory thalamic nuclei and the cerebellum during eyeblink conditioning. Cerebellar output is projected to the magnocellular red nucleus and then to the motor nuclei that generate the blink response(s). Tremendous progress has been made toward determining the neural mechanisms of delay eyeblink conditioning but there are still significant gaps in our understanding of the necessary neural circuitry and plasticity mechanisms underlying cerebellar learning. PMID:21969489

[Research Progress on the Interaction Effects and Its Neural Mechanisms between Physical Fatigue and Mental Fatigue].

PubMed

Zhang, Lixin; Zhang, Chuncui; He, Feng; Zhao, Xin; Qi, Hongzhi; Wan, Baikun; Ming, Dong

2015-10-01

Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.

An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity

PubMed Central

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities–0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking. PMID:28337137

The Neural Basis of Aversive Pavlovian Guidance during Planning

PubMed Central

Faulkner, Paul

2017-01-01

Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences is often impractical; thus, cognitive shortcuts are often essential. One pervasive and powerful heuristic is aversive pruning, in which potential decision-making avenues are curtailed at immediate negative outcomes. We used neuroimaging to examine how humans implement such pruning. We found it to be associated with activity in the subgenual cingulate cortex, with neural signatures that were distinguishable from those covarying with planning and valuation. Individual variations in aversive pruning levels related to subclinical anxiety levels and insular cortex activation. These findings reveal the neural mechanisms by which basic negative Pavlovian influences guide decision-making during planning, with implications for disrupted decision-making in psychiatric disorders. PMID:28924006

The Neural Basis of Aversive Pavlovian Guidance during Planning.

PubMed

Lally, Níall; Huys, Quentin J M; Eshel, Neir; Faulkner, Paul; Dayan, Peter; Roiser, Jonathan P

2017-10-18

Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences is often impractical; thus, cognitive shortcuts are often essential. One pervasive and powerful heuristic is aversive pruning, in which potential decision-making avenues are curtailed at immediate negative outcomes. We used neuroimaging to examine how humans implement such pruning. We found it to be associated with activity in the subgenual cingulate cortex, with neural signatures that were distinguishable from those covarying with planning and valuation. Individual variations in aversive pruning levels related to subclinical anxiety levels and insular cortex activation. These findings reveal the neural mechanisms by which basic negative Pavlovian influences guide decision-making during planning, with implications for disrupted decision-making in psychiatric disorders. Copyright © 2017 the authors 0270-6474/17/3710216-15$15.00/0.

Evolution and Optimality of Similar Neural Mechanisms for Perception and Action during Search

PubMed Central

Zhang, Sheng; Eckstein, Miguel P.

2010-01-01

A prevailing theory proposes that the brain's two visual pathways, the ventral and dorsal, lead to differing visual processing and world representations for conscious perception than those for action. Others have claimed that perception and action share much of their visual processing. But which of these two neural architectures is favored by evolution? Successful visual search is life-critical and here we investigate the evolution and optimality of neural mechanisms mediating perception and eye movement actions for visual search in natural images. We implement an approximation to the ideal Bayesian searcher with two separate processing streams, one controlling the eye movements and the other stream determining the perceptual search decisions. We virtually evolved the neural mechanisms of the searchers' two separate pathways built from linear combinations of primary visual cortex receptive fields (V1) by making the simulated individuals' probability of survival depend on the perceptual accuracy finding targets in cluttered backgrounds. We find that for a variety of targets, backgrounds, and dependence of target detectability on retinal eccentricity, the mechanisms of the searchers' two processing streams converge to similar representations showing that mismatches in the mechanisms for perception and eye movements lead to suboptimal search. Three exceptions which resulted in partial or no convergence were a case of an organism for which the targets are equally detectable across the retina, an organism with sufficient time to foveate all possible target locations, and a strict two-pathway model with no interconnections and differential pre-filtering based on parvocellular and magnocellular lateral geniculate cell properties. Thus, similar neural mechanisms for perception and eye movement actions during search are optimal and should be expected from the effects of natural selection on an organism with limited time to search for food that is not equi-detectable across its retina and interconnected perception and action neural pathways. PMID:20838589

Pay What You Want! A Pilot Study on Neural Correlates of Voluntary Payments for Music

PubMed Central

Waskow, Simon; Markett, Sebastian; Montag, Christian; Weber, Bernd; Trautner, Peter; Kramarz, Volkmar; Reuter, Martin

2016-01-01

Pay-what-you-want (PWYW) is an alternative pricing mechanism for consumer goods. It describes an exchange situation in which the price for a given good is not set by the seller but freely chosen by the buyer. In recent years, many enterprises have made use of PWYW auctions. The somewhat contra-intuitive success of PWYW has sparked a great deal of behavioral work on economical decision making in PWYW contexts in the past. Empirical studies on the neural basis of PWYW decisions, however, are scarce. In the present paper, we present an experimental protocol to study PWYW decision making while simultaneously acquiring functional magnetic resonance imaging data. Participants have the possibility to buy music either under a traditional “fixed-price” (FP) condition or in a condition that allows them to freely decide on the price. The behavioral data from our experiment replicate previous results on the general feasibility of the PWYW mechanism. On the neural level, we observe distinct differences between the two conditions: In the FP-condition, neural activity in frontal areas during decision-making correlates positively with the participants’ willingness to pay. No such relationship was observed under PWYW conditions in any neural structure. Directly comparing neural activity during PWYW and the FP-condition we observed stronger activity of the lingual gyrus during PWYW decisions. Results demonstrate the usability of our experimental paradigm for future investigations into PWYW decision-making and provides first insights into neural mechanisms during self-determined pricing decisions. PMID:27458416

Learning in neural networks based on a generalized fluctuation theorem

NASA Astrophysics Data System (ADS)

Hayakawa, Takashi; Aoyagi, Toshio

2015-11-01

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

Pop-out in visual search of moving targets in the archer fish.

PubMed

Ben-Tov, Mor; Donchin, Opher; Ben-Shahar, Ohad; Segev, Ronen

2015-03-10

Pop-out in visual search reflects the capacity of observers to rapidly detect visual targets independent of the number of distracting objects in the background. Although it may be beneficial to most animals, pop-out behaviour has been observed only in mammals, where neural correlates are found in primary visual cortex as contextually modulated neurons that encode aspects of saliency. Here we show that archer fish can also utilize this important search mechanism by exhibiting pop-out of moving targets. We explore neural correlates of this behaviour and report the presence of contextually modulated neurons in the optic tectum that may constitute the neural substrate for a saliency map. Furthermore, we find that both behaving fish and neural responses exhibit additive responses to multiple visual features. These findings suggest that similar neural computations underlie pop-out behaviour in mammals and fish, and that pop-out may be a universal search mechanism across all vertebrates.

Hydraulic and separation characteristics of an industrial gas centrifuge calculated with neural networks

NASA Astrophysics Data System (ADS)

Butov, Vladimir; Timchenko, Sergey; Ushakov, Ivan; Golovkov, Nikita; Poberezhnikov, Andrey

2018-03-01

Single gas centrifuge (GC) is generally used for the separation of binary mixtures of isotopes. Processes taking place within the centrifuge are complex and non-linear. Their characteristics can change over time with long-term operation due to wear of the main structural elements of the GC construction. The paper is devoted to the determination of basic operation parameters of the centrifuge with the help of neural networks. We have developed a method for determining the parameters of the industrial GC operation by processing statistical data. In this work, we have constructed a neural network that is capable of determining the main hydraulic and separation characteristics of the gas centrifuge, depending on the geometric dimensions of the gas centrifuge, load value, and rotor speed.

Simbrain 3.0: A flexible, visually-oriented neural network simulator.

PubMed

Tosi, Zachary; Yoshimi, Jeffrey

2016-11-01

Simbrain 3.0 is a software package for neural network design and analysis, which emphasizes flexibility (arbitrarily complex networks can be built using a suite of basic components) and a visually rich, intuitive interface. These features support both students and professionals. Students can study all of the major classes of neural networks in a familiar graphical setting, and can easily modify simulations, experimenting with networks and immediately seeing the results of their interventions. With the 3.0 release, Simbrain supports models on the order of thousands of neurons and a million synapses. This allows the same features that support education to support research professionals, who can now use the tool to quickly design, run, and analyze the behavior of large, highly customizable simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

NASA Astrophysics Data System (ADS)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Optimizing the performance of neural interface devices with hybrid poly(3,4-ethylene dioxythiophene) (PEDOT)

NASA Astrophysics Data System (ADS)

Kuo, Chin-chen

This thesis describes methods for improving the performance of poly(3,4-ethylenedioxythiophene) (PEDOT) as a direct neural interfacing material. The chronic foreign body response is always a challenge for implanted bionic devices. After long-term implantation (typically 2-4 weeks), insulating glial scars form around the devices, inhibiting signal transmission, which ultimately leads to device failure. The mechanical mismatch at the device-tissue interface is one of the issues that has been associated with chronic foreign body response. Another challenge for using PEDOT as a neural interface material is its mechanical failure after implantation. We observed cracking and delamination of PEDOT coatings on devices after extended implantations. In the first part of this thesis, we present a novel method for directly measuring the mechanical properties of a PEDOT thin film. Before investigating methods to improve the mechanical behavior of PEDOT, a comprehensive understanding of the mechanical properties of PEDOT thin film is required. A PEDOT thin film was machined into a dog-bone shape specimen with a dual beam FIB-SEM. With an OmniProbe, this PEDOT specimen could be attached onto a force sensor, while the other side was attached to OmniProbe. By moving the OmniProbe, the specimen could be deformed in tension, and a force sensor recorded the applied load on the sample simultaneously. Mechanical tensile tests were conducted in the FIB-SEM chamber along with in situ observation. With precise force measurement from the force sensor and the corresponding high resolution SEM images, we were able to convert the data to a stress-strain curve for further analysis. By analyzing these stress-strain curves, we were able to obtain information about PEDOT including the Young's modulus, strength of failure, strain to failure, and toughness (energy to failure). This information should be useful for future material selection and molecular design for specific applications. The second section of this thesis is mainly focused on developing a soft and conductive material by in situ PEDOT polymerization into soft matrix. First, PEDOT was in situ polymerized into extracellular matrix (ECM) as a conductive, soft, and bioactive material for neural interfacing. ECM is basically a matrix of proteins which provides biological cues with the potential to promote neural attachment. We modified the electrode to a needle shape, which could be inserted into the ECM film. The limited surface area on the electrode and the close contact with ECM made it possible to polymerize PEDOT into the ECM more easily. The conductivity of PEDOT-ECM was confirmed to be similar to intrinsic PEDOT. A cell adhesion test using the PC12 cell line was used to evaluate its biocompatibility. PEDOT-ECM shows improved cell adhesion for PC12 cells, as compared either bare metal electrodes or PEDOT coated surfaces. In the future this approach may be elevated to an " autologous" concept, where the ECM could be derived from the host patients themselves to further reduce the foreign body response. Second, low modulus hydrogels were used as templates for PEDOT polymerization. EDOT monomers were premixed into agarose hydrogels. The electrochemical polymerization was typically conducted in potentiostatic (constant voltage) mode with working voltage of 2 V. After 0.8 C/cm2 charge density, a significant dark blue cloud was observed indicating that PEDOT was in situ polymerized into hydrogel matrix. A series of studies was conducted to confirm the improved mechanical properties, electrical properties and biocompatibility of the PEDOT-gel as compared to the typical solid PEDOT. Animal studies were conducted to evaluate the performance of PEDOT-gel coated electrode in vivo. Rats were used as the animal model with 3 rats in each group of bare electrode, PEDOT-coated, and PEDOT-gel coated electrode (n=9). The in vivo impedance was used to confirm the performance of the implanted electrodes. The results showed that the impedance had a significant increase after 4 weeks with the bare and solid PEDOT-coated electrode. This is consistent with the typical glial scar encapsulation around the electrode leading to an impedance increase. PEDOT-gel presents consistently low impedance along with 10 weeks implantation implying there was much less reactive response around the insertion site. These in vivo experiments on PEDOT-gels suggest that PEDOT-gels are promising neural interfacing materials for patients clinically.

Functional imaging studies of emotion regulation: A synthetic review and evolving model of the cognitive control of emotion

PubMed Central

Ochsner, Kevin N.; Silvers, Jennifer A.; Buhle, Jason T.

2014-01-01

This paper reviews and synthesizes functional imaging research that over the past decade has begun to offer new insights into the brain mechanisms underlying emotion regulation. Towards that end, the first section of the paper outlines a model of the processes and neural systems involved in emotion generation and regulation. The second section surveys recent research supporting and elaborating the model, focusing primarily on studies of the most commonly investigated strategy, which is known as reappraisal. At its core, the model specifies how prefrontal and cingulate control systems modulate activity in perceptual, semantic and affect systems as a function of one's regulatory goals, tactics, and the nature of the stimuli and emotions being regulated. This section also shows how the model can be generalized to understand the brain mechanisms underlying other emotion regulation strategies as well as a range of other allied phenomena. The third and last section considers directions for future research, including how basic models of emotion regulation can be translated to understand changes in emotion across the lifespan and in clinical disorders. PMID:23025352

Muscle Co-activation: Definitions, Mechanisms, and Functions.

PubMed

Latash, Mark L

2018-03-28

The phenomenon of agonist-antagonist muscle co-activation is discussed with respect to its consequences for movement mechanics (such as increasing joint apparent stiffness, facilitating faster movements, and effects on action stability), implication for movement optimization, and involvement of different neurophysiological structures. Effects of co-activation on movement stability are ambiguous and depend on the effector representing a kinematic chain with a fixed origin or free origin. Further, co-activation is discussed within the framework of the equilibrium-point hypothesis and the idea of hierarchical control with spatial referent coordinates. Relations of muscle co-activation to changes in one of the basic commands, the c-command, are discussed and illustrated. A hypothesis is suggested that agonist-antagonist co-activation reflects a deliberate neural control strategy to preserve effector-level control and avoid making it degenerate and facing the necessity to control at the level of signals to individual muscles. This strategy, in particular, allows stabilizing motor actions by co-varied adjustments in spaces of control variables. This hypothesis is able to account for higher levels of co-activation in young healthy persons performing challenging tasks and across various populations with movement impairments.

What neuropsychology tells us about human tool use? The four constraints theory (4CT): mechanics, space, time, and effort.

PubMed

Osiurak, François

2014-06-01

Our understanding of human tool use comes mainly from neuropsychology, particularly from patients with apraxia or action disorganization syndrome. However, there is no integrative, theoretical framework explaining what these neuropsychological syndromes tell us about the cognitive/neural bases of human tool use. The goal of the present article is to fill this gap, by providing a theoretical framework for the study of human tool use: The Four Constraints Theory (4CT). This theory rests on two basic assumptions. First, everyday tool use activities can be formalized as multiple problem situations consisted of four distinct constraints (mechanics, space, time, and effort). Second, each of these constraints can be solved by the means of a specific process (technical reasoning, semantic reasoning, working memory, and simulation-based decision-making, respectively). Besides presenting neuropsychological evidence for 4CT, this article shall address epistemological, theoretical and methodological issues I will attempt to resolve. This article will discuss how 4CT diverges from current cognitive models about several widespread hypotheses (e.g., notion of routine, direct and automatic activation of tool knowledge, simulation-based tool knowledge).

Peripheral neural targets in obesity

PubMed Central

Page, Amanda J; Symonds, Erin; Peiris, Madusha; Blackshaw, L Ashley; Young, Richard L

2012-01-01

Interest in pharmacological treatments for obesity that act in the brain to reduce appetite has increased exponentially over recent years, but failures of clinical trials and withdrawals due to adverse effects have so far precluded any success. Treatments that do not act within the brain are, in contrast, a neglected area of research and development. This is despite the fact that a vast wealth of molecular mechanisms exists within the gut epithelium and vagal afferent system that could be manipulated to increase satiety. Here we discuss mechano- and chemosensory pathways from the gut involved in appetite suppression, and distinguish between gastric and intestinal vagal afferent pathways in terms of their basic physiology and activation by enteroendocrine factors. Gastric bypass surgery makes use of this system by exposing areas of the intestine to greater nutrient loads resulting in greater satiety hormone release and reduced food intake. A non-surgical approach to this system is preferable for many reasons. This review details where the opportunities may lie for such approaches by describing nutrient-sensing mechanisms throughout the gastrointestinal tract. PMID:22432806

Self-Organization: Complex Dynamical Systems in the Evolution of Speech

NASA Astrophysics Data System (ADS)

Oudeyer, Pierre-Yves

Human vocalization systems are characterized by complex structural properties. They are combinatorial, based on the systematic reuse of phonemes, and the set of repertoires in human languages is characterized by both strong statistical regularities—universals—and a great diversity. Besides, they are conventional codes culturally shared in each community of speakers. What are the origins of the forms of speech? What are the mechanisms that permitted their evolution in the course of phylogenesis and cultural evolution? How can a shared speech code be formed in a community of individuals? This chapter focuses on the way the concept of self-organization, and its interaction with natural selection, can throw light on these three questions. In particular, a computational model is presented which shows that a basic neural equipment for adaptive holistic vocal imitation, coupling directly motor and perceptual representations in the brain, can generate spontaneously shared combinatorial systems of vocalizations in a society of babbling individuals. Furthermore, we show how morphological and physiological innate constraints can interact with these self-organized mechanisms to account for both the formation of statistical regularities and diversity in vocalization systems.

Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

ERIC Educational Resources Information Center

Everson, Howard T.; And Others

This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

Motor Cortex Reorganization across the Lifespan

ERIC Educational Resources Information Center

Plowman, Emily K.; Kleim, Jeffrey A.

2010-01-01

The brain is a highly dynamic structure with the capacity for profound structural and functional change. Such neural plasticity has been well characterized within motor cortex and is believed to represent one of the neural mechanisms for acquiring and modifying motor behaviors. A number of behavioral and neural signals have been identified that…

The influence of personality on neural mechanisms of observational fear and reward learning

PubMed Central

Hooker, Christine I.; Verosky, Sara C.; Miyakawa, Asako; Knight, Robert T.; D’Esposito, Mark

2012-01-01

Fear and reward learning can occur through direct experience or observation. Both channels can enhance survival or create maladaptive behavior. We used fMRI to isolate neural mechanisms of observational fear and reward learning and investigate whether neural response varied according to individual differences in neuroticism and extraversion. Participants learned object-emotion associations by observing a woman respond with fearful (or neutral) and happy (or neutral) facial expressions to novel objects. The amygdala-hippocampal complex was active when learning the object-fear association, and the hippocampus was active when learning the object-happy association. After learning, objects were presented alone; amygdala activity was greater for the fear (vs. neutral) and happy (vs. neutral) associated object. Importantly, greater amygdala-hippocampal activity during fear (vs. neutral) learning predicted better recognition of learned objects on a subsequent memory test. Furthermore, personality modulated neural mechanisms of learning. Neuroticism positively correlated with neural activity in the amygdala and hippocampus during fear (vs. neutral) learning. Low extraversion/high introversion was related to faster behavioral predictions of the fearful and neutral expressions during fear learning. In addition, low extraversion/high introversion was related to greater amygdala activity during happy (vs. neutral) learning, happy (vs. neutral) object recognition, and faster reaction times for predicting happy and neutral expressions during reward learning. These findings suggest that neuroticism is associated with an increased sensitivity in the neural mechanism for fear learning which leads to enhanced encoding of fear associations, and that low extraversion/high introversion is related to enhanced conditionability for both fear and reward learning. PMID:18573512

Neural Mechanisms of Encoding Social and Non-Social Context Information in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Greimel, Ellen; Nehrkorn, Barbara; Fink, Gereon R.; Kukolja, Juraj; Kohls, Gregor; Muller, Kristin; Piefke, Martina; Kamp-Becker, Inge; Remschmidt, Helmut; Herpertz-Dahlmann, Beate; Konrad, Kerstin; Schulte-Ruther, Martin

2012-01-01

Individuals with autism spectrum disorder (ASD) often fail to attach context to their memories and are specifically impaired in processing social aspects of contextual information. The aim of the present study was to investigate the modulatory influence of social vs. non-social context on neural mechanisms during encoding in ASD. Using…

Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

PubMed

Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

2013-10-02

Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

Prospects for neural stem cell-based therapies for neurological diseases.

PubMed

Imitola, Jaime

2007-10-01

Neural stem and progenitor cells have great potential for the treatment of neurological disorders. However, many obstacles remain to translate this field to the patient's bedside, including rationales for using neural stem cells in individual neurological disorders; the challenges of neural stem cell biology; and the caveats of current strategies of isolation and culturing neural precursors. Addressing these challenges is critical for the translation of neural stem cell biology to the clinic. Recent work using neural stem cells has yielded novel biologic concepts such as the importance of the reciprocal interaction between neural stem cells and the neurodegenerative environment. The prospect of using transplants of neural stem cells and progenitors to treat neurological diseases requires a better understanding of the molecular mechanisms of both neural stem cell behavior in experimental models and the intrinsic repair capacity of the injured brain.

RIP140/PGC-1α axis involved in vitamin A-induced neural differentiation by increasing mitochondrial function.

PubMed

Mu, Qing; Yu, Weidong; Zheng, Shuying; Shi, Hongxia; Li, Mei; Sun, Jie; Wang, Di; Hou, Xiaoli; Liu, Ling; Wang, Xinjuan; Zhao, Zhuran; Liang, Rong; Zhang, Xue; Dong, Wei; Zeng, Chaomei; Guo, Jingzhu

2018-03-07

Vitamin A deficiency and mitochondrial dysfunction are both associated with neural differentiation-related disorders, such as Alzheimer's disease (AD) and Down syndrome (DS). The mechanism of vitamin A-induced neural differentiation and the notion that vitamin A can regulate the morphology and function of mitochondria in its induction of neural differentiation through the RIP140/PGC-1α axis are unclear. The aim of this study was to investigate the roles and underlying mechanisms of RIP140/PGC-1α axis in vitamin A-induced neural differentiation. Human neuroblastoma cells (SH-SY5Y) were used as a model of neural stem cells, which were incubated with DMSO, 9-cis-retinoic acid (9-cis-RA), 13-cis-retinoic acid (13-cis-RA) and all-trans-retinoic acid (at-RA). Neural differentiation of SH-SY5Y was evaluated by Sandquist calculation, combined with immunofluorescence and real-time polymerase chain reaction (PCR) of neural markers. Mitochondrial function was estimated by ultrastructure assay using transmission electron microscopy (TEM) combined with the expression of PGC-1α and NEMGs using real-time PCR. The participation of the RA signaling pathway was demonstrated by adding RA receptor antagonists. Vitamin A derivatives are able to regulate mitochondrial morphology and function, and furthermore to induce neural differentiation through the RA signaling pathway. The RIP140/PGC-1α axis is involved in the regulation of mitochondrial function in vitamin A derivative-induced neural differentiation.

Neural hijacking: action of high-frequency electrical stimulation on cortical circuits.

PubMed

Cheney, P D; Griffin, D M; Van Acker, G M

2013-10-01

Electrical stimulation of the brain was one of the first experimental methods applied to understanding brain organization and function and it continues as a highly useful method both in research and clinical applications. Intracortical microstimulation (ICMS) involves applying electrical stimuli through a microelectrode suitable for recording the action potentials of single neurons. ICMS can be categorized into single-pulse stimulation; high-frequency, short-duration stimulation; and high-frequency, long-duration stimulation. For clinical and experimental reasons, considerable interest focuses on the mechanism of neural activation by electrical stimuli. In this article, we discuss recent results suggesting that action potentials evoked in cortical neurons by high-frequency electrical stimulation do not sum with the natural, behaviorally related background activity; rather, high-frequency stimulation eliminates and replaces natural activity. We refer to this as neural hijacking. We propose that a major component of the mechanism underlying neural hijacking is excitation of axons by ICMS and elimination of natural spikes by antidromic collision with stimulus-driven spikes evoked at high frequency. Evidence also supports neural hijacking as an important mechanism underlying the action of deep brain stimulation in the subthalamic nucleus and its therapeutic effect in treating Parkinson's disease.

Theory of mind in schizophrenia: exploring neural mechanisms of belief attribution.

PubMed

Lee, Junghee; Quintana, Javier; Nori, Poorang; Green, Michael F

2011-01-01

Although previous behavioral studies have shown that schizophrenia patients have impaired theory of mind (ToM), the neural mechanisms associated with this impairment are poorly understood. This study aimed to identify the neural mechanisms of ToM in schizophrenia, using functional magnetic resonance imaging (fMRI) with a belief attribution task. In the scanner, 12 schizophrenia patients and 13 healthy control subjects performed the belief attribution task with three conditions: a false belief condition, a false photograph condition, and a simple reading condition. For the false belief versus simple reading conditions, schizophrenia patients showed reduced neural activation in areas including the temporoparietal junction (TPJ) and medial prefrontal cortex (MPFC) compared with controls. Further, during the false belief versus false photograph conditions, we observed increased activations in the TPJ and the MPFC in healthy controls, but not in schizophrenia patients. For the false photograph versus simple reading condition, both groups showed comparable neural activations. Schizophrenia patients showed reduced task-related activation in the TPJ and the MPFC during the false belief condition compared with controls, but not for the false photograph condition. This pattern suggests that reduced activation in these regions is associated with, and specific to, impaired ToM in schizophrenia.

Employing TDMA Protocol in Neural Nanonetworks in Case of Neuron Specific Faults.

PubMed

Tezcan, Hakan; Oktug, Sema F; Kök, Fatma Neşe

2015-09-01

Many neurodegenerative diseases arise from the malfunctioning neurons in the pathway where the signal is carried. In this paper, we propose neuron specific TDMA/multiplexing and demultiplexing mechanisms to convey the spikes of a receptor neuron over a neighboring path in case of an irreversible path fault existing in its original path. The multiplexing mechanism depends on neural delay box (NDB) which is composed of a relay unit and a buffering unit. The relay unit can be realized as a nanoelectronic device. The buffering unit can be implemented either via neural delay lines as employed in optical switching systems or via nanoelectronic delay lines, i.e., delay flip flops. Demultiplexing is realized by a demultiplexer unit according to the time slot assignment information. Besides, we propose the use of neural interfaces in the NDBs and the demultiplexer unit for detecting and stimulating the generation of spikes. The objective of the proposed mechanisms is to substitute a malfunctioning path, increase the number of spikes delivered and correctly deliver the spikes to the intended part of the somatosensory cortex. The results demonstrate that significant performance improvement on the successively delivered number of spikes is achievable when delay lines are employed as neural buffers in NDBs.

Neural mechanisms of selective attention in the somatosensory system.

PubMed

Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

2016-09-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

Signaling mechanisms regulating adult neural stem cells and neurogenesis

PubMed Central

Faigle, Roland; Song, Hongjun

2012-01-01

Background Adult neurogenesis occurs throughout life in discrete regions of the mammalian brain and is tightly regulated via both extrinsic environmental influences and intrinsic genetic factors. In recent years, several crucial signaling pathways have been identified in regulating self-renewal, proliferation, and differentiation of neural stem cells, as well as migration and functional integration of developing neurons in the adult brain. Scope of review Here we review our current understanding of signaling mechanisms, including Wnt, notch, sonic hedgehog, growth and neurotrophic factors, bone morphogenetic proteins, neurotransmitters, transcription factors, and epigenetic modulators, and crosstalk between these signaling pathways in the regulation of adult neurogenesis. We also highlight emerging principles in the vastly growing field of adult neural stem cell biology and neural plasticity. Major conclusions Recent methodological advances have enabled the field to identify signaling mechanisms that fine-tune and coordinate neurogenesis in the adult brain, leading to a better characterization of both cell-intrinsic and environmental cues defining the neurogenic niche. Significant questions related to niche cell identity and underlying regulatory mechanisms remain to be fully addressed and will be the focus of future studies. General significance A full understanding of the role and function of individual signaling pathways in regulating neural stem cells and generation and integration of newborn neurons in the adult brain may lead to targeted new therapies for neurological diseases in humans. PMID:22982587

Neural mechanisms of selective attention in the somatosensory system

PubMed Central

Hysaj, Kristjana; Niebur, Ernst

2016-01-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. PMID:27334956

Selective attention on representations in working memory: cognitive and neural mechanisms.

PubMed

Ku, Yixuan

2018-01-01

Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

Selective attention on representations in working memory: cognitive and neural mechanisms

PubMed Central

2018-01-01

Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory. PMID:29629245

Visual Aversive Learning Compromises Sensory Discrimination.

PubMed

Shalev, Lee; Paz, Rony; Avidan, Galia

2018-03-14

Aversive learning is thought to modulate perceptual thresholds, which can lead to overgeneralization. However, it remains undetermined whether this modulation is domain specific or a general effect. Moreover, despite the unique role of the visual modality in human perception, it is unclear whether this aspect of aversive learning exists in this modality. The current study was designed to examine the effect of visual aversive outcomes on the perception of basic visual and auditory features. We tested the ability of healthy participants, both males and females, to discriminate between neutral stimuli, before and after visual learning. In each experiment, neutral stimuli were associated with aversive images in an experimental group and with neutral images in a control group. Participants demonstrated a deterioration in discrimination (higher discrimination thresholds) only after aversive learning. This deterioration was measured for both auditory (tone frequency) and visual (orientation and contrast) features. The effect was replicated in five different experiments and lasted for at least 24 h. fMRI neural responses and pupil size were also measured during learning. We showed an increase in neural activations in the anterior cingulate cortex, insula, and amygdala during aversive compared with neutral learning. Interestingly, the early visual cortex showed increased brain activity during aversive compared with neutral context trials, with identical visual information. Our findings imply the existence of a central multimodal mechanism, which modulates early perceptual properties, following exposure to negative situations. Such a mechanism could contribute to abnormal responses that underlie anxiety states, even in new and safe environments. SIGNIFICANCE STATEMENT Using a visual aversive-learning paradigm, we found deteriorated discrimination abilities for visual and auditory stimuli that were associated with visual aversive stimuli. We showed increased neural activations in the anterior cingulate cortex, insula, and amygdala during aversive learning, compared with neutral learning. Importantly, similar findings were also evident in the early visual cortex during trials with aversive/neutral context, but with identical visual information. The demonstration of this phenomenon in the visual modality is important, as it provides support to the notion that aversive learning can influence perception via a central mechanism, independent of input modality. Given the dominance of the visual system in human perception, our findings hold relevance to daily life, as well as imply a potential etiology for anxiety disorders. Copyright © 2018 the authors 0270-6474/18/382766-14$15.00/0.

Implantable liquid metal-based flexible neural microelectrode array and its application in recovering animal locomotion functions

NASA Astrophysics Data System (ADS)

Guo, Rui; Liu, Jing

2017-10-01

With significant advantages in rapidly restoring the nerve function, electrical stimulation of nervous tissue is a crucial treatment of peripheral nerve injuries leading to common movement disorder. However, the currently available stimulating electrodes generally based on rigid conductive materials would cause a potential mechanical mismatch with soft neural tissues which thus reduces long-term effects of electrical stimulation. Here, we proposed and fabricated a flexible neural microelectrode array system based on the liquid metal GaIn alloy (75.5% Ga and 24.5% In by weight) and via printing approach. Such an alloy with a unique low melting point (10.35 °C) owns excellent electrical conductivity and high compliance, which are beneficial to serve as implantable flexible neural electrodes. The flexible neural microelectrode array embeds four liquid metal electrodes and stretchable interconnects in a PDMS membrane (500 µm in thickness) that possess a lower elastic modulus (1.055 MPa), which is similar to neural tissues with elastic moduli in the 0.1-1.5 MPa range. The electrical experiments indicate that the liquid metal interconnects could sustain over 7000 mechanical stretch cycles with resistance approximately staying at 4 Ω. Over the conceptual experiments on animal sciatic nerve electrical stimulation, the dead bullfrog implanted with flexible neural microelectrode array could even rhythmically contract and move its lower limbs under the electrical stimulations from the implant. This demonstrates a highly efficient way for quickly recovering biological nerve functions. Further, the good biocompatibility of the liquid metal material was justified via a series of biological experiments. This liquid metal modality for neural stimulation is expected to play important roles as biologic electrodes to overcome the fundamental mismatch in mechanics between biological tissues and electronic devices in the coming time.

Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

PubMed

Lee, Sang Eun; Han, Yeji; Park, HyunWook

2016-01-01

The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Fundamentals of neurogastroenterology: basic science.

PubMed

Grundy, David; Al-Chaer, Elie D; Aziz, Qasim; Collins, Stephen M; Ke, Meiyun; Taché, Yvette; Wood, Jackie D

2006-04-01

The focus of neurogastroenterology in Rome II was the enteric nervous system (ENS). To avoid duplication with Rome II, only advances in ENS neurobiology after Rome II are reviewed together with stronger emphasis on interactions of the brain, spinal cord, and the gut in terms of relevance for abdominal pain and disordered gastrointestinal function. A committee with expertise in selective aspects of neurogastroenterology was invited to evaluate the literature and provide a consensus overview of the Fundamentals of Neurogastroenterology textbook as they relate to functional gastrointestinal disorders (FGIDs). This review is an abbreviated version of a fuller account that appears in the forthcoming book, Rome III. This report reviews current basic science understanding of visceral sensation and its modulation by inflammation and stress and advances in the neurophysiology of the ENS. Many of the concepts are derived from animal studies in which the physiologic mechanisms underlying visceral sensitivity and neural control of motility, secretion, and blood flow are examined. Impact of inflammation and stress in experimental models relative to FGIDs is reviewed as is human brain imaging, which provides a means for translating basic science to understanding FGID symptoms. Investigative evidence and emerging concepts implicate dysfunction in the nervous system as a significant factor underlying patient symptoms in FGIDs. Continued focus on neurogastroenterologic factors that underlie the development of symptoms will lead to mechanistic understanding that is expected to directly benefit the large contingent of patients and care-givers who deal with FGIDs.

Inter-progenitor pool wiring: An evolutionarily conserved strategy that expands neural circuit diversity.

PubMed

Suzuki, Takumi; Sato, Makoto

2017-11-15

Diversification of neuronal types is key to establishing functional variations in neural circuits. The first critical step to generate neuronal diversity is to organize the compartmental domains of developing brains into spatially distinct neural progenitor pools. Neural progenitors in each pool then generate a unique set of diverse neurons through specific spatiotemporal specification processes. In this review article, we focus on an additional mechanism, 'inter-progenitor pool wiring', that further expands the diversity of neural circuits. After diverse types of neurons are generated in one progenitor pool, a fraction of these neurons start migrating toward a remote brain region containing neurons that originate from another progenitor pool. Finally, neurons of different origins are intermingled and eventually form complex but precise neural circuits. The developing cerebral cortex of mammalian brains is one of the best examples of inter-progenitor pool wiring. However, Drosophila visual system development has revealed similar mechanisms in invertebrate brains, suggesting that inter-progenitor pool wiring is an evolutionarily conserved strategy that expands neural circuit diversity. Here, we will discuss how inter-progenitor pool wiring is accomplished in mammalian and fly brain systems. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural mechanisms and personality correlates of the sunk cost effect

PubMed Central

Fujino, Junya; Fujimoto, Shinsuke; Kodaka, Fumitoshi; Camerer, Colin F.; Kawada, Ryosaku; Tsurumi, Kosuke; Tei, Shisei; Isobe, Masanori; Miyata, Jun; Sugihara, Genichi; Yamada, Makiko; Fukuyama, Hidenao; Murai, Toshiya; Takahashi, Hidehiko

2016-01-01

The sunk cost effect, an interesting and well-known maladaptive behavior, is pervasive in real life, and thus has been studied in various disciplines, including economics, psychology, organizational behavior, politics, and biology. However, the neural mechanisms underlying the sunk cost effect have not been clearly established, nor have their association with differences in individual susceptibility to the effect. Using functional magnetic resonance imaging, we investigated neural responses induced by sunk costs along with measures of core human personality. We found that individuals who tend to adhere to social rules and regulations (who are high in measured agreeableness and conscientiousness) are more susceptible to the sunk cost effect. Furthermore, this behavioral observation was strongly mediated by insula activity during sunk cost decision-making. Tight coupling between the insula and lateral prefrontal cortex was also observed during decision-making under sunk costs. Our findings reveal how individual differences can affect decision-making under sunk costs, thereby contributing to a better understanding of the psychological and neural mechanisms of the sunk cost effect. PMID:27611212

Underlying neural mechanisms of mirror therapy: Implications for motor rehabilitation in stroke.

PubMed

Arya, Kamal Narayan

2016-01-01

Mirror therapy (MT) is a valuable method for enhancing motor recovery in poststroke hemiparesis. The technique utilizes the mirror-illusion created by the movement of sound limb that is perceived as the paretic limb. MT is a simple and economical technique than can stimulate the brain noninvasively. The intervention unquestionably has neural foundation. But the underlying neural mechanisms inducing motor recovery are still unclear. In this review, the neural-modulation due to MT has been explored. Multiple areas of the brain such as the occipital lobe, dorsal frontal area and corpus callosum are involved during the simple MT regime. Bilateral premotor cortex, primary motor cortex, primary somatosensory cortex, and cerebellum also get reorganized to enhance the function of the damaged brain. The motor areas of the lesioned hemisphere receive visuo-motor processing information through the parieto-occipital lobe. The damaged motor cortex responds variably to the MT and may augment true motor recovery. Mirror neurons may also play a possible role in the cortico-stimulatory mechanisms occurring due to the MT.

Bioelectrochemical control of neural cell development on conducting polymers.

PubMed

Collazos-Castro, Jorge E; Polo, José L; Hernández-Labrado, Gabriel R; Padial-Cañete, Vanesa; García-Rama, Concepción

2010-12-01

Electrically conducting polymers hold promise for developing advanced neuroprostheses, bionic systems and neural repair devices. Among them, poly(3, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) exhibits superior physicochemical properties but biocompatibility issues have limited its use. We describe combinations of electrochemical and molecule self-assembling methods to consistently control neural cell development on PEDOT:PSS while maintaining very low interfacial impedance. Electro-adsorbed polylysine enabled long-term neuronal survival and growth on the nanostructured polymer. Neurite extension was strongly inhibited by an additional layer of PSS or heparin, which in turn could be either removed electrically or further coated with spermine to activate cell growth. Binding basic fibroblast growth factor (bFGF) to the heparin layer inhibited neurons but promoted proliferation and migration of precursor cells. This methodology may orchestrate neural cell behavior on electroactive polymers, thus improving cell/electrode communication in prosthetic devices and providing a platform for tissue repair strategies. Copyright © 2010 Elsevier Ltd. All rights reserved.

Spin switches for compact implementation of neuron and synapse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quang Diep, Vinh, E-mail: vdiep@purdue.edu; Sutton, Brian; Datta, Supriyo

2014-06-02

Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to explore the possibility of a hardware neural network implementation using a spin switch (SS) as its basic building block. SS is a recently proposed device based on established technology with a transistor-like gain and input-output isolation. This allows neural networks to be constructed with purely passive interconnections without intervening clocks or amplifiers. The weights for the neural network are conveniently adjusted through analog voltagesmore » that can be stored in a non-volatile manner in an underlying CMOS layer using a floating gate low dropout voltage regulator. The operation of a multi-layer SS neural network designed for character recognition is demonstrated using a standard simulation model based on coupled Landau-Lifshitz-Gilbert equations, one for each magnet in the network.« less

Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

PubMed

Johnson, J L

1994-09-10

The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

Interactions of Top-Down and Bottom-Up Mechanisms in Human Visual Cortex

PubMed Central

McMains, Stephanie; Kastner, Sabine

2011-01-01

Multiple stimuli present in the visual field at the same time compete for neural representation by mutually suppressing their evoked activity throughout visual cortex, providing a neural correlate for the limited processing capacity of the visual system. Competitive interactions among stimuli can be counteracted by top-down, goal-directed mechanisms such as attention, and by bottom-up, stimulus-driven mechanisms. Because these two processes cooperate in everyday life to bias processing toward behaviorally relevant or particularly salient stimuli, it has proven difficult to study interactions between top-down and bottom-up mechanisms. Here, we used an experimental paradigm in which we first isolated the effects of a bottom-up influence on neural competition by parametrically varying the degree of perceptual grouping in displays that were not attended. Second, we probed the effects of directed attention on the competitive interactions induced with the parametric design. We found that the amount of attentional modulation varied linearly with the degree of competition left unresolved by bottom-up processes, such that attentional modulation was greatest when neural competition was little influenced by bottom-up mechanisms and smallest when competition was strongly influenced by bottom-up mechanisms. These findings suggest that the strength of attentional modulation in the visual system is constrained by the degree to which competitive interactions have been resolved by bottom-up processes related to the segmentation of scenes into candidate objects. PMID:21228167

Language learning impairments: integrating basic science, technology, and remediation.

PubMed

Tallal, P; Merzenich, M M; Miller, S; Jenkins, W

1998-11-01

One of the fundamental goals of the modern field of neuroscience is to understand how neuronal activity gives rise to higher cortical function. However, to bridge the gap between neurobiology and behavior, we must understand higher cortical functions at the behavioral level at least as well as we have come to understand neurobiological processes at the cellular and molecular levels. This is certainly the case in the study of speech processing, where critical studies of behavioral dysfunction have provided key insights into the basic neurobiological mechanisms relevant to speech perception and production. Much of this progress derives from a detailed analysis of the sensory, perceptual, cognitive, and motor abilities of children who fail to acquire speech, language, and reading skills normally within the context of otherwise normal development. Current research now shows that a dysfunction in normal phonological processing, which is critical to the development of oral and written language, may derive, at least in part, from difficulties in perceiving and producing basic sensory-motor information in rapid succession--within tens of ms (see Tallal et al. 1993a for a review). There is now substantial evidence supporting the hypothesis that basic temporal integration processes play a fundamental role in establishing neural representations for the units of speech (phonemes), which must be segmented from the (continuous) speech stream and combined to form words, in order for the normal development of oral and written language to proceed. Results from magnetic resonance imaging (MRI) and positron emission tomography (PET) studies, as well as studies of behavioral performance in normal and language impaired children and adults, will be reviewed to support the view that the integration of rapidly changing successive acoustic events plays a primary role in phonological development and disorders. Finally, remediation studies based on this research, coupled with neuroplasticity research, will be presented.

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

PubMed

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

PubMed

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression

ERIC Educational Resources Information Center

Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi

2011-01-01

Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half…