Curtis, Steven A.
The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.
Curtis, Steven A. (Inventor)
An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.
Peciña, Marta; Bohnert, Amy S. B.; Sikora, Magdalena; Avery, Erich T.; Langenecker, Scott A.; Mickey, Brian J.; Zubieta, Jon-Kar
Importance High placebo responses have been observed across a wide range of pathologies, severely impacting drug development. Objective Here we examined neurochemical mechanisms underlying the formation of placebo effects in patients with Major Depressive Disorder (MDD). Participants Thirty-five medication-free MDD patients. Design and Intervention We performed a single-blinded two-week cross-over randomized controlled trial of two identical oral placebos (described as having either “active” or “inactive” fast-acting antidepressant-like effects) followed by a 10-week open-label treatment with a selective serotonin reuptake inhibitor (SSRI) or in some cases, another agent as clinically indicated. The volunteers were studied with PET and the μ-opioid receptor (MOR)-selective radiotracer [11C]carfentanil after each 1-week “inactive” and “active” oral placebo treatment. In addition, 1 mL of isotonic saline was administered intravenously (i.v.) within sight of the volunteer during PET scanning every 4 min over 20 min only after the 1-week active placebo treatment, with instructions that the compound may be associated with the activation of brain systems involved in mood improvement. This challenge stimulus was utilized to test the individual capacity to acutely activate endogenous opioid neurotransmision under expectations of antidepressant effect. Setting A University Health System. Main Outcomes and Measures Changes in depressive symptoms in response to “active” placebo and antidepressant. Baseline and activation measures of MOR binding. Results Higher baseline MOR binding in the nucleus accumbens (NAc) was associated with better response to antidepressant treatment (r=0.48; p=0.02). Reductions in depressive symptoms after 1-week of “active” placebo treatment, compared to the “inactive”, were associated with increased placebo-induced μ-opioid neurotransmission in a network of regions implicated in emotion, stress regulation, and the
Edelen, Auralee [Fermilab; Biedron, Sandra [Colorado State U., Fort Collins; Bowring, Daniel [Fermilab; Chase, Brian [Fermilab; Edelen, Jonathan [Fermilab; Milton, Stephen [Colorado State U., Fort Collins; Steimel, Jim [Fermilab
As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequency of the RFQ.
Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole
It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude.......It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able...
Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...
When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system. SIGNIFICANCE STATEMENT Our sensory organs transduce errors in behavior. To improve performance, we must generate better motor commands. How does the nervous system transform an error in sensory coordinates into better motor commands in muscle coordinates? Here we show that when an error occurs during a movement, the reflexes transform the sensory representation of error into motor
Full Text Available Individuals with borderline personality disorder (BPD are characterized by emotional instability, impaired emotion regulation and unresolved attachment patterns associated with abusive childhood experiences. We investigated the neural response during the activation of the attachment system in BPD patients compared to healthy controls using functional magnetic resonance imaging. Eleven female patients with BPD without posttraumatic stress disorder and seventeen healthy female controls matched for age and education were telling stories in the scanner in response to the Adult Attachment Projective Picture System, an eight-picture set assessment of adult attachment. The picture set includes theoretically-derived attachment scenes, such as separation, death, threat and potential abuse. The picture presentation order is designed to gradually increase the activation of the attachment system. Each picture stimulus was presented for two minutes. Analyses examine group differences in attachment classifications and neural activation patterns over the course of the task. Unresolved attachment was associated with increasing amygdala activation over the course of the attachment task in patients as well as controls. Unresolved controls, but not patients, showed activation in the right dorsolateral prefrontal cortex and the rostral cingulate zone. We interpret this as a neural signature of BPD patients’ inability to exert top-down control under conditions of attachment distress. These findings point to possible neural mechanisms for underlying affective dysregulation in BPD in the context of attachment trauma and fear.
Gentili, Claudio; Cristea, Ioana Alina; Angstadt, Mike; Klumpp, Heide; Tozzi, Leonardo; Phan, K Luan; Pietrini, Pietro
Patients with social anxiety disorder (SAD) experience anxiety and avoidance in face-to-face interactions. We performed a meta-analysis of functional magnetic resonance imaging (fMRI) studies in SAD to provide a comprehensive understanding of the neural underpinnings of face perception in this disorder. To this purpose, we adopted an innovative approach, asking authors for unpublished data. This is a common procedure for behavioral meta-analyses, which, however has never been used in neuroimaging studies. We searched Pubmed with the key words "Social Anxiety AND faces" and "Social Phobia AND faces." Then, we selected those fMRI studies for which we were able to obtain data for the comparison between SAD and healthy controls (HC) in a face perception task, either from the published papers or from the authors themselves. In this way, we obtained 23 studies (totaling 449 SAD and 424 HC individuals). We identified significant clusters in which faces evoked a higher response in SAD in bilateral amygdala, globus pallidus, superior temporal sulcus, visual cortex, and prefrontal cortex. We also found a higher activity for HC in the lingual gyrus and in the posterior cingulate. Our findings show that altered neural response to face in SAD is not limited to emotional structures but involves a complex network. These results may have implications for the understanding of SAD pathophysiology, as they suggest that a dysfunctional face perception process may bias patient person-to-person interactions. © 2015 by the Society for Experimental Biology and Medicine.
Full Text Available It is increasingly appreciated that cochlear pathology is accompanied by adaptive responses in the central auditory system. The cause of cochlear pathology varies widely, and it seems that few commonalities can be drawn. In fact, despite intricate internal neuroplasticity and diverse external symptoms, several classical injury models provide a feasible path to locate responses to different peripheral cochlear lesions. In these cases, hair cell damage may lead to considerable hyperactivity in the central auditory pathways, mediated by a reduction in inhibition, which may underlie some clinical symptoms associated with hearing loss, such as tinnitus. Homeostatic plasticity, the most discussed and acknowledged mechanism in recent years, is most likely responsible for excited central activity following cochlear damage.
Full Text Available The voltage clamp method, pioneered by Hodgkin, Huxley and Katz, laid the foundations to neurophysiological research. Its core rationale is the use of closed-loop control as a tool for system characterization. A recently introduced method, the response clamp, extends the voltage clamp rationale to the functional, phenomenological level. The method consists of on-line estimation of a response variable of interest (e.g. the probability of response or its latency and a simple feedback control mechanism designed to tightly converge this variable towards a desired trajectory. In the present contribution I offer a perspective on this novel method and its applications in the broader context of system identification and characterization. First, I demonstrate how internal state variables are exposed using the method, and how the use of several controllers may allow for a detailed, multi-variable characterization of the system. Second, I discuss three different categories of applications of the method: (i exploration of intrinsically generated dynamics, (ii exploration of extrinsically generated dynamics and (iii generation of input-output trajectories. The relation of these categories to similar uses in the voltage clamp and other techniques is also discussed. Finally, I discuss the method’s limitations, as well as its possible synthesis with existing complementary approaches.
Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L
Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Cafarelli-Dees, D.; Dillier, N.; Lai, W.K.; Wallenberg, E. von; Dijk, B. van; Akdas, F.; Aksit, M.; Batman, C.; Beynon, A.J.; Burdo, S.; Chanal, J.M.; Collet, L.; Conway, M.; Coudert, C.; Craddock, L.; Cullington, H.; Deggouj, N.; Fraysse, B.; Grabel, S.; Kiefer, J.; Kiss, J.G.; Lenarz, T.; Mair, A.; Maune, S.; Muller-Deile, J.; Piron, J.P.; Razza, S.; Tasche, C.; Thai-Van, H.; Toth, F.; Truy, E.; Uziel, A.; Smoorenburg, G.F.
One hundred and forty-seven adult recipients of the Nucleus 24 cochlear implant system, from 13 different European countries, were tested using neural response telemetry to measure the electrically evoked compound action potential (ECAP), according to a standardised postoperative measurement
Shafritz, Keith M; Bregman, Joel D; Ikuta, Toshikazu; Szeszko, Philip R
Autism is marked by impairments in social reciprocity and communication, along with restricted, repetitive and stereotyped behaviors. Prior studies have separately investigated social processing and executive function in autism, but little is known about the brain mechanisms of cognitive control for both emotional and nonemotional stimuli. We used functional magnetic resonance imaging to identify differences in neurocircuitry between individuals with high functioning autism (HFA) and neurotypical controls during two versions of a go/no-go task: emotional (fear and happy faces) and nonemotional (English letters). During the letter task, HFA participants showed hypoactivation in the ventral prefrontal cortex. During the emotion task, happy faces elicited activation in the ventral striatum, nucleus accumbens and anterior amygdala in neurotypical, but not HFA, participants. Response inhibition for fear faces compared with happy faces recruited occipitotemporal regions in HFA, but not neurotypical, participants. In a direct contrast of emotional no-go and letter no-go blocks, HFA participants showed hyperactivation in extrastriate cortex and fusiform gyrus. Accuracy for emotional no-go trials was negatively correlated with activation in fusiform gyrus in the HFA group. These results indicate that autism is associated with abnormal processing in socioemotional brain networks, and support the theory that autism is marked by a social motivational deficit. Copyright © 2015 Elsevier Inc. All rights reserved.
Omidvar, Omid; Elliott, David L
... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...
When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discuss three different neural systems that are linked by this theme. The pyloric network of the lobster, the song control system of the zebra finch, and the odor encoding system of the locust, while different in design, all contain degeneracies between their internal parameters and the outputs they encode. Indeed, although the dynamics of song generation and odor identification are quite different, computationally, odor recognition can be thought of as running the song generation circuitry backwards. In both of these systems, degeneracy plays a vital role in mapping a sparse neural representation devoid of correlations onto external stimuli (odors or song structure) that are strongly correlated. I argue that degeneracy between input and output states is an inherent feature of many neural systems, which can be exploited as a fault-tolerant method of reliably learning, generating, and discriminating closely related patterns.
Alkon, Daniel L.
Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)
Kurikawa, Tomoki; Kaneko, Kunihiko
Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study.
Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in
Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.
The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.
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
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.
Peciña, Marta; Bohnert, Amy S B; Sikora, Magdalena; Avery, Erich T; Langenecker, Scott A; Mickey, Brian J; Zubieta, Jon-Kar
High placebo responses have been observed across a wide range of pathologies, severely impacting drug development. To examine neurochemical mechanisms underlying the formation of placebo effects in patients with major depressive disorder (MDD). In this study involving 2 placebo lead-in phases followed by an open antidepressant administration, we performed a single-blinded 2-week crossover randomized clinical trial of 2 identical oral placebos (described as having either active or inactive fast-acting antidepressant-like effects) followed by a 10-week open-label treatment with a selective serotonin reuptake inhibitor or, in some cases, another agent as clinically indicated. The volunteers (35 medication-free patients with MDD at a university health system) were studied with positron emission tomography and the µ-opioid receptor-selective radiotracer [11C]carfentanil after each 1-week inactive and active oral placebo treatment. In addition, 1 mL of isotonic saline was administered intravenously within sight of the volunteer during positron emission tomographic scanning every 4 minutes over 20 minutes only after the 1-week active placebo treatment, with instructions that the compound may be associated with the activation of brain systems involved in mood improvement. This challenge stimulus was used to test the individual capacity to acutely activate endogenous opioid neurotransmision under expectations of antidepressant effect. Changes in depressive symptoms in response to active placebo and antidepressant. Baseline and activation measures of µ-opioid receptor binding. Higher baseline µ-opioid receptor binding in the nucleus accumbens was associated with better response to antidepressant treatment (r = 0.48; P = .02). Reductions in depressive symptoms after 1 week of active placebo treatment, compared with the inactive, were associated with increased placebo-induced µ-opioid neurotransmission in a network of regions implicated in emotion, stress
von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H
The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability
Vilim, R.B.; Gross, K.C.; Wegerich, S.W.
A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.
Newton Sabino Canteras
Full Text Available O nosso entendimento das bases neurofisiológicas da reação emocional do medo baseia-se em grande parte nos estudos que envolvem respostas condicionadas a estímulos fisicamente aversivos, como, por exemplo, o choque elétrico nas patas. Enquanto este paradigma parece ser útil para avaliarmos os sistemas neurais envolvidos na resposta do, assim chamado, medo condicionado (que tipicamente tem se limitado à observação da resposta de congelamento, este paradigma parece ter sérias limitações para investigarmos as bases neurais das respostas de medo em circunstancias naturais. Trabalhos recentes utilizando técnicas de lesões neurais bem como de mapeamento funcional em animais expostos a predadores naturais, ou somente ao odor destes predadores, revelam uma série de estruturas neurais como responsáveis pelas respostas de medo inato, bastante distintas daquelas previamente implicadas nas respostas de condicionamento aversivo. Como revisto no presente trabalho, entre estas estruturas temos distritos diferenciados da zona medial do hipotálamo; setores específicos da amídala e do sistema septo-hipocampal, envolvidos, respectivamente no processamento de pistas relacionadas à presença do predador e na análise contextual do ambiente; e setores da matéria cinzenta periaquedutal, já classicamente envolvidos na expressão de respostas de defesa. Estas informações podem ser potencialmente importantes para a análise e terapêutica de psicopatologias relacionadas aos distúrbios da reação emocional de medo.Unconditioned emotional responses elicited by exposure to a predator have served as the prototypical exemplar for analyses of the behavioral biology of fear-related emotionality. However, the primary research model for the study of fear has involved shock-based cue and context conditioning. While these shock-based models have provided a good understanding of neural systems regulating specific conditioned fear-related behaviors
Meyer, Martin; Elmer, Stefan; Baumann, Simon; Jancke, Lutz
auditory imagery of music and speech prompts involvement of distinct neural circuits residing in the perisylvian cortex.
Bonda, E; Petrides, M; Evans, A
1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.
Full Text Available In this paper we study neural responses to inequitable distributions of rewards despite equal performance. We specifically focus on differences between advantageous (AI and disadvantageous inequity (DI. AI and DI were realized in a hyperscanning fMRI experiment with pairs of subjects simultaneously performing a task in adjacent scanners and observing both subjects' rewards. Results showed i hypoactivation of the ventral striatum under DI but not under AI; ii inequity induced activation of medial and dorsolateral prefrontal regions, that were stronger under DI than AI; iii correlations between subjective evaluations of DI and amygdala activity, and between AI evaluation and right ventrolateral prefrontal activity. Our study provides neurophysiological evidence for different cognitive processes that occur when exposed to DI and AI, respectively. Our data is compatible with the assumption that any form of inequity represents a norm violation, but that important differences between AI and DI emerge from an asymmetric involvement of status concerns.
Full Text Available Inhibition of irrelevant information (conflict monitoring and/or of prepotent actions is an essential component of adaptive self-organized behavior. Neural dynamics underlying these functions has been studied in humans using event-related brain potentials (ERPs elicited in Go/NoGo tasks that require a speeded motor response to the Go stimuli and withholding a prepotent response when a NoGo stimulus is presented. However, averaged ERP waveforms provide only limited information about the neuronal mechanisms underlying stimulus processing, motor preparation, and response production or inhibition. In this study, we examine the cortical representation of conflict monitoring and response inhibition using time-frequency analysis of electroencephalographic (EEG recordings during continuous performance Go/NoGo task in 50 young adult females. We hypothesized that response inhibition would be associated with a transient boost in both temporal and spatial synchronization of prefrontal cortical activity, consistent with the role of the anterior cingulate and lateral prefrontal cortices in cognitive control. Overall, phase synchronization across trials measured by Phase Locking Index and phase synchronization between electrode sites measured by Phase Coherence were the highest in the Go and NoGo conditions, intermediate in the Warning condition, and the lowest under Neutral condition. The NoGo condition was characterized by significantly higher fronto-central synchronization in the 300-600 ms window, whereas in the Go condition, delta- and theta-band synchronization was higher in centro-parietal regions in the first 300 ms after the stimulus onset. The present findings suggest that response production and inhibition is supported by dynamic functional networks characterized by distinct patterns of temporal and spatial synchronization of brain oscillations.
Weng, Helen Y; Fox, Andrew S; Shackman, Alexander J; Stodola, Diane E; Caldwell, Jessica Z K; Olson, Matthew C; Rogers, Gregory M; Davidson, Richard J
Compassion is a key motivator of altruistic behavior, but little is known about individuals' capacity to cultivate compassion through training. We examined whether compassion may be systematically trained by testing whether (a) short-term compassion training increases altruistic behavior and (b) individual differences in altruism are associated with training-induced changes in neural responses to suffering. In healthy adults, we found that compassion training increased altruistic redistribution of funds to a victim encountered outside of the training context. Furthermore, increased altruistic behavior after compassion training was associated with altered activation in brain regions implicated in social cognition and emotion regulation, including the inferior parietal cortex and dorsolateral prefrontal cortex (DLPFC), and in DLPFC connectivity with the nucleus accumbens. These results suggest that compassion can be cultivated with training and that greater altruistic behavior may emerge from increased engagement of neural systems implicated in understanding the suffering of other people, executive and emotional control, and reward processing.
Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard
Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.
Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.
Yeo, S.; Choe, I.H.; Noort, M.W.M.L. van den; Bosch, M.P.C.; Lim, S.
Objective: Functional magnetic resonance imaging (fMRI), in combination with block design paradigms with consecutive acupuncture stimulations, has often been used to investigate the neural responses to acupuncture. In this study, we investigated whether previous acupuncture stimulations can affect
Alberts, David S.
Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.
Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong
Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.
Hill, David L.
Sensory systems adapt to changing environmental influences by coordinated alterations in structure and function. These alterations are referred to as plastic changes. The gustatory system displays numerous plastic changes even in receptor cells. This review focuses on the plasticity of gustatory structures through the first synaptic relay in the brain. Unlike other sensory systems, there is a remarkable amount of environmentally induced changes in these peripheral-most neural structures. The ...
Curtis, Steven A. (Inventor)
Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.
Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.
Yang, Y.; Dewald, J.P.A.; van der Helm, F.C.T.; Schouten, A.C.
Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending
Campbell, Luke; Kaicer, Arielle; Briggs, Robert; O'Leary, Stephen
To record cochlear responses to acoustic stimulation (electrocochleography) directly from a cochlear implant (CI) in awake recipients with residual hearing, using an adaptation of Neural Response Telemetry (NRT) that achieves a 10-ms recording window. Modern cochlear implants contain circuitry for recording neural responses to electrical stimulation, which is known in Cochlear Ltd systems as NRT. We adapted NRT to achieve an extended recording window long enough to record an acoustic electrocochleogram. This paper reports recordings made with this system in recipients with residual hearing. Subjects were adults with CI422 CIs who retained audiometric thresholds between 75 and 90 dB HL at 500 Hz in their implanted ear. The CI was interfaced to a laptop via a Freedom speech processor connected by USB. Calibrated acoustic stimuli (clicks and tone bursts between 500 and 1,500 Hz) were presented via insert tube phones to the implanted ear. Responses were acquired through the adapted NRT system. Recordings were made from apical, mid-array, and basal electrodes. Electrocochleography responses were compared with audiometric thresholds. Electrocochleography could be recorded from all five subjects. The compound action potential, cochlear microphonic, and summating potentials were identified. Good quality recordings were most reliably attained from apical electrodes using 40 to 100 repetitions. Audiometric thresholds were similar to compound action potential thresholds. Intracochlear responses to acoustic stimulation can be recorded directly from the CI in awake recipients with residual hearing. This may prove useful for monitoring postoperative hearing and for device fitting.
in studying neuronal avalanches. Finally, we show in a computational model that two prevalent features of cortical single-neuron activity, irregular spiking and the decline of response variability at stimulus onset, both are emergent properties of a recurrent network operating near criticality. Our findings establish criticality as a unifying principle for the statistics of single-neuron spiking and the collective behavior of recurrent circuits in cerebral cortex. Moreover, as the observed decline in response variability is regarded as an essential mechanism to enhance response fidelity to stimuli, our discovery of its relation to network criticality offers a starting point toward unraveling the possible roles of critical dynamics in neural coding.
Adam E. Green
Conclusions: In this sample of young adult smokers, GWLs promoted neural activation in brain regions involved in cognitive and affective decision-making and memory formation and the effects of GWLs did not differ on branded or plain cigarette packaging. These findings complement other recent neuroimaging GWL studies conducted with older adult smokers and with adolescents by demonstrating similar patterns of neural activation in response to GWLs among young adult smokers.
Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.
Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.
KARAM M. Z. OTHMAN
Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.
Cascio, Carissa J; Foss-Feig, Jennifer H; Heacock, Jessica; Schauder, Kimberly B; Loring, Whitney A; Rogers, Baxter P; Pryweller, Jennifer R; Newsom, Cassandra R; Cockhren, Jurnell; Cao, Aize; Bolton, Scott
Restricted interests are a class of repetitive behavior in autism spectrum disorders (ASD) whose intensity and narrow focus often contribute to significant interference with daily functioning. While numerous neuroimaging studies have investigated executive circuits as putative neural substrates of repetitive behavior, recent work implicates affective neural circuits in restricted interests. We sought to explore the role of affective neural circuits and determine how restricted interests are distinguished from hobbies or interests in typical development. We compared a group of children with ASD to a typically developing (TD) group of children with strong interests or hobbies, employing parent report, an operant behavioral task, and functional imaging with personalized stimuli based on individual interests. While performance on the operant task was similar between the two groups, parent report of intensity and interference of interests was significantly higher in the ASD group. Both the ASD and TD groups showed increased BOLD response in widespread affective neural regions to the pictures of their own interest. When viewing pictures of other children's interests, the TD group showed a similar pattern, whereas BOLD response in the ASD group was much more limited. Increased BOLD response in the insula and anterior cingulate cortex distinguished the ASD from the TD group, and parent report of the intensity and interference with daily life of the child's restricted interest predicted insula response. While affective neural network response and operant behavior are comparable in typical and restricted interests, the narrowness of focus that clinically distinguishes restricted interests in ASD is reflected in more interference in daily life and aberrantly enhanced insula and anterior cingulate response to individuals' own interests in the ASD group. These results further support the involvement of affective neural networks in repetitive behaviors in ASD. © 2013 The
Daly, Ian; Malik, Asad; Hwang, Faustina; Roesch, Etienne; Weaver, James; Kirke, Alexis; Williams, Duncan; Miranda, Eduardo; Nasuto, Slawomir J
This paper presents an EEG study into the neural correlates of music-induced emotions. We presented participants with a large dataset containing musical pieces in different styles, and asked them to report on their induced emotional responses. We found neural correlates of music-induced emotion in a number of frequencies over the pre-frontal cortex. Additionally, we found a set of patterns of functional connectivity, defined by inter-channel coherence measures, to be significantly different between groups of music-induced emotional responses. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Daniel Hart Baker
Full Text Available White pixel noise is widely used to estimate the level of internal noise in a system by injecting external variance into the detecting mechanism. Recent work (Baker & Meese, 2012, J Vis, 12(10:20 has provided psychophysical evidence that such noise masks might also cause suppression that could invalidate estimates of internal noise. Here we measure neural population responses directly, using steady-state visual evoked potentials, elicited by target stimuli embedded in different mask types. Sinusoidal target gratings of 1c/deg flickered at 5Hz, and were shown in isolation, or with superimposed orthogonal grating masks or 2D white noise masks, flickering at 7Hz. Compared with responses to a blank screen, the Fourier amplitude at the target frequency increased monotonically as a function of target contrast when no mask was present. Both orthogonal and white noise masks caused rightward shifts of the contrast response function, providing evidence of contrast gain control suppression. We also calculated within-observer amplitude variance across trials. This increased in proportion to the target response, implying signal-dependent (i.e. multiplicative noise at the system level, the implications of which we discuss for behavioural tasks. This measure of variance was reduced by both mask types, consistent with the changes in mean target response. An alternative variety of noise, which we term zero-dimensional noise, involves trial-by-trial jittering of the target contrast. This type of noise produced no gain control suppression, and increased the amplitude variance across trials.
Campbell, Luke J.; Sly, David James; O'Leary, Stephen John
This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.
Full Text Available Although attention deficit hyperactivity disorders (ADHD and autism spectrum disorders (ASD share certain neurocognitive characteristics, it has been hypothesized to differentiate the two disorders based on their brain's reward responsiveness to either social or monetary reward. Thus, the present fMRI study investigated neural activation in response to both reward types in age and IQ-matched boys with ADHD versus ASD relative to typically controls (TDC. A significant group by reward type interaction effect emerged in the ventral striatum with greater activation to monetary versus social reward only in TDC, whereas subjects with ADHD responded equally strong to both reward types, and subjects with ASD showed low striatal reactivity across both reward conditions. Moreover, disorder-specific neural abnormalities were revealed, including medial prefrontal hyperactivation in response to social reward in ADHD versus ventral striatal hypoactivation in response to monetary reward in ASD. Shared dysfunction was characterized by fronto-striato-parietal hypoactivation in both clinical groups when money was at stake. Interestingly, lower neural activation within parietal circuitry was associated with higher autistic traits across the entire study sample. In sum, the present findings concur with the assumption that both ASD and ADHD display distinct and shared neural dysfunction in response to reward.
Andrillon, Thomas; Poulsen, Andreas Trier; Hansen, Lars Kai
Sleep is characterized by a loss of behavioral responsiveness. However, recent research has shown that the sleeping brain is not completely disconnected from its environment. How neural activity constrains the ability to process sensory information while asleep is yet unclear. Here, we instructed...... by Lempel-Ziv complexity (LZc), a measure shown to track arousal in sleep and anesthesia. Neural activity related to the semantic content of stimuli was conserved in light non-rapid eye movement (NREM) sleep. However, these processes were suppressed in deep NREM sleep and, importantly, also in REM sleep......, despite the recovery of wake-like neural activity in the latter. In NREM sleep, sensory activations were counterbalanced by evoked down states, which, when present, blocked further processing of external information. In addition, responsiveness markers correlated positively with baseline complexity, which...
Drábek Oldøich; Taufer Ivan
The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.
Full Text Available The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.
Gradin, Victoria B; Waiter, Gordon; Kumar, Poornima; Stickle, Catriona; Milders, Maarten; Matthews, Keith; Reid, Ian; Hall, Jeremy; Steele, J Douglas
Social exclusion is an influential concept in politics, mental health and social psychology. Studies on healthy subjects have implicated the medial prefrontal cortex (mPFC), a region involved in emotional and social information processing, in neural responses to social exclusion. Impairments in social interactions are common in schizophrenia and are associated with reduced quality of life. Core symptoms such as delusions usually have a social content. However little is known about the neural underpinnings of social abnormalities. The aim of this study was to investigate the neural substrates of social exclusion in schizophrenia. Patients with schizophrenia and healthy controls underwent fMRI while participating in a popular social exclusion paradigm. This task involves passing a 'ball' between the participant and two cartoon representations of other subjects. The extent of social exclusion (ball not being passed to the participant) was parametrically varied throughout the task. Replicating previous findings, increasing social exclusion activated the mPFC in controls. In contrast, patients with schizophrenia failed to modulate mPFC responses with increasing exclusion. Furthermore, the blunted response to exclusion correlated with increased severity of positive symptoms. These data support the hypothesis that the neural response to social exclusion differs in schizophrenia, highlighting the mPFC as a potential substrate of impaired social interactions.
Guyer, Amanda E; Choate, Victoria R; Pine, Daniel S; Nelson, Eric E
Peer feedback affects adolescents' behaviors, cognitions and emotions. We examined neural circuitry underlying adolescents' emotional response to peer feedback using a functional neuroimaging paradigm whereby, 36 adolescents (aged 9-17 years) believed they would interact with unknown peers postscan. Neural activity was expected to vary based on adolescents' perceptions of peers and feedback type. Ventrolateral prefrontal cortex (vlPFC) activity was found when adolescents indicated how they felt following feedback (acceptance or rejection) from peers of low vs high interest. Greater activation in both cortical (e.g. superior temporal gyrus, insula, anterior cingulate) and subcortical (e.g. striatum, thalamus) regions emerged in response to acceptance vs rejection feedback. Response to acceptance also varied by age and gender in similar regions (e.g. superior temporal gyrus, fusiform, insula), with greater age-related increases in activation to acceptance vs rejection for females than males. Affective response to rejection vs acceptance did not yield significantly greater neural activity in any region. vlPFC response suggests cognitive flexibility in reappraising initial perceptions of peers following feedback. Striatal response suggests that acceptance is a potent social reward for adolescents, an interpretation supported by more positive self-reported affective response to acceptance than rejection from high- but not low-interest peers.
Junius, D.; Dau, Torsten
), disparities occurred between the responses, reflecting a nonlinearity in the processing when neural activity is integrated across frequency. In the third experiment, the effect of within-train rate on wave-V response was investigated. The response to the chirp presented at a within-train rate of 95 Hz...... processing in the human auditory system. The findings might also be useful for the development of effective stimulation paradigms in clinical applications....
Melrose, A James; Bailer, Ursula; Wierenga, Christina E; Bischoff-Grethe, Amanda; Paulus, Martin P; Kaye, Walter H
Amphetamine, likely via action on the brain's dopaminergic systems, induces anorectic eating behavior and blunts dopaminergic midbrain activation to rewards. Past work has hypothesized that this blunted reward responsivity is a result of increasing tonic over phasic DA activity. We sought to extend past findings to sweet taste during fMRI following single-blind administration of dextroamphetamine and placebo in 11 healthy women. We hypothesized that neural response in both limbic and cognitive sweet taste circuits would mirror past work with monetary rewards by effectively blunting sweet taste reward, and 'equalizing' it's rewarding taste with receipt of water. Behavioral results showed that amphetamine reduced self-reported hunger (supporting the existence of amphetamine anorexia) and increased self-report euphoria. In addition, region of Interest analysis revealed significant treatment by taste interactions in the middle insula and dorsal anterior cingulate confirming the 'equalizing' hypothesis in the cingulate, but unlike monetary reinforcers, the insula actually evinced enhanced separation between tastes on the amphetamine day. These results suggest a divergence from prior research using monetary reinforcers when extended to primary reinforcers, and may hint that altering dopaminergic signaling in the insula and anterior cingulate may be a target for pharmacological manipulation of appetite, and the treatment of obesity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Park, Mona; Hennig-Fast, Kristina; Bao, Yan; Carl, Petra; Pöppel, Ernst; Welker, Lorenz; Reiser, Maximilian; Meindl, Thomas; Gutyrchik, Evgeny
Music communicates and evokes emotions. The number of studies on the neural correlates of musical emotion processing is increasing but few have investigated the factors that modulate these neural activations. Previous research has shown that personality traits account for individual variability of neural responses. In this study, we used functional magnetic resonance imaging (fMRI) to investigate how the dimensions Extraversion and Neuroticism are related to differences in brain reactivity to musical stimuli expressing the emotions happiness, sadness and fear. 12 participants (7 female, M=20.33 years) completed the NEO-Five Factor Inventory (NEO-FFI) and were scanned while performing a passive listening task. Neurofunctional analyses revealed significant positive correlations between Neuroticism scores and activations in bilateral basal ganglia, insula and orbitofrontal cortex in response to music expressing happiness. Extraversion scores were marginally negatively correlated with activations in the right amygdala in response to music expressing fear. Our findings show that subjects' personality may have a predictive power in the neural correlates of musical emotion processing and should be considered in the context of experimental group homogeneity. Copyright © 2013 Elsevier B.V. All rights reserved.
Markwardt, Neil T.; Stokol, Jodi; Rennaker, Robert L.
Glial scar formation around neural interfaces inhibits their ability to acquire usable signals from the surrounding neurons. To improve neural recording performance, the inflammatory response and glial scarring must be minimized. Previous work has indicated that meningeally derived cells participate in the immune response, and it is possible that the meninges may grow down around the shank of a neural implant, contributing to the formation of the glial scar. This study examines whether the glial scar can be reduced by placing a neural probe completely below the meninges. Rats were implanted with sets of loose microwire implants placed either completely below the meninges or implanted conventionally with the upper end penetrating the meninges, but not attached to the skull. Histological analysis was performed 4 weeks following surgical implantation to evaluate the glial scar. Our results found that sub-meninges implants showed an average reduction in reactive astrocyte activity of 63% compared to trans-meninges implants. Microglial activity was also reduced for sub-meninges implants. These results suggest that techniques that isolate implants from the meninges offer the potential to reduce the encapsulation response which should improve chronic recording quality and stability. PMID:23370311
Moroz, Leonid L
Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved
Full Text Available The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.
Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei
The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.
Abbas, Paul J; Tejani, Viral D; Scheperle, Rachel A; Brown, Carolyn J
This report describes the results of a series of experiments where we use the neural response telemetry (NRT) system of the Nucleus cochlear implant (CI) to measure the response of the peripheral auditory system to acoustic stimulation in Nucleus Hybrid CI users. The objectives of this study were to determine whether they could separate responses from hair cells and neurons and to evaluate the stability of these measures over time. Forty-four CI users participated. They all had residual acoustic hearing and used a Nucleus Hybrid S8, S12, or L24 CI or the standard lateral wall CI422 implant. The NRT system of the CI was used to trigger an acoustic stimulus (500-Hz tone burst or click), which was presented at a low stimulation rate (10, 15, or 50 per second) to the implanted ear via an insert earphone and to record the cochlear microphonic, the auditory nerve neurophonic and the compound action potential (CAP) from an apical intracochlear electrode. To record acoustically evoked responses, a longer time window than is available with the commercial NRT software is required. This limitation was circumvented by making multiple recordings for each stimulus using different time delays between the onset of stimulation and the onset of averaging. These recordings were then concatenated off-line. Matched recordings elicited using positive and negative polarity stimuli were added off-line to emphasize neural potentials (SUM) and subtracted off-line to emphasize potentials primarily generated by cochlear hair cells (DIF). These assumptions regarding the origin of the SUM and DIF components were tested by comparing the magnitude of these derived responses recorded using various stimulation rates. Magnitudes of the SUM and DIF components were compared with each other and with behavioral thresholds. SUM and DIF components were identified for most subjects, consistent with both hair cell and neural responses to acoustic stimulation. For a subset of the study participants, the DIF
Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah
Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffer...
Ayodele I., Olanipekun
The nonlinear behavior exhibited by altitude control system processes and also the presence of external constraints on the operating conditions causes hitch in the dynamics of system processes. This research work proposes a fault detection/tolerant prediction in an altitude control system. This is done through the artificial neural network fault detection by deploying the neural network approach. A fault detection and isolation module is developed in the actuator system of the Altitude Control System, thereby achieving the goal of this thesis. This can be done by two basic classification stages: Neural Residual Generator (Neural Observer)- This stage is responsible for generating residual errors that can reflect the real behavior of the entire process as against its normal conditions. Adaptive Neural Classifier - This stage is responsible for managing the isolation task of the fault detected by evaluating the generated residual errors from the neural estimator which gives detailed information about faults detected e.g., fault location and time. These two stages can be implemented by executing the tasks listed below: 1. Study and develop a generic three axis stabilized altitude control model based on the reaction wheels. This is established with three separate PD controllers designed for each reaction wheel of the satellite axis using the Matlab - SIMULINK. 2. Develop a dynamic neural network residual generator based on Dynamic Multilayer Perceptron Network (DMLP) which is then applied to the reaction wheel model designed commonly called the actuator in the altitude control system of a satellite 3. Develop a neural network adaptive classifier based on the Learning Vector Quantization (LVQ) model which is used for the isolation concept. The advantages of the proposed dynamic neural network and neural adaptive classifier approach are showcased.
Mazin Abdulrasool Hameed
Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance
Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T
The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.
Madsen, Per Printz
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes
Son, Taeyoon; Wang, Benquan; Lu, Yiming; Chen, Yanjun; Cao, Dingcai; Yao, Xincheng
It is well established that major retinal diseases involve distortions of the retinal neural physiology and blood vascular structures. However, the details of distortions in retinal neurovascular coupling associated with major eye diseases are not well understood. In this study, a multi-modal optical coherence tomography (OCT) imaging system was developed to enable concurrent imaging of retinal neural activity and vascular hemodynamics. Flicker light stimulation was applied to mouse retinas to evoke retinal neural responses and hemodynamic changes. The OCT images were acquired continuously during the pre-stimulation, light-stimulation, and post-stimulation phases. Stimulus-evoked intrinsic optical signals (IOSs) and hemodynamic changes were observed over time in blood-free and blood regions, respectively. Rapid IOSs change occurred almost immediately after stimulation. Both positive and negative signals were observed in adjacent retinal areas. The hemodynamic changes showed time delays after stimulation. The signal magnitudes induced by light stimulation were observed in blood regions and did not show significant changes in blood-free regions. These differences may arise from different mechanisms in blood vessels and neural tissues in response to light stimulation. These characteristics agreed well with our previous observations in mouse retinas. Further development of the multimodal OCT may provide a new imaging method for studying how retinal structures and metabolic and neural functions are affected by age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and other diseases, which promises novel noninvasive biomarkers for early disease detection and reliable treatment evaluations of eye diseases.
Hulsey, Daniel R; Riley, Jonathan R; Loerwald, Kristofer W; Rennaker, Robert L; Kilgard, Michael P; Hays, Seth A
Vagus nerve stimulation (VNS) has emerged as a therapy to treat a wide range of neurological disorders, including epilepsy, depression, stroke, and tinnitus. Activation of neurons in the locus coeruleus (LC) is believed to mediate many of the effects of VNS in the central nervous system. Despite the importance of the LC, there is a dearth of direct evidence characterizing neural activity in response to VNS. A detailed understanding of the brain activity evoked by VNS across a range of stimulation parameters may guide selection of stimulation regimens for therapeutic use. In this study, we recorded neural activity in the LC and the mesencephalic trigeminal nucleus (Me5) in response to VNS over a broad range of current amplitudes, pulse frequencies, train durations, inter-train intervals, and pulse widths. Brief 0.5s trains of VNS drive rapid, phasic firing of LC neurons at 0.1mA. Higher current intensities and longer pulse widths drive greater increases in LC firing rate. Varying the pulse frequency substantially affects the timing, but not the total amount, of phasic LC activity. VNS drives pulse-locked neural activity in the Me5 at current levels above 1.2mA. These results provide insight into VNS-evoked phasic neural activity in multiple neural structures and may be useful in guiding the selection of VNS parameters to enhance clinical efficacy. Copyright © 2016 Elsevier Inc. All rights reserved.
. Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.
Song, Penglong; Wang, Ningyu; Wang, Hui; Xie, Yan; Jia, Jun; Li, Huijun
The precedence effect (PE) is thought to be beneficial for proper localization and perception of sounds. The majority of recent physiological studies focus on the neural discharges correlated with PE in the inferior colliculus (IC). Pentobarbital anesthesia is widely used in physiological studies. However, little is known of the effect of pentobarbital on the discharge of neurons in PE. Neuronal responses in the IC from 23 male SD rats were recorded by standard extracellular recording techniques following presentation of 4 ms white noise bursts, presented from either or both of two loud speakers, at different interstimulus delays (ISDs). The neural responses were recorded for off-line analysis before or after intraperitoneal administration of pentobarbital at a loading or maintenance dose. Data were assessed by one-way repeated measures analysis of variance and pairwise comparisons. When the ipsilateral stimuli were leading, pentobarbital at a loading dose significantly increased normalized response to lagging stimuli during recovery from anesthesia. However, it was not the case when the contralateral stimuli were leading. At a maintenance dose, the normalized response to lagging stimuli were significantly reduced, independent of whether contralateral or ipsilateral stimuli were leading. These data show that pentobarbital have no effect on the normalized response of leading stimuli but can prolong the recovery time of lagging stimuli to paired sources produced PE illusions, which was gradually attenuated during recovery from anesthesia. Thus, extracellular recording immediately after administration of pentobarbital should be avoided in physiological studies of neural correlates of PE. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Xue, Gui; Aron, Adam R; Poldrack, Russell A
The inhibition of speech acts is a critical aspect of human executive control over thought and action, but its neural underpinnings are poorly understood. Using functional magnetic resonance imaging and the stop-signal paradigm, we examined the neural correlates of speech control in comparison to manual motor control. Initiation of a verbal response activated left inferior frontal cortex (IFC: Broca's area). Successful inhibition of speech (naming of letters or pseudowords) engaged a region of right IFC (including pars opercularis and anterior insular cortex) as well as presupplementary motor area (pre-SMA); these regions were also activated by successful inhibition of a hand response (i.e., a button press). Moreover, the speed with which subjects inhibited their responses, stop-signal reaction time, was significantly correlated between speech and manual inhibition tasks. These findings suggest a functional dissociation of left and right IFC in initiating versus inhibiting vocal responses, and that manual responses and speech acts share a common inhibitory mechanism localized in the right IFC and pre-SMA.
is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...
Monsen, P T; Dzwonczyk, M; Manolakos, E S
The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.
Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.
The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we
Canterberry, Melanie; Peltier, MacKenzie R; Brady, Kathleen T; Hanlon, Colleen A
Cocaine users often report a loss of arousal for nondrug-related stimuli, which may contribute to their response to drug-related rewards. However, little is known about users' neural reactivity to emotional nondrug-related stimuli and the potential influence of gender. Test the hypotheses that cocaine-dependent individuals have an attenuated neural response to arousing stimuli relative to controls and that this difference is amplified in women. The brain response to typically arousing positive and negative images as well as neutral images from the International Affective Picture System was measured in 40 individuals (20 non-treatment seeking cocaine-dependent and 20 age- and gender-matched control participants; 50% of whom were women). Images were displayed for 4 s each in blocks of five across two 270-second runs. General linear models assessed within and between group activation differences for the emotional images. Cocaine-dependent individuals had a significantly lower response to typically arousing positive and negative images than controls, with attenuated neural activity present in the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). Analyses by gender revealed less mPFC/ACC activation among female users, but not males, for both positive and negative images. The dampened neural response to typically arousing stimuli among cocaine-dependent polydrug users suggests decreased salience processing for nondrug stimuli, particularly among female users. This decreased responding is consistent with data from other substance using populations and suggests that this may be a general feature of addiction. Amplifying the neural response to naturally arousing nondrug-related reinforcers may present an opportunity for unique behavioral and brain stimulation therapies.
Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante
Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.
Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy
Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…
Petrican, Raluca; Rosenbaum, R. Shayna; Grady, Cheryl
responsiveness to nonverbal affective cues, while also suggesting one explanation for the suppressors’ poorer cognitive performance in social situations. Moreover, our results point to a potential neural mechanism supporting the development and perpetuation of expressive suppression as an emotion regulation strategy. PMID:26365712
Hawes, Daniel R; DeYoung, Colin G; Gray, Jeremy R; Rustichini, Aldo
The relations between intelligence (IQ) and neural responses to monetary gains and losses were investigated in a simple decision task. In 94 healthy adults, typical responses of striatal blood oxygen level-dependent (BOLD) signal after monetary reward and punishment were weaker for subjects with higher IQ. IQ-moderated differential responses to gains and losses were also found for regions in the medial prefrontal cortex, posterior cingulate cortex, and left inferior frontal cortex. These regions have previously been identified with the subjective utility of monetary outcomes. Analysis of subjects' behavior revealed a correlation between IQ and the extent to which choices were related to experienced decision outcomes in preceding trials. Specifically, higher IQ predicted behavior to be more strongly correlated with an extended period of previously experienced decision outcomes, whereas lower IQ predicted behavior to be correlated exclusively to the most recent decision outcomes. We link these behavioral and imaging findings to a theoretical model capable of describing a role for intelligence during the evaluation of rewards generated by unknown probabilistic processes. Our results demonstrate neural differences in how people of different intelligence respond to experienced monetary rewards and punishments. Our theoretical discussion offers a functional description for how these individual differences may be linked to choice behavior. Together, our results and model support the hypothesis that observed correlations between intelligence and preferences may be rooted in the way decision outcomes are experienced ex post, rather than deriving exclusively from how choices are evaluated ex ante.
Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.
We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.
Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.
Flexas, Albert; de Miguel, Pedro; Cela-Conde, Camilo J.; Munar, Enric
This study provides exploratory evidence about how behavioral and neural responses to standard moral dilemmas are influenced by religious belief. Eleven Catholics and 13 Atheists (all female) judged 48 moral dilemmas. Differential neural activity between the two groups was found in precuneus and in prefrontal, frontal and temporal regions. Furthermore, a double dissociation showed that Catholics recruited different areas for deontological (precuneus; temporoparietal junction) and utilitarian moral judgments [dorsolateral prefrontal cortex (DLPFC); temporal poles], whereas Atheists did not (superior parietal gyrus for both types of judgment). Finally, we tested how both groups responded to personal and impersonal moral dilemmas: Catholics showed enhanced activity in DLPFC and posterior cingulate cortex during utilitarian moral judgments to impersonal moral dilemmas and enhanced responses in anterior cingulate cortex and superior temporal sulcus during deontological moral judgments to personal moral dilemmas. Our results indicate that moral judgment can be influenced by an acquired set of norms and conventions transmitted through religious indoctrination and practice. Catholic individuals may hold enhanced awareness of the incommensurability between two unequivocal doctrines of the Catholic belief set, triggered explicitly in a moral dilemma: help and care in all circumstances—but thou shalt not kill. PMID:23160812
Braams, Barbara R; Güroğlu, Berna; de Water, Erik; Meuwese, Rosa; Koolschijn, P Cédric; Peper, Jiska S; Crone, Eveline A
Prior studies have suggested that positive social interactions are experienced as rewarding. Yet, it is not well understood how social relationships influence neural responses to other persons' gains. In this study, we investigated neural responses during a gambling task in which healthy participants (N = 31; 18 females) could win or lose money for themselves, their best friend or a disliked other (antagonist). At the moment of receiving outcome, person-related activity was observed in the dorsal medial prefrontal cortex (dmPFC), precuneus and temporal parietal junction (TPJ), showing higher activity for friends and antagonists than for self, and this activity was independent of outcome. The only region showing an interaction between the person-participants played for and outcome was the ventral striatum. Specifically, the striatum was more active following gains than losses for self and friends, whereas for the antagonist this pattern was reversed. Together, these results show that, in a context with social and reward information, social aspects are processed in brain regions associated with social cognition (mPFC, TPJ), and reward aspects are processed in primary reward areas (striatum). Furthermore, there is an interaction of social and reward information in the striatum, such that reward-related activity was dependent on social relationship. © The Author (2013). Published by Oxford University Press. For Permissions, please email: firstname.lastname@example.org.
Botha, Elizabeth; Casasent, David; Barnard, Etienne
Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.
Yin, Ming; Ghovanloo, Maysam
We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.
Smith, Robert E.
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.
Chen, Yvonnes; Fowler, Carina H; Papa, Vlad B; Lepping, Rebecca J; Brucks, Morgan G; Fox, Andrew T; Martin, Laura E
Although adolescents are a group heavily targeted by the e-cigarette industry, research in cue-reactivity has not previously examined adolescents' behavioral and neural responses to e-cigarette advertising. This study addressed this gap through two experiments. In Experiment One, adult traditional cigarette smokers (n = 41) and non-smokers (n = 41) answered questions about e-cigarette and neutral advertising images. The 40 e-cigarette advertising images that most increased desire to use the product were matched to 40 neutral advertising images with similar content. In Experiment Two, the 80 advertising images selected in Experiment One were presented to adolescents (n = 30) during an functional magnetic resonance imaging brain scan. There was a range of traditional cigarette smoking across the sample with some adolescents engaging in daily smoking and others who had never smoked. Adolescents self-reported that viewing the e-cigarette advertising images increased their desire to smoke. Additionally, all participants regardless of smoking statuses showed significantly greater brain activation to e-cigarette advertisements in areas associated with cognitive control (left middle frontal gyrus), reward (right medial frontal gyrus), visual processing/attention (left lingual gyrus/fusiform gyrus, right inferior parietal lobule, left posterior cingulate, left angular gyrus) and memory (right parahippocampus, left insula). Further, an exploratory analysis showed that compared with age-matched non-smokers (n = 7), adolescent smokers (n = 7) displayed significantly greater neural activation to e-cigarette advertising images in the left inferior temporal gyrus/fusiform gyrus, compared with their responses to neutral advertising images. Overall, participants' brain responses to e-cigarette advertisements suggest a need to further investigate the long-run impact of e-cigarette advertising on adolescents. © 2017 Society for the Study of Addiction.
Full Text Available Economic status played an important role in the modulation of economic decision making. The present fMRI study aimed at investigating how economic status modulated behavioral and neural responses to unfairness in a modified Ultimatum Game (UG. During scanning, participants played as responders in the UG, and they were informed of the economic status of proposers before receiving offers. At the behavioral level, higher rejection rates and lower fairness ratings were revealed when proposers were in high economic status than in low economic status. Besides, the most time-consuming decisions tended to occur at lower unfairness level when the proposers were in high (relative to low economic status. At the neural level, stronger activation of left thalamus was revealed when fair offers were proposed by proposers in high rather than in low economic status. Greater activation of right medial prefrontal cortex was revealed during acceptance to unfair offers in high economic status condition rather than in low economic status condition. Taken together, these findings shed light on the significance of proposers’ economic status in responders’ social decision making in UG.
Full Text Available Introduction Neural response telemetry (NRT is a method of capturing the action potential of the distal portion of the auditory nerve in cochlear implant (CI users, using the CI itself to elicit and record the answers. In addition, it can also measure the recovery function of the auditory nerve (REC, that is, the refractory properties of the nerve. It is not clear in the literature whether the responses from adults are the same as those from children. Objective To compare the results of NRT and REC between adults and children undergoing CI surgery. Methods Cross-sectional, descriptive, and retrospective study of the results of NRT and REC for patients undergoing IC at our service. The NRT is assessed by the level of amplitude (microvolts and REC as a function of three parameters: A (saturation level, in microvolts, t0 (absolute refractory period, in seconds, and tau (curve of the model function, measured in three electrodes (apical, medial, and basal. Results Fifty-two patients were evaluated with intraoperative NRT (26 adults and 26 children, and 24 with REC (12 adults and 12 children. No statistically significant difference was found between intraoperative responses of adults and children for NRT or for REC's three parameters, except for parameter A of the basal electrode. Conclusion The results of intraoperative NRT and REC were not different between adults and children, except for parameter A of the basal electrode.
Carvalho, Bettina; Hamerschmidt, Rogerio; Wiemes, Gislaine
Introduction Neural response telemetry (NRT) is a method of capturing the action potential of the distal portion of the auditory nerve in cochlear implant (CI) users, using the CI itself to elicit and record the answers. In addition, it can also measure the recovery function of the auditory nerve (REC), that is, the refractory properties of the nerve. It is not clear in the literature whether the responses from adults are the same as those from children. Objective To compare the results of NRT and REC between adults and children undergoing CI surgery. Methods Cross-sectional, descriptive, and retrospective study of the results of NRT and REC for patients undergoing IC at our service. The NRT is assessed by the level of amplitude (microvolts) and REC as a function of three parameters: A (saturation level, in microvolts), t0 (absolute refractory period, in seconds), and tau (curve of the model function), measured in three electrodes (apical, medial, and basal). Results Fifty-two patients were evaluated with intraoperative NRT (26 adults and 26 children), and 24 with REC (12 adults and 12 children). No statistically significant difference was found between intraoperative responses of adults and children for NRT or for REC's three parameters, except for parameter A of the basal electrode. Conclusion The results of intraoperative NRT and REC were not different between adults and children, except for parameter A of the basal electrode. PMID:25992145
Stubbs, Derek F.
We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...
Interrelationships between Hormones, Behavior, and Affect during Adolescence: Complex Relationships Exist between Reproductive Hormones, Stress‐Related Hormones, and the Activity of Neural Systems That Regulate Behavioral Affect. Comments on Part III
CAMERON, JUDY L
..., and changes in behavioral affect regulation. The interactions between activity in the reproductive axis, the neural systems that regulate stress, hormones produced in response to stress, and neural systems governing behavioral affect regulation...
Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra
Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.
Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P
In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.
Vagnoni, Eleonora; Lourenco, Stella F; Longo, Matthew R
Objects on a collision course with an observer produce a specific pattern of optical expansion on the retina known as looming, which in theory exactly specifies the time-to-collision (TTC) of approaching objects. It was recently demonstrated that the affective content of looming stimuli influences perceived TTC, with threatening objects judged as approaching sooner than non-threatening objects. Here, the neural mechanisms by which perceived threat modulates spatiotemporal perception were investigated. Participants judged the TTC of threatening (snakes, spiders) or non-threatening (butterflies, rabbits) stimuli, which expanded in size at a rate indicating one of five TTCs. Visual-evoked potentials (VEPs) and oscillatory neural responses measured with electroencephalography were analysed. The arrival time of threatening stimuli was underestimated compared with non-threatening stimuli, though an interaction suggested that this underestimation was not constant across TTCs. Further, both speed of approach and threat modulated both VEPs and oscillatory responses. Speed of approach modulated the N1 parietal and oscillations in the beta band. Threat modulated several VEP components (P1, N1 frontal, N1 occipital, early posterior negativity and late positive potential) and oscillations in the alpha and high gamma band. The results for the high gamma band suggest an interaction between these two factors. Previous evidence suggests that looming stimuli activate sensorimotor areas, even in the absence of an intended action. The current results show that threat disrupts the synchronization over the sensorimotor areas that are likely activated by the presentation of a looming stimulus. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
S. Shaun Ho
Full Text Available Mothers need to make caregiving decisions to meet the needs of children, which may or may not result in positive child feedback. Variations in caregivers’ emotional reactivity to unpleasant child-feedback may be partially explained by their dispositional empathy levels. Furthermore, empathic response to the child’s unpleasant feedback likely helps mothers to regulate their own stress. We investigated the relationship between maternal dispositional empathy, stress reactivity, and neural correlates of child feedback to caregiving decisions. In Part 1 of the study, 33 female participants were recruited to undergo a lab-based mild stressor, the Social Evaluation Test (SET, and then in Part 2 of the study, a subset of the participants, fourteen mothers, performed a Parenting Decision Making Task (PDMT in an fMRI setting. Four dimensions of dispositional empathy based on the Interpersonal Reactivity Index were measured in all participants – Personal Distress, Empathic Concern, Perspective Taking, and Fantasy. Overall, we found that the Personal Distress and Perspective Taking were associated with greater and lesser cortisol reactivity, respectively. The four types of empathy were distinctly associated with the negative (versus positive child feedback activation in the brain. Personal Distress was associated with amygdala and hypothalamus activation, Empathic Concern with the left ventral striatum, ventrolateral prefrontal cortex (VLPFC, and supplemental motor area (SMA activation, and Fantasy with the septal area, right SMA and VLPFC activation. Interestingly, hypothalamus-septal coupling during the negative feedback condition was associated with less PDMT-related cortisol reactivity. The roles of distinct forms of dispositional empathy in neural and stress responses are discussed.
Zioga, Ioanna; Di Bernardi Luft, Caroline; Bhattacharya, Joydeep
Current research on music processing and syntax or semantics in language suggests that music and language share partially overlapping neural resources. Pitch also constitutes a common denominator, forming melody in music and prosody in language. Further, pitch perception is modulated by musical training. The present study investigated how music and language interact on pitch dimension and whether musical training plays a role in this interaction. For this purpose, we used melodies ending on an expected or unexpected note (melodic expectancy being estimated by a computational model) paired with prosodic utterances which were either expected (statements with falling pitch) or relatively unexpected (questions with rising pitch). Participants' (22 musicians, 20 nonmusicians) ERPs and behavioural responses in a statement/question discrimination task were recorded. Participants were faster for simultaneous expectancy violations in the melodic and linguistic stimuli. Further, musicians performed better than nonmusicians, which may be related to their increased pitch tracking ability. At the neural level, prosodic violations elicited a front-central positive ERP around 150ms after the onset of the last word/note, while musicians presented reduced P600 in response to strong incongruities (questions on low-probability notes). Critically, musicians' P800 amplitudes were proportional to their level of musical training, suggesting that expertise might shape the pitch processing of language. The beneficial aspect of expertise could be attributed to its strengthening effect of general executive functions. These findings offer novel contributions to our understanding of shared higher-order mechanisms between music and language processing on pitch dimension, and further demonstrate a potential modulation by musical expertise. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
Full Text Available Substance abuse in pregnant and recently postpartum women is a major public health concern because of effects on the infant and on the ability of the adult to care for the infant. In addition to the negative health effects of teratogenic substances on fetal development, substance use can contribute to difficulties associated with the social and behavioral aspects of parenting. Neural circuits associated with parenting behavior overlap with circuits involved in addiction (e.g., frontal, striatal and limbic systems and thus may be co-opted for the craving/reward cycle associated with substance use and abuse and be less available for parenting. The current study investigates the degree to which neural circuits associated with parenting are disrupted in mothers who are substance-using. Specifically, we used functional magnetic resonance imaging to examine the neural response to emotional infant cues (faces and cries in substance-using compared to non-using mothers. In response to both faces (of varying emotional valence and cries (of varying distress levels, substance-using mothers evidenced reduced neural activation in regions that have been previously implicated in reward and motivation as well as regions involved in cognitive control. Specifically, in response to faces, substance users showed reduced activation in prefrontal regions, including the dorsolateral and ventromedial prefrontal cortex, as well as visual processing (occipital lobes and limbic regions (parahippocampus and amygdala. Similarly, in response to infant cries substance-using mothers showed reduced activation relative to non-using mothers in prefrontal regions, auditory sensory processing regions, insula and limbic regions (parahippocampus and amygdala. These findings suggest that infant stimuli may be less salient for substance-using mothers, and such reduced saliency may impair developing infant-caregiver attachment and the ability of mothers to respond appropriately to their
Shin, Yeon Soon; Kim, Hye-Young; Han, Sanghoon
Accurate person perception is crucial in social decision-making. One of the central elements in successful social perception is the ability to understand another's response bias; this is because the same behavior can represent different inner states depending on whether other people are yea-sayers or naysayers. In the present study, we have tried to investigate how the internal biases of others are perceived. Using a multi-trial learning paradigm, perceivers made predictions about a target's responses to various suggested activities and then received feedback for each prediction trial-by-trial. Our hypotheses were that (1) the internal decision criterion of the targets would be realized through repeated experiences, and (2) due to positive-negative asymmetry, yea-sayers would be recognized more gradually than naysayers through the probabilistic integration of repeated experiences. To find neural evidence that tracks probabilistic integration when forming person knowledge on response biases, we employed a model-based fMRI with a State-Space Model. We discovered that person knowledge about yea-sayers modulated several brain regions, including caudate nucleus, DLPFC, hippocampus, etc. Moreover, when person knowledge was updated with incorrect performance feedback, brain regions including the caudate nucleus, DLPFC, dmPFC, and TPJ were also involved. There were overlapping regions for both processes, caudate nucleus and DLPFC, suggesting that these regions take crucial roles in forming person knowledge with repeated feedback, while reflecting acquired information up to the current prediction. Copyright © 2014 Elsevier Inc. All rights reserved.
van der Laan, Laura N.; Barendse, Marjolein E. A.; Viergever, Max A.; Smeets, Paul A. M.
Impulsivity is a personality trait that is linked to unhealthy eating and overweight. A few studies assessed how impulsivity relates to neural responses to anticipating and tasting food, but it is unknown how impulsivity relates to neural responses during food choice. Although impulsivity is a
Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.
An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.
Cieslewski, Grzegorz; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C
This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.
These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identiﬁcation. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and ...
Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad
The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...
Kupershtein, Leonid M.; Martyniuk, Tatiana B.; Krencin, Myhail D.; Kozhemiako, Andriy V.; Bezsmertnyi, Yurii; Bezsmertna, Halyna; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, RóŻa; Uvaysova, Svetlana
In the work the hybrid expert system for stroke diagnosis was presented. The base of expert system consists of neural network and production rules. This program can quickly and accurately set to the patient preliminary and final diagnoses, get examination and treatment plans, print data of patient, analyze statistics data and perform parameterized search for patients.
Insel, Catherine; Reinen, Jenna; Weber, Jochen; Wager, Tor D; Jarskog, L Fredrik; Shohamy, Daphna; Smith, Edward E
Schizophrenia is characterized by an abnormal dopamine system, and dopamine blockade is the primary mechanism of antipsychotic treatment. Consistent with the known role of dopamine in reward processing, prior research has demonstrated that patients with schizophrenia exhibit impairments in reward-based learning. However, it remains unknown how treatment with antipsychotic medication impacts the behavioral and neural signatures of reinforcement learning in schizophrenia. The goal of this study was to examine whether antipsychotic medication modulates behavioral and neural responses to prediction error coding during reinforcement learning. Patients with schizophrenia completed a reinforcement learning task while undergoing functional magnetic resonance imaging. The task consisted of two separate conditions in which participants accumulated monetary gain or avoided monetary loss. Behavioral results indicated that antipsychotic medication dose was associated with altered behavioral approaches to learning, such that patients taking higher doses of medication showed increased sensitivity to negative reinforcement. Higher doses of antipsychotic medication were also associated with higher learning rates (LRs), suggesting that medication enhanced sensitivity to trial-by-trial feedback. Neuroimaging data demonstrated that antipsychotic dose was related to differences in neural signatures of feedback prediction error during the loss condition. Specifically, patients taking higher doses of medication showed attenuated prediction error responses in the striatum and the medial prefrontal cortex. These findings indicate that antipsychotic medication treatment may influence motivational processes in patients with schizophrenia.
Garris, Michael D.; Wilson, Charles L.
Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.
Kulkarni, Arun D.; Giridhar, G. B.; Coca, Praveen
During the last few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of neural networks and fuzzy logic seems natural because the two approaches generally attack the design of `intelligent' system from quite different angles. Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic often deals with issues such as reasoning in a high (semantic or linguistic) level. Consequently the two technologies complement each other. In this paper, we combine neural networks with fuzzy logic techniques. We propose an artificial neural network (ANN) model for a fuzzy logic decision system. The model consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns the decision rules using a supervised gradient descent procedure. As an illustration we considered two examples. The first example deals with pixel classification in multispectral satellite images. In our second example we used the fuzzy decision system to analyze data from magnetic resonance imaging (MRI) scans for tissue classification.
Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.
Perera. J. Sebastian
Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.
Lo, Yee-Man V.
The brain can perform the tasks of associative recall detection recognition and optimization. In this paper space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC). 1.
Mueller, Stefanie Verena; Mihov, Yoan; Federspiel, Andrea; Wiest, Roland; Hasler, Gregor
Bulimia nervosa has been associated with a dysregulated catecholamine system. Nevertheless, the influence of this dysregulation on bulimic symptoms, on neural activity, and on the course of the illness is not clear yet. An instructive paradigm for directly investigating the relationship between catecholaminergic functioning and bulimia nervosa has involved the behavioral and neural responses to experimental catecholamine depletion. The purpose of this study was to examine the neural substrate of catecholaminergic dysfunction in bulimia nervosa and its relationship to relapse. In a randomized, double-blind and crossover study design, catecholamine depletion was achieved by using the oral administration of alpha-methyl-paratyrosine (AMPT) over 24 h in 18 remitted bulimic (rBN) and 22 healthy (HC) female participants. Cerebral blood flow (CBF) was measured using a pseudo continuous arterial spin labeling (pCASL) sequence. In a follow-up telephone interview, bulimic relapse was assessed. Following AMPT, rBN participants revealed an increased vigor reduction and CBF decreases in the pallidum and posterior midcingulate cortex (pMCC) relative to HC participants showing no CBF changes in these regions. These results indicated that the pallidum and the pMCC are the functional neural correlates of the dysregulated catecholamine system in bulimia nervosa. Bulimic relapse was associated with increased depressive symptoms and CBF reduction in the hippocampus/parahippocampal gyrus following catecholamine depletion. AMPT-induced increased CBF in this region predicted staying in remission. These findings demonstrated the importance of depressive symptoms and the stress system in the course of bulimia nervosa. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
Chapin, Heather; Jantzen, Kelly; Kelso, J A Scott; Steinberg, Fred; Large, Edward
Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production, perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time emotional responses on a 2-dimensional rating scale (arousal and valence) as they listened to each performance. During fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to the expressive dynamics of human music performance, and both emotion and reward related activations during music listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror neuron system. These findings show that expressive music performance evokes emotion and reward related neural activations, and that music's affective impact on the brains of listeners is altered by musical training. Our observations are consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at least in part, on the perception of movement and on violations of pulse-based temporal expectancies.
Full Text Available Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production, perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time emotional responses on a 2-dimensional rating scale (arousal and valence as they listened to each performance. During fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to the expressive dynamics of human music performance, and both emotion and reward related activations during music listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror neuron system. These findings show that expressive music performance evokes emotion and reward related neural activations, and that music's affective impact on the brains of listeners is altered by musical training. Our observations are consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at least in part, on the perception of movement and on violations of pulse-based temporal expectancies.
Shenoy, Krishna V.; Kaufman, Matthew T.; Sahani, Maneesh; Churchland, Mark M.
Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached. PMID:21763517
Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.
Lee, Andrew; Casasent, David
An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.
McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A
The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry
Chambers, Christopher D; Garavan, Hugh; Bellgrove, Mark A
Neural mechanisms of cognitive control enable us to initiate, coordinate and update behaviour. Central to successful control is the ability to suppress actions that are no longer relevant or required. In this article, we review the contribution of cognitive neuroscience, molecular genetics and clinical investigations to understanding how response inhibition is mediated in the human brain. In Section 1, we consider insights into the neural basis of inhibitory control from the effects of neural interference, neural dysfunction, and drug addiction. In Section 2, we explore the functional specificity of inhibitory mechanisms among a range of related processes, including response selection, working memory, and attention. In Section 3, we focus on the contribution of response inhibition to understanding flexible behaviour, including the effects of learning and individual differences. Finally, in Section 4, we propose a series of technical and conceptual objectives for future studies addressing the neural basis of inhibition.
Berlin, Heather A; Schulz, Kurt P; Zhang, Sam; Turetzky, Rachel; Rosenthal, David; Goodman, Wayne
Failure to inhibit recurrent anxiety-provoking thoughts is a central symptom of obsessive-compulsive disorder (OCD). Neuroimaging studies suggest inhibitory control and disgust processing abnormalities in patients with OCD. However, the emotional modulation of response inhibition deficits in OCD and their neural correlates remain to be elucidated. For this preliminary study we administered an adapted affective response inhibition paradigm, an emotional go/no-go task, during fMRI to characterize the neural systems underlying disgust-related and fear-related inhibition in nine adults with contamination-type OCD compared to ten matched healthy controls. Participants with OCD had significantly greater anterior insula cortex activation when inhibiting responses to both disgusting (bilateral), and fearful (right-sided) images, compared to healthy controls. They also had increased activation in several frontal, temporal, and parietal regions, but there was no evidence of amygdala activation in OCD or healthy participants and no significant between-group differences in performance on the emotion go/no-go task. The anterior insula appears to play a central role in the emotional modulation of response inhibition in contamination-type OCD to both fearful and disgusting images. The insula may serve as a potential treatment target for contamination-type OCD. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Riem, M.M.E.; van IJzendoorn, M.H.; De Carli, P.; Vingerhoets, A.J.J.M.; Bakermans-Kranenburg, M. J.
The current study examined behavioral and neural responses to infant and adult tears, taking into account childhood experiences with parental love-withdrawal. With functional MRI (fMRI), we measured neural reactivity to pictures of infants and adults with and without tears on their faces in
Ladeby, Rune; Wirenfeldt, Martin; Dalmau, Ishar
Reactive microgliosis is a highly characteristic response to neural injury and disease, which may influence neurodegenerative processes and neural plasticity. We have investigated the origin and characteristics of reactive microglia in the acute phase of their activation in the dentate gyrus...
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.
In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....
Sep 28, 2006 ... Keywords. Dynamic causal modelling; EEG; effective connectivity; event-related potentials; fMRI; neural system ... In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing ...
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage ... environments is script/language identification (Muthusamy et al 1994; Hochberg et al 1997). ... In order to take advantage of the learning and generalization abilities ...
Orr, J A; Westenskow, D R
The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. Neural networks may be useful in creating intelligent anesthesia alarm systems.
Wang, Jing-jing; Li, Yang
M channels, an important regulator of neural excitability, are composed of four subunits of the Kv7 (KCNQ) K(+) channel family. M channels were named as such because their activity was suppressed by stimulation of muscarinic acetylcholine receptors. These channels are of particular interest because they are activated at the subthreshold membrane potentials. Furthermore, neural KCNQ channels are drug targets for the treatments of epilepsy and a variety of neurological disorders, including chronic and neuropathic pain, deafness, and mental illness. This review will update readers on the roles of KCNQ channels in the sensory system and neural circuits as well as discuss their respective mechanisms and the implications for physiology and medicine. We will also consider future perspectives and the development of additional pharmacological models, such as seizure, stroke, pain and mental illness, which work in combination with drug-design targeting of KCNQ channels. These models will hopefully deepen our understanding of KCNQ channels and provide general therapeutic prospects of related channelopathies.
Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K. Y. Michael; Zhou, Changsong
Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.
Full Text Available This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN and an modified Elman Neural Network (ENN for maximum power point tracking (MPPT. The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC I DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.
Green, Andrea M; Angelaki, Dora E
The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.
Zhang, Ruibin; Geng, Xiujuan; Lee, Tatia M C
An influential hypothesis from the last decade proposed that regions within the right inferior frontal cortex of the human brain were dedicated to supporting response inhibition. There is growing evidence, however, to support an alternative model, which proposes that neural areas associated with specific inhibitory control tasks co-exist as common network mechanisms, supporting diverse cognitive processes. This meta-analysis of 225 studies comprising 323 experiments examined the common and distinct neural correlates of cognitive processes for response inhibition, namely interference resolution, action withholding, and action cancellation. Activation coordinates for each subcategory were extracted using multilevel kernel density analysis (MKDA). The extracted activity patterns were then mapped onto the brain functional network atlas to derive the common (i.e., process-general) and distinct (i.e., domain-oriented) neural network correlates of these processes. Independent of the task types, activation of the right hemispheric regions (inferior frontal gyrus, insula, median cingulate, and paracingulate gyri) and superior parietal gyrus was common across the cognitive processes studied. Mapping the activation patterns to a brain functional network atlas revealed that the fronto-parietal and ventral attention networks were the core neural systems that were commonly engaged in different processes of response inhibition. Subtraction analyses elucidated the distinct neural substrates of interference resolution, action withholding, and action cancellation, revealing stronger activation in the ventral attention network for interference resolution than action inhibition. On the other hand, action withholding/cancellation primarily engaged the fronto-striatal circuit. Overall, our results suggest that response inhibition is a multidimensional cognitive process involving multiple neural regions and networks for coordinating optimal performance. This finding has significant
Mhd Saeed Sharif
Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.
Williams-Hayes, Peggy S.
The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.
Trischler, Adam P; D'Eleuterio, Gabriele M T
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.
Orfanidou, Eleni; Marslen-Wilson, William D; Davis, Matthew H
An important method for studying how the brain processes familiar stimuli is to present the same item on more than one occasion and measure how responses change with repetition. Here we use repetition priming in a sparse functional magnetic resonance imaging (fMRI) study to probe the neuroanatomical basis of spoken word recognition and the representations of spoken words that mediate repetition priming effects. Participants made lexical decisions to words and pseudowords spoken by a male or female voice that were presented twice, with half of the repetitions in a different voice. Behavioral and neural priming was observed for both words and pseudowords and was not affected by voice changes. The fMRI data revealed an elevated response to words compared to pseudowords in both posterior and anterior temporal regions, suggesting that both contribute to word recognition. Both reduced and elevated activation for second presentations (repetition suppression and enhancement) were observed in frontal and posterior regions. Correlations between behavioral priming and neural repetition suppression were observed in frontal regions, suggesting that repetition priming effects for spoken words reflect changes within systems involved in generating behavioral responses. Based on the current results, these processes are sufficiently abstract to display priming despite changes in the physical form of the stimulus and operate equivalently for words and pseudowords.
Zhang, Jing; Song, Long; Zhang, Zhipan; Chen, Nan; Qu, Liangti
Graphene materials have been attracting significant research interest in the past few years, with the recent focuses on graphene-based electronic devices and smart stimulus-responsive systems that have a certain degree of automatism. Owing to its huge specific surface area, large room-temperature electron mobility, excellent mechanical flexibility, exceptionally high thermal conductivity and environmental stability, graphene is identified as a beneficial additive or an effective responding component by itself to improve the conductivity, flexibility, mechanical strength and/or the overall responsive performance of smart systems. In this review article, we aim to present the recent advances in graphene systems that are of spontaneous responses to external stimulations, such as environmental variation in pH, temperature, electric current, light, moisture and even gas ambient. These smart stimulus-responsive graphene systems are believed to have great theoretical and practical interests to a wide range of device applications including actuators, switches, robots, sensors, drug/gene deliveries, etc. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Kaustubh Supekar; Anna G. Swigart; Caitlin Tenison; Dietsje D. Jolles; Miriam Rosenberg-Lee; Lynn Fuchs; Vinod Menon
... 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...
Guo, Yong-Feng; Xi, Bei; Wei, Fang; Tan, Jian-Guo
In this paper, the phenomenon of stochastic resonance in FitzHugh-Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.
Full Text Available We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed Kindchenschema or baby schema, and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It next reviews the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in both parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and then neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cutenessin infant faces with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses.Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relevant cues and a stronger activation in their brain reward circuitry. Perception of infant cuteness may also be influenced by reproductive hormones with the hypothalamic neuropeptide oxytocin being most strongly associated to date with increased attention andattractionto infant cues in both sexes.
Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin
Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.
Full Text Available How natural communication sounds are spatially represented across the inferior colliculus, the main center of convergence for auditory information in the midbrain, is not known. The neural representation of the acoustic stimuli results from the interplay of locally differing input and the organization of spectral and temporal neural preferences that change gradually across the nucleus. This raises the question how similar the neural representation of the communication sounds is across these gradients of neural preferences, and whether it also changes gradually. Analyzed neural recordings were multi-unit cluster spike trains from guinea pigs presented with a spectrotemporally rich set of eleven species-specific communication sounds. Using cross-correlation, we analyzed the response similarity of spiking activity across a broad frequency range for neurons of similar and different frequency tuning. Furthermore, we separated the contribution of the stimulus to the correlations to investigate whether similarity is only attributable to the stimulus, or, whether interactions exist between the multi-unit clusters that lead to neural correlations and whether these follow the same representation as the response correlations. We found that similarity of responses is dependent on the neurons' spatial distance for similarly and differently frequency-tuned neurons, and that similarity decreases gradually with spatial distance. Significant neural correlations exist, and contribute to the total response similarity. Our findings suggest that for multi-unit clusters in the mammalian inferior colliculus, the gradual response similarity with spatial distance to natural complex sounds is shaped by neural interactions and the gradual organization of neural preferences.
Khalighinejad, Bahar; Cruzatto da Silva, Guilherme; Mesgarani, Nima
Humans are unique in their ability to communicate using spoken language. However, it remains unclear how the speech signal is transformed and represented in the brain at different stages of the auditory pathway. In this study, we characterized electroencephalography responses to continuous speech by obtaining the time-locked responses to phoneme instances (phoneme-related potential). We showed that responses to different phoneme categories are organized by phonetic features. We found that each instance of a phoneme in continuous speech produces multiple distinguishable neural responses occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Comparing the patterns of phoneme similarity in the neural responses and the acoustic signals confirms a repetitive appearance of acoustic distinctions of phonemes in the neural data. Analysis of the phonetic and speaker information in neural activations revealed that different time intervals jointly encode the acoustic similarity of both phonetic and speaker categories. These findings provide evidence for a dynamic neural transformation of low-level speech features as they propagate along the auditory pathway, and form an empirical framework to study the representational changes in learning, attention, and speech disorders.SIGNIFICANCE STATEMENT We characterized the properties of evoked neural responses to phoneme instances in continuous speech. We show that each instance of a phoneme in continuous speech produces several observable neural responses at different times occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Each temporal event explicitly encodes the acoustic similarity of phonemes, and linguistic and nonlinguistic information are best represented at different time intervals. Finally, we show a joint encoding of phonetic and speaker information, where the neural representation of speakers is dependent on phoneme category. These findings provide compelling new evidence for
Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such
Mi, Yuanyuan; Lin, Xiaohan; Wu, Si
Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569
Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min
An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.
Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.
Phan, Mimi L.; Vicario, David S.
How do social interactions form and modulate the neural representations of specific complex signals? This question can be addressed in the songbird auditory system. Like humans, songbirds learn to vocalize by imitating tutors heard during development. These learned vocalizations are important in reproductive and social interactions and in individual recognition. As a model for the social reinforcement of particular songs, male zebra finches were trained to peck for a food reward in response to one song stimulus (GO) and to withhold responding for another (NoGO). After performance reached criterion, single and multiunit neural responses to both trained and novel stimuli were obtained from multiple electrodes inserted bilaterally into two songbird auditory processing areas [caudomedial mesopallium (CMM) and caudomedial nidopallium (NCM)] of awake, restrained birds. Neurons in these areas undergo stimulus-specific adaptation to repeated song stimuli, and responses to familiar stimuli adapt more slowly than to novel stimuli. The results show that auditory responses differed in NCM and CMM for trained (GO and NoGO) stimuli vs. novel song stimuli. When subjects were grouped by the number of training days required to reach criterion, fast learners showed larger neural responses and faster stimulus-specific adaptation to all stimuli than slow learners in both areas. Furthermore, responses in NCM of fast learners were more strongly left-lateralized than in slow learners. Thus auditory responses in these sensory areas not only encode stimulus familiarity, but also reflect behavioral reinforcement in our paradigm, and can potentially be modulated by social interactions. PMID:25475353
A. R Tahavvor
Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of
Marsden, Karen E; Ma, Wei Ji; Deci, Edward L; Ryan, Richard M; Chiu, Pearl H
The duration and quality of human performance depend on both intrinsic motivation and external incentives. However, little is known about the neuroscientific basis of this interplay between internal and external motivators. Here, we used functional magnetic resonance imaging to examine the neural substrates of intrinsic motivation, operationalized as the free-choice time spent on a task when this was not required, and tested the neural and behavioral effects of external reward on intrinsic motivation. We found that increased duration of free-choice time was predicted by generally diminished neural responses in regions associated with cognitive and affective regulation. By comparison, the possibility of additional reward improved task accuracy, and specifically increased neural and behavioral responses following errors. Those individuals with the smallest neural responses associated with intrinsic motivation exhibited the greatest error-related neural enhancement under the external contingency of possible reward. Together, these data suggest that human performance is guided by a "tonic" and "phasic" relationship between the neural substrates of intrinsic motivation (tonic) and the impact of external incentives (phasic).
Skoe, Erika; Krizman, Jennifer; Kraus, Nina
Despite the prevalence of poverty worldwide, little is known about how early socioeconomic adversity affects auditory brain function. Socioeconomically disadvantaged children are underexposed to linguistically and cognitively stimulating environments and overexposed to environmental toxins, including noise pollution. This kind of sensory impoverishment, we theorize, has extensive repercussions on how the brain processes sound. To characterize how this impoverishment affects auditory brain function, we compared two groups of normal-hearing human adolescents who attended the same schools and who were matched in age, sex, and ethnicity, but differed in their maternal education level, a correlate of socioeconomic status (SES). In addition to lower literacy levels and cognitive abilities, adolescents from lower maternal education backgrounds were found to have noisier neural activity than their classmates, as reflected by greater activity in the absence of auditory stimulation. Additionally, in the lower maternal education group, the neural response to speech was more erratic over repeated stimulation, with lower fidelity to the input signal. These weaker, more variable, and noisier responses are suggestive of an inefficient auditory system. By studying SES within a neuroscientific framework, we have the potential to expand our understanding of how experience molds the brain, in addition to informing intervention research aimed at closing the achievement gap between high-SES and low-SES children.
van Rooij, Sanne J H; Geuze, Elbert; Kennis, Mitzy; Rademaker, Arthur R|info:eu-repo/dai/nl/304836427; Vink, Matthijs
Thirty to fifty percent of posttraumatic stress disorder (PTSD) patients do not respond to treatment. Understanding the neural mechanisms underlying treatment response could contribute to improve response rates. PTSD is often associated with decreased inhibition of fear responses in a safe
Sylvester, Jared; Reggia, James
There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
Kim, Pilyoung; Capistrano, Christian G; Erhart, Andrew; Gray-Schiff, Rachel; Xu, Nanxi
During the early postpartum period, mothers exhibit increased amygdala responses to positive infant expressions, which are important for positive mother-infant relationships. Socioeconomic disadvantage is associated with altered amygdala response to emotional stimuli as well as more negative mother-infant relationships. However, little is known about the role of socioeconomic disadvantage in neural responses specifically to infants. Thus, we examined whether socioeconomic disadvantage (indexed by lower income-to-needs ratio) is associated with neural responses to infant emotions and parenting behaviors among new mothers. Using fMRI, neural responses to infants' emotional expressions (positive, negative, and neutral faces) were assessed among 39 low- and middle-income first-time mothers during 0-6 postpartum months. Lower income-to-needs ratio was associated with dampened amygdala responses to positive infant faces, but increased amygdala responses to negative infant faces. An indirect effect of socioeconomic disadvantage on emotional availability via amygdala activation suggests that socioeconomic disadvantage is associated with heightened neural sensitivity to infants' negative emotions, which is further associated with mothers' intrusiveness observed during interactions with their own infant. The findings suggest that low-income mothers may be more vulnerable to altered neural processing of infants' emotional expressions which may further influence mothers' emotional availability during interactions with their own infants. Copyright © 2017 Elsevier B.V. All rights reserved.
Full Text Available Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics, the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding. A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.
Singh, H P; Sukavanam, N
This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Y. D. Song
Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.
von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime
"Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.
Fang, Yanshan; Soares, Lorena; Bonini, Nancy M
Live-imaging technology has markedly advanced in the field of neural injury and axon degeneration; however, studies are still predominantly performed in in vitro settings such as cultured neuronal cells or in model organisms such as Caenorhabditis elegans in which axons lack glial wrappings. We recently developed a new in vivo model for adult-stage neural injury in Drosophila melanogaster, using the highly accessible wing of the animal. Because the Drosophila wing is translucent and dispensable for survival, it allows clear and direct visualization of injury-induced progressive responses of axons and glia highlighted by fluorescent protein (FP) markers in live animals over time. Moreover, unlike previous Drosophila models of neural injury, this procedure does not require dissection of the CNS. Thus, the key preparation steps for in vivo imaging of the neural injury response described in this protocol can be completed within 30 min.
Tkačik, Gašper; Granot-Atedgi, Einat; Segev, Ronen; Schneidman, Elad
The ability of an organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this "neural metric" tells us how distinguishable a pair of stimulus clips is to the retina, based on the similarity between the induced distributions of population responses. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the support-vector-machine-like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.
is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...
Kelkar, Atul G.
Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.
Anastassiou, George A
This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries. .
Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo
A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.
Bell, Brittany A.; Phan, Mimi L.; Vicario, David S.
How do social interactions form and modulate the neural representations of specific complex signals? This question can be addressed in the songbird auditory system. Like humans, songbirds learn to vocalize by imitating tutors heard during development. These learned vocalizations are important in reproductive and social interactions and in individual recognition. As a model for the social reinforcement of particular songs, male zebra finches were trained to peck for a food reward in response t...
Wang, Min; Song, Yongji; Suen, Jiantao; Zhao, Yiliang; Jia, Aibin; Zhu, Jianping
Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.
Jouffroy, Guillaume; Jouffroy, Jerome
model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.
Winklewski, Pawel J; Radkowski, Marek; Wszedybyl-Winklewska, Magdalena; Demkow, Urszula
Locus coeruleus is a critical component of the brain noradrenergic system. The brain noradrenergic system provides the neural substrate for the architecture supporting the interaction with, and navigation through, an external world complexity. Changes in locus coeruleus tonic and phasic activity and the interplay between norepinephrine and α1- and α2-adrenoceptors in the prefrontal cortex are the key elements of this sophisticated architecture. In this narrative review we discuss how the brain noradrenergic system is affected by increased exposure to corticotropin-releasing hormone triggered by stress response. In particular, we present the mechanisms responsible for thinking inflexibility often observed under highly stressful conditions. Finally, the main directions for future research are highlighted.
Todd Vollmer; Ondrej Linda; Milos Manic
Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.
Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin
A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.
Flowers, D. A.; Ayres, V. M.; Delgado-Rivera, R.; Ahmed, I.; Meiners, S. A.
Preliminary data from in-vivo investigations (rat model) suggest that a nanofiber prosthetic device of fibroblast growth factor-2 (FGF-2)-modified nanofibers can correctly guide regenerating axons across an injury gap with aligned functional recovery. Scanning Probe Recognition Microscopy (SPRM) with auto-tracking of individual nanofibers is used for investigation of the key nanoscale properties of the nanofiber prosthetic device for central nervous system tissue engineering and repair. The key properties under SPRM investigation include nanofiber stiffness and surface roughness, nanofiber curvature, nanofiber mesh density and porosity, and growth factor presentation and distribution. Each of these factors has been demonstrated to have global effects on cell morphology, function, proliferation, morphogenesis, migration, and differentiation. The effect of FGF-2 modification on the key nanoscale properties is investigated. Results from the nanofiber prosthetic properties investigations are correlated with astrocyte response to unmodified and FGF-2 modified scaffolds, using 2D planar substrates as a control.
Wang, Chao; Trongnetrpunya, Amy; Samuel, Immanuel Babu Henry; Ding, Mingzhou; Kluger, Benzi M
Prolonged continuous performance of a cognitively demanding task induces cognitive fatigue and is associated with a time-related deterioration of objective performance, the degree of which is referred to cognitive fatigability. Although the neural underpinnings of cognitive fatigue are poorly understood, prior studies report changes in neural activity consistent with deterioration of task-related networks over time. While compensatory brain activity is reported to maintain motor task performance in the face of motor fatigue and cognitive performance in the face of other stressors (e.g., aging) and structural changes, there are no studies to date demonstrating compensatory activity for cognitive fatigue. High-density electroencephalography was recorded from human subjects during a 160 min continuous performance of a cognitive control task. While most time-varying neural activity showed a linear decline over time, we identified an evoked potential over the anterior frontal region which demonstrated an inverted U-shaped time-on-task profile. This evoked brain activity peaked between 60 and 100 min into the task and was positively associated with better behavioral performance only during this interval. Following the peak and during subsequent decline of this anterior frontal activity, the rate of performance decline also accelerated. These findings demonstrate that this anterior frontal brain activity, which is not part of the primary task-related activity at baseline, is recruited to compensate for fatigue-induced impairments in the primary task-related network, and that this compensation terminates as cognitive fatigue further progresses. These findings may be relevant to understanding individual differences in cognitive fatigability and developing interventions for clinical conditions afflicted by fatigue. Fatigue refers to changes in objective performance and subjective effort induced by continuous task performance. We examined the neural underpinnings of cognitive
Pairan, M. F.; Shamsudin, S. S.
This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.
Tang, Yin; Wang, Fei
The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.
Benjamin, A.; Altman, B.; O`Gorman, C.; Rodeman, R.; Paez, T.L.
Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the space where they were trained.They do this by interpolating specified inputs among the training inputs to yield outputs that are interpolations of =Ming outputs. The reason for using ANNs for the simulation of system response is that they provide accurate approximations of system behavior and are typically much more efficient than phenomenological models. This efficiency is very important in situations where multiple response computations are required, as in, for example, Monte Carlo analysis of probabilistic system response. This paper describes two frameworks in which we have used ANNs to good advantage in the approximate simulation of the behavior of physical system response. These frameworks are the non-recurrent and recurrent frameworks. It is assumed in these applications that physical experiments have been performed to obtain data characterizing the behavior of a system, or that an accurate finite element model has been run to establish system response. The paper provides brief discussions on the operation of ANNs, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented to demonstrate system simulation with ANNs.
Hinaut, Xavier; Lance, Florian; Droin, Colas; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford
Language production requires selection of the appropriate sentence structure to accommodate the communication goal of the speaker - the transmission of a particular meaning. Here we consider event meanings, in terms of predicates and thematic roles, and we address the problem that a given event can be described from multiple perspectives, which poses a problem of response selection. We present a model of response selection in sentence production that is inspired by the primate corticostriatal system. The model is implemented in the context of reservoir computing where the reservoir - a recurrent neural network with fixed connections - corresponds to cortex, and the readout corresponds to the striatum. We demonstrate robust learning, and generalization properties of the model, and demonstrate its cross linguistic capabilities in English and Japanese. The results contribute to the argument that the corticostriatal system plays a role in response selection in language production, and to the stance that reservoir computing is a valid potential model of corticostriatal processing. Copyright © 2015 Elsevier Inc. All rights reserved.
Chaves, Paulo; Chang, Fi-John
We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.
Moroz, Leonid L; Kocot, Kevin M; Citarella, Mathew R; Dosung, Sohn; Norekian, Tigran P; Povolotskaya, Inna S; Grigorenko, Anastasia P; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V; Jurka, Jerzy; Bobkov, Yuri V; Swore, Joshua J; Girardo, David O; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E; Rast, Jonathan P; Derelle, Romain; Solovyev, Victor V; Kondrashov, Fyodor A; Swalla, Billie J; Sweedler, Jonathan V; Rogaev, Evgeny I; Halanych, Kenneth M; Kohn, Andrea B
The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.
Krok, A.; Waszczyszyn, Z. [Cracow University of Technology, Krakow (Poland)
Acceleration response spectra (ARS) for mining tremors in the Upper Silesian Coalfield, Poland are generated using neural networks trained by means of Kalman filtering. The target ARS were computed on the base of measured accelerograms. It was proved that the standard feed-forward, layered neural network, trained by the DEFK (decoupled extended Kalman filter) algorithm is numerically much less efficient than the standard recurrent NN learnt by Recurrent DEKF, cf. (Haykin S, (editor). Kalman filtering and neural networks. New York: John Wiley & Sons; 2001). It is also shown that the studied KF algorithms are better than the traditional Resilient-Propagation learning method. The improvement of the training process and neural prediction due to introduction of an autoregressive input is also discussed in the paper.
Full Text Available Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.
Murao, Ema; Sugihara, Genichi; Isobe, Masanori; Noda, Tomomi; Kawabata, Michiko; Matsukawa, Noriko; Takahashi, Hidehiko; Murai, Toshiya; Noma, Shun'ichi
Anorexia nervosa (AN) includes the restricting (AN-r) and binge-eating/purging (AN-bp) subtypes, which have been reported to differ regarding their underlying pathophysiologies as well as their behavioral patterns. However, the differences in neural mechanisms of reward systems between AN subtypes remain unclear. The aim of the present study was to explore differences in the neural processing of reward and punishment between AN subtypes. Twenty-three female patients with AN (11 AN-r and 12 AN-bp) and 20 healthy women underwent functional magnetic resonance imaging while performing a monetary incentive delay task. Whole-brain one-way analysis of variance was conducted to test between-group differences. There were significant group differences in brain activation in the rostral anterior cingulate cortex and right posterior insula during loss anticipation, with increased brain activation in the AN-bp group relative to the AN-r and healthy women groups. No significant differences were found during gain anticipation. AN-bp patients showed altered neural responses to punishment in brain regions implicated in emotional arousal. Our findings suggest that individuals with AN-bp are more sensitive to potential punishment than individuals with AN-r and healthy individuals at the neural level. The present study provides preliminary evidence that there are neurobiological differences between AN subtypes with regard to the reward system, especially punishment processing. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.
Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.
McClure, Samuel M; Laibson, David I; Loewenstein, George; Cohen, Jonathan D
When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.
Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine
Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.
Madipakkam, Apoorva Rajiv; Rothkirch, Marcus; Guggenmos, Matthias; Heinz, Andreas; Sterzer, Philipp
Gaze direction and especially direct gaze is a powerful nonverbal cue that plays an important role in social interactions. Here we studied the neural mechanisms underlying the privileged access of direct gaze to visual awareness. We performed functional magnetic resonance imaging in healthy human volunteers who were exposed to faces with direct or averted gaze under continuous flash suppression, thereby manipulating their awareness of the faces. A gaze processing network comprising fusiform face area (FFA), superior temporal sulcus, amygdala, and intraparietal sulcus showed overall reduced neural responses when participants reported to be unaware of the faces. Interestingly, direct gaze elicited greater responses than averted gaze when participants were aware of the faces, but smaller responses when they were unaware. Additional between-subject correlation and single-trial analyses indicated that this pattern of results was due to a modulation of the relationship between neural responses and awareness by gaze direction: with increasing neural activation in the FFA, direct-gaze faces entered awareness more readily than averted-gaze faces. These findings suggest that for direct gaze, lower levels of neural activity are sufficient to give rise to awareness than for averted gaze, thus providing a neural basis for privileged access of direct gaze to awareness. Significance statement: Another person's eye gaze directed at oneself is a powerful social signal acting as a catalyst for further communication. Here, we studied the neural mechanisms underlying the prioritized access of direct gaze to visual awareness in healthy human volunteers and show that with increasing neural activation, direct-gaze faces enter awareness more readily than averted-gaze faces. This suggests that for a socially highly relevant cue like direct gaze, lower levels of neural activity are sufficient to give rise to awareness compared with averted gaze, possibly because the human brain is attuned
Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system
Cascio Carissa J
Full Text Available Abstract Background One hypothesis for the social deficits that characterize autism spectrum disorders (ASD is diminished neural reward response to social interaction and attachment. Prior research using established monetary reward paradigms as a test of non-social reward to compare with social reward may involve confounds in the ability of individuals with ASD to utilize symbolic representation of money and the abstraction required to interpret monetary gains. Thus, a useful addition to our understanding of neural reward circuitry in ASD includes a characterization of the neural response to primary rewards. Method We asked 17 children with ASD and 18 children without ASD to abstain from eating for at least four hours before an MRI scan in which they viewed images of high-calorie foods. We assessed the neural reward network for increases in the blood oxygenation level dependent (BOLD signal in response to the food images Results We found very similar patterns of increased BOLD signal to these images in the two groups; both groups showed increased BOLD signal in the bilateral amygdala, as well as in the nucleus accumbens, orbitofrontal cortex, and insula. Direct group comparisons revealed that the ASD group showed a stronger response to food cues in bilateral insula along the anterior-posterior gradient and in the anterior cingulate cortex than the control group, whereas there were no neural reward regions that showed higher activation for controls than for ASD. Conclusion These results suggest that neural response to primary rewards is not diminished but in fact shows an aberrant enhancement in children with ASD.
Unhjem, Runar; Lundestad, Raymond; Fimland, Marius Steiro; Mosti, Mats Peder; Wang, Eivind
Although reductions in resting H-reflex responses and maximal firing frequency suggest that reduced efferent drive may limit muscle strength in elderly, there are currently no reports of V-wave measurements in elderly, reflecting the magnitude of efferent output to the muscle during maximal contraction. Furthermore, it is uncertain whether potential age-related neural deficiencies can be restored by resistance training. We assessed evoked reflex recordings in the triceps surae muscles during rest and maximal voluntary contraction (MVC), rate of force development (RFD), and muscle mass in seven elderly (74 ± 6 years) males before and after 8 weeks of heavy resistance training, contrasted by seven young (24 ± 4 years) male controls. At baseline, m. soleus (SOL) V/M ratio (0.124 ± 0.082 vs. 0.465 ± 0.197, p elderly compared to young. Also, SOL H-reflex latency (33.29 ± 2.41 vs. 30.29 ± 0.67 ms, p elderly. The reduced neural drive was, despite similar leg muscle mass (10.7 ± 1.2 vs. 11.5 ± 1.4 kg), mirrored by lower MVC (158 ± 48 vs. 240 ± 54 Nm, p elderly. In response to training SOL V/M ratio (0.184 ± 0.092, p elderly, yet only to a level ~40 % of the young. This was accompanied by increased MVC (190 ± 70 Nm, p muscle strength. Furthermore, this motor system impairment can to some extent be improved by heavy resistance training.
Gilman, Jodi M; Ramchandani, Vijay A.; Crouss, Tess; Hommer, Daniel W.
Heavy alcohol consumption during young adulthood is a risk factor for the development of serious alcohol use disorders. Research has shown that individual differences in subjective responses to alcohol may affect individuals' vulnerability to developing alcoholism. Studies comparing the subjective and objective response to alcohol between light and heavy drinkers (HDs), however, have yielded inconsistent results, and neural responses to alcohol in these groups have not been characterized. We ...
Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.
A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus. PMID:27271802
Kang, Min-Joo; Kang, Je-Won
A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.
Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.
Mascaro, Jennifer S.; Hackett, Patrick D.; Rilling, James K.
Despite the well-documented importance of paternal caregiving for positive child development, little is known about the neural changes that accompany the transition to fatherhood in humans, or about how changes in hormone levels affect paternal brain function. We compared fathers of children aged 1–2 with non-fathers in terms of hormone levels (oxytocin and testosterone), neural responses to child picture stimuli, and neural responses to visual sexual stimuli. Compared to non-fathers, fathers...
National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...
Rodney A. Swain
Full Text Available In its strictest application, the term reinforcement learning refers to a computational approach to learning in which an agent (often a machine interacts with a mutable environment to maximize reward through trial and error. The approach borrows essentials from several fields, most notably Computer Science, Behavioral Neuroscience, and Psychology. At the most basic level, a neural system capable of mediating reinforcement learning must be able to acquire sensory information about the external environment and internal milieu (either directly or through connectivities with other brain regions, must be able to select a behavior to be executed, and must be capable of providing evaluative feedback about the success of that behavior. Given that Psychology informs us that reinforcers, both positive and negative, are stimuli or consequences that increase the probability that the immediately antecedent behavior will be repeated and that reinforcer strength or viability is modulated by the organism’s past experience with the reinforcer, its affect, and even the state of its muscles (e.g., eyes open or closed; it is the case that any neural system that supports reinforcement learning must also be sensitive to these same considerations. Once learning is established, such a neural system must finally be able to maintain continued response expression and prevent response drift. In this report, we examine both historical and recent evidence that the cerebellum satisfies all of these requirements. While we report evidence from a variety of learning paradigms, the majority of our discussion will focus on classical conditioning of the rabbit eye blink response as an ideal model system for the study of reinforcement and reinforcement learning.
Cascio, Christopher N; Carp, Joshua; O'Donnell, Matthew Brook; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G; Falk, Emily B
Adolescence is a period characterized by increased sensitivity to social cues, as well as increased risk-taking in the presence of peers. For example, automobile crashes are the leading cause of death for adolescents, and driving with peers increases the risk of a fatal crash. Growing evidence points to an interaction between neural systems implicated in cognitive control and social and emotional context in predicting adolescent risk. We tested such a relationship in recently licensed teen drivers. Participants completed an fMRI session in which neural activity was measured during a response inhibition task, followed by a separate driving simulator session 1 week later. Participants drove alone and with a peer who was randomly assigned to express risk-promoting or risk-averse social norms. The experimentally manipulated social context during the simulated drive moderated the relationship between individual differences in neural activity in the hypothesized cognitive control network (right inferior frontal gyrus, BG) and risk-taking in the driving context a week later. Increased activity in the response inhibition network was not associated with risk-taking in the presence of a risky peer but was significantly predictive of safer driving in the presence of a cautious peer, above and beyond self-reported susceptibility to peer pressure. Individual differences in recruitment of the response inhibition network may allow those with stronger inhibitory control to override risky tendencies when in the presence of cautious peers. This relationship between social context and individual differences in brain function expands our understanding of neural systems involved in top-down cognitive control during adolescent development.
Sproule, Michael K J; Chacron, Maurice J
Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.
Li, Jian; Delgado, Mauricio R; Phelps, Elizabeth A
Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.
Sobhani-Tehrani, E; Talebi, H A; Khorasani, K
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.
Youngentob, Steven L; Kent, Paul F; Youngentob, Lisa M
The association between gestational exposure to ethanol and adolescent ethanol abuse is well established. Recent animal studies support the role of fetal ethanol experience-induced chemosensory plasticity as contributing to this observation. Previously, we established that fetal ethanol exposure, delivered through a dam's diet throughout gestation, tuned the neural response of the peripheral olfactory system of early postnatal rats to the odor of ethanol. This occurred in conjunction with a loss of responsiveness to other odorants. The instinctive behavioral response to the odor of ethanol was also enhanced. Importantly, there was a significant contributory link between the altered response to the odor of ethanol and increased ethanol avidity when assessed in the same animals. Here, we tested whether the neural and behavioral olfactory plasticity, and their relationship to enhanced ethanol intake, is a result of the mere exposure to ethanol or whether it requires the animal to associate ethanol's reinforcing properties with its odor attributes. In this later respect, the opioid system is important in the mediation (or modulation) of the reinforcing aspects of ethanol. To block endogenous opiates during prenatal life, pregnant rats received daily intraperitoneal administration of the opiate antagonist naltrexone from gestational day 6-21 jointly with ethanol delivered via diet. Relative to control progeny, we found that gestational exposure to naltrexone ameliorated the enhanced postnatal behavioral response to the odor of ethanol and postnatal drug avidity. Our findings support the proposition that in utero ethanol-induced olfactory plasticity (and its relationship to postnatal intake) requires, at least in part, the associative pairing between ethanol's odor quality and its reinforcing aspects. We also found suggestive evidence that fetal naltrexone ameliorated the untoward effects of gestational ethanol exposure on the neural response to non
Yeo, S.; Rosen, B.; Bosch, M.P.C.; Noort, M.W.M.L. van den; Lim, S.
Objective: To examine gender differences and similarities in the psychophysical and brain responses to acupuncture at GB34, a point that is frequently used to treat motor function issues in Traditional Chinese Medicine. Methods: Functional MRI (fMRI) was used to measure brain activation in response
Gregory, Jeffrey A; Borna, Amir; Roy, Sabyasachi; Wang, Xiaoqin; Lewandowski, Brian; Schmidt, Marc; Najafi, Khalil
We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25microV(rms) in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented.
Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.
Justin P Kinney
Full Text Available Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the data acquisition process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.
Full Text Available Functional MRI analyses commonly rely on the assumption that the temporal dynamics of hemodynamic response functions (HRFs are independent of the amplitude of the neural signals that give rise to them. The validity of this assumption is particularly important for techniques that use fMRI to resolve sub-second timing distinctions between responses, in order to make inferences about the ordering of neural processes. Whether or not the detailed shape of the HRF is independent of neural response amplitude remains an open question, however. We performed experiments in which we measured responses in primary visual cortex (V1 to large, contrast-reversing checkerboards at a range of contrast levels, which should produce varying amounts of neural activity. Ten subjects (ages 22-52 were studied in each of two experiments using 3 Tesla scanners. We used rapid, 250 msec, temporal sampling (repetition time, or TR and both short and long inter-stimulus interval (ISI stimulus presentations. We tested for a systematic relationship between the onset of the HRF and its amplitude across conditions, and found a strong negative correlation between the two measures when stimuli were separated in time (long- and medium-ISI experiments, but not the short-ISI experiment. Thus, stimuli that produce larger neural responses, as indexed by HRF amplitude, also produced HRFs with shorter onsets. The relationship between amplitude and latency was strongest in voxels with lowest mean-normalized variance (i.e., parenchymal voxels. The onset differences observed in the longer-ISI experiments are likely attributable to mechanisms of neurovascular coupling, since they are substantially larger than reported differences in the onset of action potentials in V1 as a function of response amplitude.
Bell, Brittany A; Phan, Mimi L; Vicario, David S
How do social interactions form and modulate the neural representations of specific complex signals? This question can be addressed in the songbird auditory system. Like humans, songbirds learn to vocalize by imitating tutors heard during development. These learned vocalizations are important in reproductive and social interactions and in individual recognition. As a model for the social reinforcement of particular songs, male zebra finches were trained to peck for a food reward in response to one song stimulus (GO) and to withhold responding for another (NoGO). After performance reached criterion, single and multiunit neural responses to both trained and novel stimuli were obtained from multiple electrodes inserted bilaterally into two songbird auditory processing areas [caudomedial mesopallium (CMM) and caudomedial nidopallium (NCM)] of awake, restrained birds. Neurons in these areas undergo stimulus-specific adaptation to repeated song stimuli, and responses to familiar stimuli adapt more slowly than to novel stimuli. The results show that auditory responses differed in NCM and CMM for trained (GO and NoGO) stimuli vs. novel song stimuli. When subjects were grouped by the number of training days required to reach criterion, fast learners showed larger neural responses and faster stimulus-specific adaptation to all stimuli than slow learners in both areas. Furthermore, responses in NCM of fast learners were more strongly left-lateralized than in slow learners. Thus auditory responses in these sensory areas not only encode stimulus familiarity, but also reflect behavioral reinforcement in our paradigm, and can potentially be modulated by social interactions. Copyright © 2015 the American Physiological Society.
Lau, Jennifer Y. F.; Guyer, Amanda E.; Tone, Erin B.; Jenness, Jessica; Parrish, Jessica M.; Pine, Daniel S.; Nelson, Eric E.
Peer rejection powerfully predicts adolescent anxiety. While cognitive differences influence anxious responses to social feedback, little is known about neural contributions. Twelve anxious and twelve age-, gender- and IQ-matched, psychiatrically healthy adolescents received "not interested" and "interested" feedback from unknown peers during a…
Metting, H.J; Coenegracht, P.M J
The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method
Will, G.J.; Van, Lier P.A.; Crone, E.A.; Guroglu, B.
This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12-15) who either had a stable accepted (n = 27; 14 males) or a chronic rejected (n = 19; 12 males) status among peers from age 6 to 12. Both groups of adolescents
Caseras, X.; Mataix-Cols, D.; An, S.K.; Lawrence, N.S.; Speckens, A.E.M.; Giampietro, V.; Brammer, M.J.; Phillips, M.L.
BACKGROUND: A majority of patients with disgust-related psychiatric disorders such as animal phobias and contamination-related obsessive-compulsive disorder are women. The aim of this functional magnetic resonance imaging (fMRI) study was to examine possible sex differences in neural responses to
Masten, Carrie L.; Eisenberger, Naomi I.; Pfeifer, Jennifer H.; Colich, Natalie L.; Dapretto, Mirella
Links among concurrent and longitudinal changes in pubertal development and empathic ability from ages 10 to 13 and neural responses while witnessing peer rejection at age 13 were examined in 16 participants. More advanced pubertal development at age 13, and greater longitudinal increases in pubertal development, related to increased activity in…
Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong
This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
Vaitsekhovich, L.; Golovko, V; Rubanau, V.
In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP. The KDD-99 dataset was used for experiments performing. The experimental results show that such int...
Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.
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
Icenhour, A; Labrenz, F; Ritter, C; Theysohn, N; Forsting, M; Bingel, U; Elsenbruch, S
Studies investigating mechanisms underlying nocebo responses in pain have mainly focused on negative expectations induced by verbal suggestions. Herein, we addressed neural and behavioral correlates of nocebo responses induced by classical conditioning in a visceral pain model. In two independent studies, a total of 40 healthy volunteers underwent classical conditioning, consisting of repeated pairings of one visual cue (CS High ) with rectal distensions of high intensity, while a second cue (CS Low ) was always followed by low-intensity distensions. During subsequent test, only low-intensity distensions were delivered, preceded by either CS High or CS Low . Distension intensity ratings were assessed in both samples and functional magnetic resonance imaging data were available from one study (N=16). As a consequence of conditioning, we hypothesized CS High -cued distensions to be perceived as more intense and expected enhanced cue- and distension-related neural responses in regions encoding sensory and affective dimensions of pain and in structures associated with pain-related fear memory. During test, distension intensity ratings did not differ depending on preceding cue. Greater distension-induced neural activation was observed in somatosensory, prefrontal, and cingulate cortices and caudate when preceded by CS High . Analysis of cue-related responses revealed strikingly similar activation patterns. We report changes in neural activation patterns during anticipation and visceral stimulation induced by prior conditioning. In the absence of behavioral effects, markedly altered neural responses may indicate conditioning with visceral signals to induce hypervigilance rather than hyperalgesia, involving altered attention, reappraisal, and perceptual acuity as processes contributing to the pathophysiology of visceral pain. © 2017 John Wiley & Sons Ltd.
Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.
Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.
Jagiellowicz, Jadzia; Xu, Xiaomeng; Aron, Arthur; Aron, Elaine; Cao, Guikang; Feng, Tingyong; Weng, Xuchu
This exploratory study examined the extent to which individual differences in sensory processing sensitivity (SPS), a temperament/personality trait characterized by social, emotional and physical sensitivity, are associated with neural response in visual areas in response to subtle changes in visual scenes. Sixteen participants completed the Highly Sensitive Person questionnaire, a standard measure of SPS. Subsequently, they were tested on a change detection task while undergoing functional m...
Sekiguchi, Atsushi; Sugiura, Motoaki; Yokoyama, Satoru; Sassa, Yuko; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta
Background Frustrating situations are encountered daily, and it is necessary to respond in an adaptive fashion. A psychological definition states that adaptive social behaviors are ?self-performing? and ?contain a solution.? The present study investigated the neural correlates of adaptive social responses to frustrating situations by assessing the dimension of causal attribution. Based on attribution theory, internal causality refers to one?s aptitudes that cause natural responses in real-lif...
Lillian eMay; Krista eByers-Heinlein; Judit eGervain; Werker, Janet F.
Previous research has shown that by the time of birth, the neonate brain responds specially to the native language when compared to acoustically similar non-language stimuli. In the current study, we use Near Infrared Spectroscopy to ask how prenatal language experience might shape the brain response to language in newborn infants. To do so, we examine the neural response of neonates when listening to familiar versus unfamiliar language, as well as to non-linguistic backwards language. Twenty...
Alarcón, Gabriela; Cservenka, Anita; Nagel, Bonnie J
Risky decision making is prominent during adolescence, perhaps contributed to by heightened sensation seeking and ongoing maturation of reward and dopamine systems in the brain, which are, in part, modulated by sex hormones. In this study, we examined sex differences in the neural substrates of reward sensitivity during a risky decision-making task and hypothesized that compared with girls, boys would show heightened brain activation in reward-relevant regions, particularly the nucleus accumbens, during reward receipt. Further, we hypothesized that testosterone and estradiol levels would mediate this sex difference. Moreover, we predicted boys would make more risky choices on the task. While boys showed increased nucleus accumbens blood oxygen level-dependent (BOLD) response relative to girls, sex hormones did not mediate this effect. As predicted, boys made a higher percentage of risky decisions during the task. Interestingly, boys also self-reported more motivation to perform well and earn money on the task, while girls self-reported higher state anxiety prior to the scan session. Motivation to earn money partially mediated the effect of sex on nucleus accumbens activity during reward. Previous research shows that increased motivation and salience of reinforcers is linked with more robust striatal BOLD response, therefore psychosocial factors, in addition to sex, may play an important role in reward sensitivity. Elucidating neurobiological mechanisms that support adolescent sex differences in risky decision making has important implications for understanding individual differences that lead to advantageous and adverse behaviors that affect health outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.
Hamilton, Lisa Dawn; Meston, Cindy M
In non-human animal research, studies comparing socially monogamous and promiscuous species of voles (Microtus) have identified some key neural differences related to monogamy and non-monogamy. Specifically, densities of the vasopressin V1a receptor and dopamine D2 receptors in subcortical reward-related and limbic areas of the brain have been linked to monogamous behavior in prairie voles (Microtus ochrogaster). Similar brain areas have been shown to be correlated with feelings of romantic love in monogamously pair-bonded humans. Humans vary in the degree to which they engage in (non-)monogamous behaviors. The present study examined the differences in neural activation in response to sexual and romantic stimuli in monogamous (n = 10) and non-monogamous (n = 10) men. Results indicated that monogamous men showed more reward-related neural activity when viewing romantic pictures compared to non-monogamous men. Areas with increased activation for monogamous men were all in the right hemisphere and included the thalamus, accumbens, striatum, pallidum, insula, and orbitofrontal cortex. There were no significant differences between groups in activation to sexual stimuli. These results demonstrate that the neural processing of romantic images is different for monogamous and non-monogamous men. There is some overlap in the neural areas showing increased activation in monogamous men in the present study and the neural areas that show differences in the vole models of monogamy and affiliation. Future research will be needed to clarify whether similar factors are contributing to the neural differences seen in monogamous and non-monogamous humans and voles.
Mathur, Vani A; Cheon, Bobby K; Harada, Tokiko; Scimeca, Jason M; Chiao, Joan Y
Interpersonal pain perception is a fundamental and evolutionarily beneficial social process. While critical for navigating the social world, whether or not people rely on similar processes to perceive and respond to the harm of the non-human biological world remains largely unknown. Here we investigate whether neural reactivity toward the suffering of other people is distinct from or overlapping with the neural response to pain and harm inflicted upon non-human entities, specifically animals and nature. We used fMRI to measure neural activity while participants (n=15) perceived and reported how badly they felt for the pain or harm of humans, animals, and nature, relative to neutral situations. Neural regions associated with perceiving the pain of other people (e.g. dorsal anterior cingulate cortex, bilateral anterior insula) were similarly recruited when perceiving and responding to painful scenes across people, animals, and nature. These results suggest that similar brain responses are relied upon when perceiving the harm of social and non-social biological entities, broadly construed, and that activity within the dorsal anterior cingulate cortex and bilateral anterior insula in response to pain-relevant stimuli is not uniquely specific to humans. Copyright © 2016 Elsevier Ltd. All rights reserved.
Yeo, Sujung; Lim, Sabina; Choe, Il-Hwan; Choi, Yeong-Gon; Chung, Kyung-Cheon; Jahng, Geon-Ho; Kim, Sung-Hoon
Parkinson's disease (PD) is a degenerative brain disorder that is caused by neural defects in the substantia nigra. Numerous studies have reported that acupuncture treatment on GB34 (Yanglingquan) leads to significant improvements in patients with PD and in PD animal models. Studies using functional magnetic resonance imaging (fMRI) have shown that patients with PD, compared to healthy participants, have lower neural responses in extensive brain regions including the putamen, thalamus, and the supplementary motor area. This study investigated the reported association between acupuncture point GB34 and PD. Using fMRI, neural responses of 12 patients with PD and 12 healthy participants were examined before and after acupuncture stimulation. Acupuncture stimulation increased neural responses in regions including the substantia nigra, caudate, thalamus, and putamen, which are impaired caused by PD. Areas associated with PD were activated by the acupuncture stimulation on GB34. This shows that acupuncture treatment on GB34 may be effective in improving the symptoms of PD. Although more randomized controlled trials on the topic will be needed, this study shows that acupuncture may be helpful in the treatment of symptoms involving PD. © 2012 Blackwell Publishing Ltd.
Li, Xiaoyang; Liu, Yi; Luo, Siyang; Wu, Bing; Wu, Xinhuai; Han, Shihui
Behavioral research suggests that mortality salience (MS) leads to increased in-group identification and in-group favoritism in prosocial behavior. What remains unknown is whether and how MS influences brain activity that mediates emotional resonance with in-group and out-group members and is associated with in-group favoritism in helping behavior. The current work investigated MS effects on empathic neural responses to racial in-group and out-group members' suffering. Experiments 1 and 2 respectively recorded event related potentials (ERPs) and blood oxygen level dependent signals to pain/neutral expressions of Asian and Caucasian faces from Chinese adults who had been primed with MS or negative affect (NA). Experiment 1 found that an early frontal/central activity (P2) was more strongly modulated by pain vs. neutral expressions of Asian than Caucasian faces, but this effect was not affected by MS vs. NA priming. However, MS relative to NA priming enhanced racial in-group bias in long-latency neural response to pain expressions over the central/parietal regions (P3). Experiment 2 found that MS vs. NA priming increased racial in-group bias in empathic neural responses to pain expression in the anterior and mid-cingulate cortex. Our findings indicate that reminding mortality enhances brain activity that differentiates between racial in-group and out-group members' emotional states and suggest a neural basis of in-group favoritism under mortality threat. Copyright © 2015 Elsevier Inc. All rights reserved.
Cowdrey, Felicity A; Harmer, Catherine J; Park, Rebecca J; McCabe, Ciara
Impairments in emotional processing have been associated with anorexia nervosa. However, it is unknown whether neural and behavioural differences in the processing of emotional stimuli persist following recovery. The aim of this study was to investigate the neural processing of emotional faces in individuals recovered from anorexia nervosa compared with healthy controls. Thirty-two participants (16 recovered anorexia nervosa, 16 healthy controls) underwent a functional magnetic resonance imaging (fMRI) scan. Participants viewed fearful and happy emotional faces and indicated the gender of the face presented. Whole brain analysis revealed no significant differences between the groups to the contrasts of fear versus happy and vice versa. Region of interest analysis demonstrated no significant differences in the neural response to happy or fearful stimuli between the groups in the amygdala or fusiform gyrus. These results suggest that processing of emotional faces may not be aberrant after recovery from anorexia nervosa. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro
In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.
Wang, Bin; Yan, Tianyi; Wu, Jinglong; Chen, Kewei; Imajyo, Satoshi; Ohno, Seiichiro; Kanazawa, Susumu
In human visual cortex, the primary visual cortex (V1) is considered to be essential for visual information processing; the fusiform face area (FFA) and parahippocampal place area (PPA) are considered as face-selective region and places-selective region, respectively. Recently, a functional magnetic resonance imaging (fMRI) study showed that the neural activity ratios between V1 and FFA were constant as eccentricities increasing in central visual field. However, in wide visual field, the neural activity relationships between V1 and FFA or V1 and PPA are still unclear. In this work, using fMRI and wide-view present system, we tried to address this issue by measuring neural activities in V1, FFA and PPA for the images of faces and houses aligning in 4 eccentricities and 4 meridians. Then, we further calculated ratio relative to V1 (RRV1) as comparing the neural responses amplitudes in FFA or PPA with those in V1. We found V1, FFA, and PPA showed significant different neural activities to faces and houses in 3 dimensions of eccentricity, meridian, and region. Most importantly, the RRV1s in FFA and PPA also exhibited significant differences in 3 dimensions. In the dimension of eccentricity, both FFA and PPA showed smaller RRV1s at central position than those at peripheral positions. In meridian dimension, both FFA and PPA showed larger RRV1s at upper vertical positions than those at lower vertical positions. In the dimension of region, FFA had larger RRV1s than PPA. We proposed that these differential RRV1s indicated FFA and PPA might have different processing strategies for encoding the wide field visual information from V1. These different processing strategies might depend on the retinal position at which faces or houses are typically observed in daily life. We posited a role of experience in shaping the information processing strategies in the ventral visual cortex.
Bressloff, Paul C.
We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.
Brown, Ramsay A; Swanson, Larry W
Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.
Ramamoorthy, P. A.
Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.
Sekiguchi, Atsushi; Sugiura, Motoaki; Yokoyama, Satoru; Sassa, Yuko; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta
Frustrating situations are encountered daily, and it is necessary to respond in an adaptive fashion. A psychological definition states that adaptive social behaviors are "self-performing" and "contain a solution." The present study investigated the neural correlates of adaptive social responses to frustrating situations by assessing the dimension of causal attribution. Based on attribution theory, internal causality refers to one's aptitudes that cause natural responses in real-life situations, whereas external causality refers to environmental factors, such as experimental conditions, causing such responses. To investigate the issue, we developed a novel approach that assesses causal attribution under experimental conditions. During fMRI scanning, subjects were required to engage in virtual frustrating situations and play the role of protagonists by verbalizing social responses, which were socially adaptive or non-adaptive. After fMRI scanning, the subjects reported their causal attribution index of the psychological reaction to the experimental condition. We performed a correlation analysis between the causal attribution index and brain activity. We hypothesized that the brain region whose activation would have a positive and negative correlation with the self-reported index of the causal attributions would be regarded as neural correlates of internal and external causal attribution of social responses, respectively. We found a significant negative correlation between external causal attribution and neural responses in the right anterior temporal lobe for adaptive social behaviors. This region is involved in the integration of emotional and social information. These results suggest that, particularly in adaptive social behavior, the social demands of frustrating situations, which involve external causality, may be integrated by a neural response in the right anterior temporal lobe.
Full Text Available Lehky et al. (2011 provided a statistical analysis on the responses of the recorded 674 neurons to 806 image stimuli in anterior inferotemporalm (AIT cortex of two monkeys. In terms of kurtosis and Pareto tail index, they observed that the population sparseness of both unnormalized and normalized responses is always larger than their single-neuron selectivity, hence concluded that the critical features for individual neurons in primate AIT cortex are not very complex, but there is an indefinitely large number of them. In this work, we explore an “inverse problem” by simulation, that is, by simulating each neuron indeed only responds to a very limited number of stimuli among a very large number of neurons and stimuli, to assess whether the population sparseness is always larger than the single-neuron selectivity. Our simulation results show that the population sparseness exceeds the single-neuron selectivity in most cases even if the number of neurons and stimuli are much larger than several hundreds, which confirms the observations in Lehky et al. (2011. In addition, we found that the variances of the computed kurtosis and Pareto tail index are quite large in some cases, which reveals some limitations of these two criteria when used for neuron response evaluation.
Full Text Available Mothers are important to all humans. Research has established that maternal information affects individuals' cognition, emotion, and behavior. We measured event-related potentials (ERPs to examine attentional and evaluative processing of maternal stimuli while participants completed a Go/No-go Association Task that paired mother or others words with good or bad evaluative words. Behavioral data showed that participants responded faster to mother words paired with good than the mother words paired with bad but showed no difference in response to these others across conditions, reflecting a positive evaluation of mother. ERPs showed larger P200 and N200 in response to mother than in response to others, suggesting that mother attracted more attention than others. In the subsequent time window, mother in the mother + bad condition elicited a later and larger late positive potential (LPP than it did in the mother + good condition, but this was not true for others, also suggesting a positive evaluation of mother. These results suggest that people differentiate mother from others during initial attentional stage, and evaluative mother positively during later stage.
In this thesis, two platforms for simulating artificial neural networks are discussed: MIMD-parallel processor systems as an execution platform and neurosimulators as a research and development platform. Because of the parallelism encountered in neural networks, distributed processor systems seem to
Gayet, Surya; Guggenmos, Matthias; Christophel, Thomas B; Haynes, John-Dylan; Paffen, Chris L E; Van der Stigchel, Stefan; Sterzer, Philipp
Visual working memory (VWM) is used to maintain visual information available for subsequent goal-directed behavior. The content of VWM has been shown to affect the behavioral response to concurrent visual input, suggesting that visual representations originating from VWM and from sensory input draw upon a shared neural substrate (i.e., a sensory recruitment stance on VWM storage). Here, we hypothesized that visual information maintained in VWM would enhance the neural response to concurrent visual input that matches the content of VWM. To test this hypothesis, we measured fMRI BOLD responses to task-irrelevant stimuli acquired from 15 human participants (three males) performing a concurrent delayed match-to-sample task. In this task, observers were sequentially presented with two shape stimuli and a retro-cue indicating which of the two shapes should be memorized for subsequent recognition. During the retention interval, a task-irrelevant shape (the probe) was briefly presented in the peripheral visual field, which could either match or mismatch the shape category of the memorized stimulus. We show that this probe stimulus elicited a stronger BOLD response, and allowed for increased shape-classification performance, when it matched rather than mismatched the concurrently memorized content, despite identical visual stimulation. Our results demonstrate that VWM enhances the neural response to concurrent visual input in a content-specific way. This finding is consistent with the view that neural populations involved in sensory processing are recruited for VWM storage, and it provides a common explanation for a plethora of behavioral studies in which VWM-matching visual input elicits a stronger behavioral and perceptual response. SIGNIFICANCE STATEMENT Humans heavily rely on visual information to interact with their environment and frequently must memorize such information for later use. Visual working memory allows for maintaining such visual information in the mind
Mohamed A. Korany
Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.
Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina
Auditory processing is presumed to be influenced by cognitive processes – including attentional control – in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] and separately obtained measures of attentional control and language ability in adolescent Spanish-English bilinguals and English monolinguals. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. PMID:24413593
Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P
In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Yokum, Sonja; Stice, Eric; Harris, Jennifer L.; Brownell, Kelly D.
Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean to obese in response to food and non-food commercials imbedded in a television show. Adolescents exhibited greater activation in regions implicated in visual processing (e.g. occipital gyrus), attention (e.g. parietal lobes), cognition (e.g. temporal gyrus and posterior cerebellar lobe), movement (e.g. anterior cerebellar cortex), somatosensory response (e.g. postcentral gyrus) and reward [e.g. orbitofrontal cortex and anterior cingulate cortex (ACC)] during food commercials. Obese participants exhibited less activation during food relative to non-food commercials in neural regions implicated in visual processing (e.g. cuneus), attention (e.g. posterior cerebellar lobe), reward (e.g. ventromedial prefrontal cortex and ACC) and salience detection (e.g. precuneus). Obese participants did exhibit greater activation in a region implicated in semantic control (e.g. medial temporal gyrus). These findings may inform current policy debates regarding the impact of food advertising to minors. PMID:23576811
Gearhardt, Ashley N; Yokum, Sonja; Stice, Eric; Harris, Jennifer L; Brownell, Kelly D
Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean to obese in response to food and non-food commercials imbedded in a television show. Adolescents exhibited greater activation in regions implicated in visual processing (e.g. occipital gyrus), attention (e.g. parietal lobes), cognition (e.g. temporal gyrus and posterior cerebellar lobe), movement (e.g. anterior cerebellar cortex), somatosensory response (e.g. postcentral gyrus) and reward [e.g. orbitofrontal cortex and anterior cingulate cortex (ACC)] during food commercials. Obese participants exhibited less activation during food relative to non-food commercials in neural regions implicated in visual processing (e.g. cuneus), attention (e.g. posterior cerebellar lobe), reward (e.g. ventromedial prefrontal cortex and ACC) and salience detection (e.g. precuneus). Obese participants did exhibit greater activation in a region implicated in semantic control (e.g. medial temporal gyrus). These findings may inform current policy debates regarding the impact of food advertising to minors. © The Author (2013). Published by Oxford University Press. For Permissions, please email: email@example.com.
Hashimoto, Kosuke; Kudoh, Suguru N; Sato, Hidetoshi
We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system.
The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS
Full Text Available This paper presents a neural network for designing of a PID controller for suspension system. The suspension system, designed as a quarter model, is used to simplify the problem to one-dimensional spring-damper system. In this paper, back propagation neural network (BPN has been used for determining the gain parameters of a PID controller for suspension system of automotive. The BPN method is found to be the most accurate and quick. The best results were obtained by the BPN by Levenberg-Marquardt algorithm training with 10 neurons in the one hidden layer. Training was continued until the mean squared error is less than . Desired error value was achieved in the BPN, and the BPN was tested with both data used and not used for training. By training of this network, it is possible to estimate the gain parameters of PID controller at any condition. The inputs of network are automotive velocity, overshoot percentage, settling time, and steady state error of suspension system response. Also outputs of the net are the gain parameters of PID controller. Resultant low relative error value of the ANN model indicates the usability of the BPN in this area.
Babo-Rebelo, Mariana; Richter, Craig G; Tallon-Baudry, Catherine
The default network (DN) has been consistently associated with self-related cognition, but also to bodily state monitoring and autonomic regulation. We hypothesized that these two seemingly disparate functional roles of the DN are functionally coupled, in line with theories proposing that selfhood is grounded in the neural monitoring of internal organs, such as the heart. We measured with magnetoencephalograhy neural responses evoked by heartbeats while human participants freely mind-wandered. When interrupted by a visual stimulus at random intervals, participants scored the self-relatedness of the interrupted thought. They evaluated their involvement as the first-person perspective subject or agent in the thought ("I"), and on another scale to what degree they were thinking about themselves ("Me"). During the interrupted thought, neural responses to heartbeats in two regions of the DN, the ventral precuneus and the ventromedial prefrontal cortex, covaried, respectively, with the "I" and the "Me" dimensions of the self, even at the single-trial level. No covariation between self-relatedness and peripheral autonomic measures (heart rate, heart rate variability, pupil diameter, electrodermal activity, respiration rate, and phase) or alpha power was observed. Our results reveal a direct link between selfhood and neural responses to heartbeats in the DN and thus directly support theories grounding selfhood in the neural monitoring of visceral inputs. More generally, the tight functional coupling between self-related processing and cardiac monitoring observed here implies that, even in the absence of measured changes in peripheral bodily measures, physiological and cognitive functions have to be considered jointly in the DN. The default network (DN) has been consistently associated with self-processing but also with autonomic regulation. We hypothesized that these two functions could be functionally coupled in the DN, inspired by theories according to which selfhood is
Romens, Sarah E; Casement, Melynda D; McAloon, Rose; Keenan, Kate; Hipwell, Alison E; Guyer, Amanda E; Forbes, Erika E
Children who experience socioeconomic disadvantage are at heightened risk for developing depression; however, little is known about neurobiological mechanisms underlying this association. Low socioeconomic status (SES) during childhood may confer risk for depression through its stress-related effects on the neural circuitry associated with processing monetary rewards. In a prospective study, we examined the relationships among the number of years of household receipt of public assistance from age 5-16 years, neural activation during monetary reward anticipation and receipt at age 16, and depression symptoms at age 16 in 123 girls. Number of years of household receipt of public assistance was positively associated with heightened response in the medial prefrontal cortex during reward anticipation, and this heightened neural response mediated the relationship between socioeconomic disadvantage and current depression symptoms, controlling for past depression. Chronic exposure to socioeconomic disadvantage in childhood may alter neural circuitry involved in reward anticipation in adolescence, which in turn may confer risk for depression. © 2015 Association for Child and Adolescent Mental Health.
Romens, Sarah E.; Casement, Melynda D.; McAloon, Rose; Keenan, Kate; Hipwell, Alison E.; Guyer, Amanda E.; Forbes, Erika E.
Background Children who experience socioeconomic disadvantage are at heightened risk for developing depression; however, little is known about neurobiological mechanisms underlying this association. Low socioeconomic status (SES) during childhood may confer risk for depression through its stress-related effects on the neural circuitry associated with processing monetary rewards. Methods In a prospective study, we examined the relationships among the number of years of household receipt of public assistance from age 5–16 years, neural activation during monetary reward anticipation and receipt at age 16, and depression symptoms at age 16 in 123 girls. Results Number of years of household receipt of public assistance was positively associated with heightened response in the medial prefrontal cortex during reward anticipation, and this heightened neural response mediated the relationship between socioeconomic disadvantage and current depression symptoms, controlling for past depression. Conclusions Chronic exposure to socioeconomic disadvantage in childhood may alter neural circuitry involved in reward anticipation in adolescence, which in turn may confer risk for depression. PMID:25846746
McAdams, Carrie J.; Lohrenz, Terry; Montague, P. Read
In anorexia nervosa, problems with social relationships contribute to illness, and improvements in social support are associated with recovery. Using the multiround trust game and 3T MRI, we compare neural responses in a social relationship in three groups of women: women with anorexia nervosa, women in long-term weight recovery from anorexia nervosa, and healthy comparison women. Surrogate markers related to social signals in the game were computed each round to assess whether the relationship was improving (benevolence) or deteriorating (malevolence) for each subject. Compared with healthy women, neural responses to benevolence were diminished in the precuneus and right angular gyrus in both currently-ill and weight-recovered subjects with anorexia, but neural responses to malevolence differed in the left fusiform only in currently-ill subjects. Next, using a whole-brain regression, we identified an office assessment, the positive personalizing bias, that was inversely correlated with neural activity in the occipital lobe, the precuneus and posterior cingulate, the bilateral temporoparietal junctions, and dorsal anterior cingulate, during benevolence for all groups of subjects. The positive personalizing bias is a self-report measure that assesses the degree with which a person attributes positive experiences to other people. These data suggest that problems in perceiving kindness may be a consistent trait related to the development of anorexia nervosa, whereas recognizing malevolence may be related to recovery. Future work on social brain function, in both healthy and psychiatric populations, should consider positive personalizing biases as a possible marker of neural differences related to kindness perception. PMID:26416161
McAdams, Carrie J; Lohrenz, Terry; Montague, P Read
In anorexia nervosa, problems with social relationships contribute to illness, and improvements in social support are associated with recovery. Using the multiround trust game and 3T MRI, we compare neural responses in a social relationship in three groups of women: women with anorexia nervosa, women in long-term weight recovery from anorexia nervosa, and healthy comparison women. Surrogate markers related to social signals in the game were computed each round to assess whether the relationship was improving (benevolence) or deteriorating (malevolence) for each subject. Compared with healthy women, neural responses to benevolence were diminished in the precuneus and right angular gyrus in both currently-ill and weight-recovered subjects with anorexia, but neural responses to malevolence differed in the left fusiform only in currently-ill subjects. Next, using a whole-brain regression, we identified an office assessment, the positive personalizing bias, that was inversely correlated with neural activity in the occipital lobe, the precuneus and posterior cingulate, the bilateral temporoparietal junctions, and dorsal anterior cingulate, during benevolence for all groups of subjects. The positive personalizing bias is a self-report measure that assesses the degree with which a person attributes positive experiences to other people. These data suggest that problems in perceiving kindness may be a consistent trait related to the development of anorexia nervosa, whereas recognizing malevolence may be related to recovery. Future work on social brain function, in both healthy and psychiatric populations, should consider positive personalizing biases as a possible marker of neural differences related to kindness perception. © 2015 Wiley Periodicals, Inc.
Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.
Ahmad Danial Azzahari
Full Text Available A gel polymer electrolyte system based on phthaloylchitosan was prepared. The effects of process variables, such as lithium iodide, caesium iodide, and 1-butyl-3-methylimidazolium iodide were investigated using a distance-based ternary mixture experimental design. A comparative approach was made between response surface methodology (RSM and artificial neural network (ANN to predict the ionic conductivity. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination R2 based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.
Bari, Andrea; Robbins, Trevor W
In many circumstances alternative courses of action and thoughts have to be inhibited to allow the emergence of goal-directed behavior. However, this has not been the accepted view in the past and only recently has inhibition earned its own place in the neurosciences as a fundamental cognitive function. In this review we first introduce the concept of inhibition from early psychological speculations based on philosophical theories of the human mind. The broad construct of inhibition is then reduced to its most readily observable component which necessarily is its behavioral manifestation. The study of 'response inhibition' has the advantage of dealing with a relatively simple and straightforward process, the overriding of a planned or already initiated action. Deficient inhibitory processes profoundly affect everyday life, causing impulsive conduct which is generally detrimental for the individual. Impulsivity has been consistently linked to several types of addiction, attention deficit/hyperactivity disorder, mania and other psychiatric conditions. Our discussion of the behavioral assessment of impulsivity will focus on objective laboratory tasks of response inhibition that have been implemented in parallel for humans and other species with relatively few qualitative differences. The translational potential of these measures has greatly improved our knowledge of the neurobiological basis of behavioral inhibition and impulsivity. We will then review the current models of behavioral inhibition along with their expression via underlying brain regions, including those involved in the activation of the brain's emergency 'brake' operation, those engaged in more controlled and sustained inhibitory processes and other ancillary executive functions. Copyright © 2013 Elsevier Ltd. All rights reserved.
Full Text Available Abstract Background Impairments in executive function and language processing are characteristic of both schizophrenia and bipolar disorder. Their functional neuroanatomy demonstrate features that are shared as well as specific to each disorder. Determining the distinct pattern of neural responses in schizophrenia and bipolar disorder may provide biomarkers for their diagnoses. Methods 104 participants underwent functional magnetic resonance imaging (fMRI scans while performing a phonological verbal fluency task. Subjects were 32 patients with schizophrenia in remission, 32 patients with bipolar disorder in an euthymic state, and 40 healthy volunteers. Neural responses to verbal fluency were examined in each group, and the diagnostic potential of the pattern of the neural responses was assessed with machine learning analysis. Results During the verbal fluency task, both patient groups showed increased activation in the anterior cingulate, left dorsolateral prefrontal cortex and right putamen as compared to healthy controls, as well as reduced deactivation of precuneus and posterior cingulate. The magnitude of activation was greatest in patients with schizophrenia, followed by patients with bipolar disorder and then healthy individuals. Additional recruitment in the right inferior frontal and right dorsolateral prefrontal cortices was observed in schizophrenia relative to both bipolar disorder and healthy subjects. The pattern of neural responses correctly identified individual patients with schizophrenia with an accuracy of 92%, and those with bipolar disorder with an accuracy of 79% in which mis-classification was typically of bipolar subjects as healthy controls. Conclusions In summary, both schizophrenia and bipolar disorder are associated with altered function in prefrontal, striatal and default mode networks, but the magnitude of this dysfunction is particularly marked in schizophrenia. The pattern of response to verbal fluency is highly
Yamano, Emi; Ishii, Akira; Tanaka, Masaaki; Nomura, Shusaku; Watanabe, Yasuyoshi
Stress is a risk factor for the onset of mental disorders. Although stress response varies across individuals, the mechanism of individual differences remains unclear. Here, we investigated the neural basis of individual differences in response to mental stress using magnetoencephalography (MEG). Twenty healthy male volunteers completed the Temperament and Character Inventory (TCI). The experiment included two types of tasks: a non-stress-inducing task and a stress-inducing task. During these tasks, participants passively viewed non-stress-inducing images and stress-inducing images, respectively, and MEG was recorded. Before and after each task, MEG and electrocardiography were recorded and subjective ratings were obtained. We grouped participants according to Novelty seeking (NS) - tendency to be exploratory, and Harm avoidance (HA) - tendency to be cautious. Participants with high NS and low HA (n = 10) assessed by TCI had a different neural response to stress than those with low NS and high HA (n = 10). Event-related desynchronization (ERD) in the beta frequency band was observed only in participants with high NS and low HA in the brain region extending from Brodmann's area 31 (including the posterior cingulate cortex and precuneus) from 200 to 350 ms after the onset of picture presentation in the stress-inducing task. Individual variation in personality traits (NS and HA) was associated with the neural response to mental stress. These findings increase our understanding of the psychological and neural basis of individual differences in the stress response, and will contribute to development of the psychotherapeutic approaches to stress-related disorders.
Dedovic, Katarina; Slavich, George M.; Jarcho, Michael R.; Breen, Elizabeth C.; Bower, Julienne E.; Irwin, Michael R.; Eisenberger, Naomi I.
Social stratification has important implications for health and well-being, with individuals lower in standing in a hierarchy experiencing worse outcomes than those higher up the social ladder. Separate lines of past research suggest that alterations in inflammatory processes and neural responses to threat may link lower social status with poorer outcomes. This study was designed to bridge these literatures to investigate the neurocognitive mechanisms linking subjective social status and inflammation. Thirty-one participants reported their subjective social status, and underwent a functional magnetic resonance imaging scan while they were socially evaluated. Participants also provided blood samples before and after the stressor, which were analysed for changes in inflammation. Results showed that lower subjective social status was associated with greater increases in inflammation. Neuroimaging data revealed lower subjective social status was associated with greater neural activity in the dorsomedial prefrontal cortex (DMPFC) in response to negative feedback. Finally, results indicated that activation in the DMPFC in response to negative feedback mediated the relation between social status and increases in inflammatory activity. This study provides the first evidence of a neurocognitive pathway linking subjective social status and inflammation, thus furthering our understanding of how social hierarchies shape neural and physiological responses to social interactions. PMID:26979965
Verma, Rohit; Guex, Amelie A.; Hancock, Kenneth E.; Durakovic, Nedim; McKay, Colette M.; Slama, Michaël C. C.; Brown, M. Christian; Lee, Daniel J.
In an effort to improve the auditory brainstem implant, a prosthesis in which user outcomes are modest, we applied electric and infrared neural stimulation (INS) to the cochlear nucleus in a rat animal model. Electric stimulation evoked regions of neural activation in the inferior colliculus and short-latency, multipeaked auditory brainstem responses (ABRs). Pulsed INS, delivered to the surface of the cochlear nucleus via an optical fiber, evoked broad neural activation in the inferior colliculus. Strongest responses were recorded when the fiber was placed at lateral positions on the cochlear nucleus, close to the temporal bone. INS-evoked ABRs were multipeaked but longer in latency than those for electric stimulation; they resembled the responses to acoustic stimulation. After deafening, responses to electric stimulation persisted, whereas those to INS disappeared, consistent with a reported “optophonic” effect, a laser-induced acoustic artifact. Thus, for deaf individuals who use the auditory brainstem implant, INS alone did not appear promising as a new approach. PMID:24508368
Verma, Rohit U; Guex, Amélie A; Hancock, Kenneth E; Durakovic, Nedim; McKay, Colette M; Slama, Michaël C C; Brown, M Christian; Lee, Daniel J
In an effort to improve the auditory brainstem implant, a prosthesis in which user outcomes are modest, we applied electric and infrared neural stimulation (INS) to the cochlear nucleus in a rat animal model. Electric stimulation evoked regions of neural activation in the inferior colliculus and short-latency, multipeaked auditory brainstem responses (ABRs). Pulsed INS, delivered to the surface of the cochlear nucleus via an optical fiber, evoked broad neural activation in the inferior colliculus. Strongest responses were recorded when the fiber was placed at lateral positions on the cochlear nucleus, close to the temporal bone. INS-evoked ABRs were multipeaked but longer in latency than those for electric stimulation; they resembled the responses to acoustic stimulation. After deafening, responses to electric stimulation persisted, whereas those to INS disappeared, consistent with a reported "optophonic" effect, a laser-induced acoustic artifact. Thus, for deaf individuals who use the auditory brainstem implant, INS alone did not appear promising as a new approach. Copyright © 2014 Elsevier B.V. All rights reserved.
Casement, Melynda D.; Guyer, Amanda E.; Hipwell, Alison; McAloon, Rose L.; Hoffmann, Amy M.; Keenan, Kathryn; Forbes, Erika E.
Developmental models of psychopathology posit that exposure to social stressors may confer risk for depression in adolescent girls by disrupting neural reward circuitry. The current study tested this hypothesis by examining the relationship between early adolescent social stressors and later neural reward processing and depressive symptoms. Participants were 120 girls from an ongoing longitudinal study of precursors to depression across adolescent development. Low parental warmth, peer victimization, and depressive symptoms were assessed when the girls were 11 and 12 years old, and participants completed a monetary reward guessing fMRI task and assessment of depressive symptoms at age 16. Results indicate that low parental warmth was associated with increased response to potential rewards in the medial prefrontal cortex (mPFC), striatum, and amygdala, whereas peer victimization was associated with decreased response to potential rewards in the mPFC. Furthermore, concurrent depressive symptoms were associated with increased reward anticipation response in mPFC and striatal regions that were also associated with early adolescent psychosocial stressors, with mPFC and striatal response mediating the association between social stressors and depressive symptoms. These findings are consistent with developmental models that emphasize the adverse impact of early psychosocial stressors on neural reward processing and risk for depression in adolescence. PMID:24397999
Casement, Melynda D; Guyer, Amanda E; Hipwell, Alison E; McAloon, Rose L; Hoffmann, Amy M; Keenan, Kathryn E; Forbes, Erika E
Developmental models of psychopathology posit that exposure to social stressors may confer risk for depression in adolescent girls by disrupting neural reward circuitry. The current study tested this hypothesis by examining the relationship between early adolescent social stressors and later neural reward processing and depressive symptoms. Participants were 120 girls from an ongoing longitudinal study of precursors to depression across adolescent development. Low parental warmth, peer victimization, and depressive symptoms were assessed when the girls were 11 and 12 years old, and participants completed a monetary reward guessing fMRI task and assessment of depressive symptoms at age 16. Results indicate that low parental warmth was associated with increased response to potential rewards in the medial prefrontal cortex (mPFC), striatum, and amygdala, whereas peer victimization was associated with decreased response to potential rewards in the mPFC. Furthermore, concurrent depressive symptoms were associated with increased reward anticipation response in mPFC and striatal regions that were also associated with early adolescent psychosocial stressors, with mPFC and striatal response mediating the association between social stressors and depressive symptoms. These findings are consistent with developmental models that emphasize the adverse impact of early psychosocial stressors on neural reward processing and risk for depression in adolescence. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.
Melynda D. Casement
Full Text Available Developmental models of psychopathology posit that exposure to social stressors may confer risk for depression in adolescent girls by disrupting neural reward circuitry. The current study tested this hypothesis by examining the relationship between early adolescent social stressors and later neural reward processing and depressive symptoms. Participants were 120 girls from an ongoing longitudinal study of precursors to depression across adolescent development. Low parental warmth, peer victimization, and depressive symptoms were assessed when the girls were 11 and 12 years old, and participants completed a monetary reward guessing fMRI task and assessment of depressive symptoms at age 16. Results indicate that low parental warmth was associated with increased response to potential rewards in the medial prefrontal cortex (mPFC, striatum, and amygdala, whereas peer victimization was associated with decreased response to potential rewards in the mPFC. Furthermore, concurrent depressive symptoms were associated with increased reward anticipation response in mPFC and striatal regions that were also associated with early adolescent psychosocial stressors, with mPFC and striatal response mediating the association between social stressors and depressive symptoms. These findings are consistent with developmental models that emphasize the adverse impact of early psychosocial stressors on neural reward processing and risk for depression in adolescence.
Patricia Z. Tan
Full Text Available Parenting is often implicated as a potential source of individual differences in youths’ emotional information processing. The present study examined whether parental affect is related to an important aspect of adolescent emotional development, response to peer evaluation. Specifically, we examined relations between maternal negative affect, observed during parent–adolescent discussion of an adolescent-nominated concern with which s/he wants parental support, and adolescent neural responses to peer evaluation in 40 emotionally healthy and depressed adolescents. We focused on a network of ventral brain regions involved in affective processing of social information: the amygdala, anterior insula, nucleus accumbens, and subgenual anterior cingulate, as well as the ventrolateral prefrontal cortex. Maternal negative affect was not associated with adolescent neural response to peer rejection. However, longer durations of maternal negative affect were associated with decreased responsivity to peer acceptance in the amygdala, left anterior insula, subgenual anterior cingulate, and left nucleus accumbens. These findings provide some of the first evidence that maternal negative affect is associated with adolescents’ neural processing of social rewards. Findings also suggest that maternal negative affect could contribute to alterations in affective processing, specifically, dampening the saliency and/or reward of peer interactions during adolescence.
Muscatell, Keely A; Dedovic, Katarina; Slavich, George M; Jarcho, Michael R; Breen, Elizabeth C; Bower, Julienne E; Irwin, Michael R; Eisenberger, Naomi I
Social stratification has important implications for health and well-being, with individuals lower in standing in a hierarchy experiencing worse outcomes than those higher up the social ladder. Separate lines of past research suggest that alterations in inflammatory processes and neural responses to threat may link lower social status with poorer outcomes. This study was designed to bridge these literatures to investigate the neurocognitive mechanisms linking subjective social status and inflammation. Thirty-one participants reported their subjective social status, and underwent a functional magnetic resonance imaging scan while they were socially evaluated. Participants also provided blood samples before and after the stressor, which were analysed for changes in inflammation. Results showed that lower subjective social status was associated with greater increases in inflammation. Neuroimaging data revealed lower subjective social status was associated with greater neural activity in the dorsomedial prefrontal cortex (DMPFC) in response to negative feedback. Finally, results indicated that activation in the DMPFC in response to negative feedback mediated the relation between social status and increases in inflammatory activity. This study provides the first evidence of a neurocognitive pathway linking subjective social status and inflammation, thus furthering our understanding of how social hierarchies shape neural and physiological responses to social interactions. © The Author (2016). Published by Oxford University Press. For Permissions, please email: firstname.lastname@example.org.
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...
Just, Marcel Adam; Keller, Timothy A.; Malave, Vicente L.; Kana, Rajesh K.; Varma, Sashank
The underconnectivity theory of autism attributes the disorder to lower anatomical and functional systems connectivity between frontal and more posterior cortical processing. Here we review evidence for the theory and present a computational model of an executive functioning task (Tower of London) implementing the assumptions of underconnectivity. We make two modifications to a previous computational account of performance and brain activity in typical individuals in the Tower of London task (Newman et al., 2003): (1) the communication bandwidth between frontal and parietal areas was decreased and (2) the posterior centers were endowed with more executive capability (i.e., more autonomy, an adaptation is proposed to arise in response to the lowered frontal-posterior bandwidth). The autism model succeeds in matching the lower frontal-posterior functional connectivity (lower synchronization of activation) seen in fMRI data, as well as providing insight into behavioral response time results. The theory provides a unified account of how a neural dysfunction can produce a neural systems disorder and a psychological disorder with the widespread and diverse symptoms of autism. PMID:22353426
Full Text Available Sepsis progresses to multiple organ dysfunction due to the uncontrolled release of inflammatory mediators, and a growing body of evidence shows that neural signals play a significant role in modulating the immune response. Thus, similar toall other physiological systems, the immune system is both connected to and regulated by the central nervous system. The efferent arc consists of the activation of the hypothalamic–pituitary–adrenal axis, sympathetic activation, the cholinergic anti-inflammatory reflex, and the local release of physiological neuromodulators. Immunosensory activity is centered on the production of pro-inflammatory cytokines, signals that are conveyed to the brain through different pathways. The activation of peripheral sensory nerves, i.e., vagal paraganglia by the vagus nerve, and carotid body (CB chemoreceptors by the carotid/sinus nerve are broadly discussed here. Despite cytokine receptor expression in vagal afferent fibers, pro-inflammatory cytokines have no significant effect on vagus nerve activity. Thus, the CB may be the source of immunosensory inputs and incoming neural signals and, in fact, sense inflammatory mediators, playing a protective role during sepsis. Considering that CB stimulation increases sympathetic activity and adrenal glucocorticoids release, the electrical stimulation of arterial chemoreceptors may be suitable therapeutic approach for regulating systemic inflammation.
Mauricio R Delgado
Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based
Nawijn, Laura; van Zuiden, Mirjam; Koch, Saskia B J; Frijling, Jessie L; Veltman, Dick J; Olff, Miranda
Therapeutic alliance and perceived social support are important predictors of treatment response for post-traumatic stress disorder (PTSD). Intranasal oxytocin administration may enhance treatment response by increasing sensitivity for social reward and thereby therapeutic alliance and perceived social support. As a first step to investigate this therapeutical potential, we investigated whether intranasal oxytocin enhances neural sensitivity to social reward in PTSD patients. Male and female police officers with (n = 35) and without PTSD (n = 37) were included in a double-blind, randomized, placebo-controlled cross-over fMRI study. After intranasal oxytocin (40 IU) and placebo administration, a social incentive delay task was conducted to investigate neural responses during social reward and punishment anticipation and feedback. Under placebo, PTSD patients showed reduced left anterior insula (AI) responses to social rewards (i.e. happy faces) compared with controls. Oxytocin administration increased left AI responses during social reward in PTSD patients, such that PTSD patients no longer differed from controls under placebo. Furthermore, in PTSD patients, oxytocin increased responses to social reward in the right putamen. By normalizing abberant insula responses and increasing putamen responses to social reward, oxytocin administration may enhance sensitivity for social support and therapeutic alliance in PTSD patients. Future studies are needed to investigate clinical effects of oxytocin. © The Author (2016). Published by Oxford University Press.
Heather L Chapin
Full Text Available The aim of this study was to explore the role of attention in pulse and meter perception using complex rhythms. We used a selective attention paradigm in which participants attended to either a complex auditory rhythm or a visually presented word list. Performance on a reproduction task was used to gauge whether participants were attending to the appropriate stimulus. We hypothesized that attention to complex rhythms – which contain no energy at the pulse frequency – would lead to activations in motor areas involved in pulse perception. Moreover, because multiple repetitions of a complex rhythm are needed to perceive a pulse, activations in pulse related areas would be seen only after sufficient time had elapsed for pulse perception to develop. Selective attention was also expected to modulate activity in sensory areas specific to the modality. We found that selective attention to rhythms led to increased BOLD responses in basal ganglia, and basal ganglia activity was observed only after the rhythms had cycled enough times for a stable pulse percept to develop. These observations suggest that attention is needed to recruit motor activations associated with the perception of pulse in complex rhythms. Moreover, attention to the auditory stimulus enhanced activity in an attentional sensory network including primary auditory, insula, anterior cingulate, and prefrontal cortex, and suppressed activity in sensory areas associated with attending to the visual stimulus.
Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)
Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.
Lemon, Christian H
This mini-review discusses some of the parallels between rodent neurophysiological and human psychophysical data concerning temperature effects on sweet taste. "Sweet" is an innately rewarding taste sensation that is associated in part with foods that contain calories in the form of sugars. Humans and other mammals can show unconditioned preference for select sweet stimuli. Such preference is poised to influence diet selection and, in turn, nutritional status, which underscores the importance of delineating the physiological mechanisms for sweet taste with respect to their influence on human health. Advances in our knowledge of the biology of sweet taste in humans have arisen in part through studies on mechanisms of gustatory processing in rodent models. Along this line, recent work has revealed there are operational parallels in neural systems for sweet taste between mice and humans, as indexed by similarities in the effects of temperature on central neurophysiological and psychophysical responses to sucrose in these species. Such association strengthens the postulate that rodents can serve as effective models of particular mechanisms of appetitive taste processing. Data supporting this link are discussed here, as are rodent and human data that shed light on relationships between mechanisms for sweet taste and ingestive disorders, such as alcohol abuse. Rodent models have utility for understanding mechanisms of taste processing that may pertain to human flavor perception. Importantly, there are limitations to generalizing data from rodents, albeit parallels across species do exist.
Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan
Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.
LAHEEB MOHAMMAD IBRAHIM
Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.
Iris I A Groen
Full Text Available The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis. Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.
Zachary B Gaber
Full Text Available Distinct classes of neurons and glial cells in the developing spinal cord arise at specific times and in specific quantities from spatially discrete neural progenitor domains. Thus, adjacent domains can exhibit marked differences in their proliferative potential and timing of differentiation. However, remarkably little is known about the mechanisms that account for this regional control. Here, we show that the transcription factor Promyelocytic Leukemia Zinc Finger (PLZF plays a critical role shaping patterns of neuronal differentiation by gating the expression of Fibroblast Growth Factor (FGF Receptor 3 and responsiveness of progenitors to FGFs. PLZF elevation increases FGFR3 expression and STAT3 pathway activity, suppresses neurogenesis, and biases progenitors towards glial cell production. In contrast, PLZF loss reduces FGFR3 levels, leading to premature neuronal differentiation. Together, these findings reveal a novel transcriptional strategy for spatially tuning the responsiveness of distinct neural progenitor groups to broadly distributed mitogenic signals in the embryonic environment.
Full Text Available During neural tissue genesis, neural stem/progenitor cells are exposed to bioelectric stimuli well before synaptogenesis and neural circuit formation. Fluctuations in the electrochemical potential in the vicinity of developing cells influence the genesis, migration and maturation of neuronal precursors. The complexity of the in vivo environment and the coexistence of various progenitor populations hinder the understanding of the significance of ionic/bioelectric stimuli in the early phases of neuronal differentiation. Using optogenetic stimulation, we investigated the in vitro motility responses of radial glia-like neural stem/progenitor populations to ionic stimuli. Radial glia-like neural stem cells were isolated from CAGloxpStoploxpChR2(H134-eYFP transgenic mouse embryos. After transfection with Cre-recombinase, ChR2(channelrhodopsin-2-expressing and non-expressing cells were separated by eYFP fluorescence. Expression of light-gated ion channels were checked by patch clamp and fluorescence intensity assays. Neurogenesis by ChR2-expressing and non-expressing cells was induced by withdrawal of EGF from the medium. Cells in different (stem cell, migrating progenitor and maturing precursor stages of development were illuminated with laser light (λ = 488 nm; 1.3 mW/mm2; 300 ms in every 5 min for 12 h. The displacement of the cells was analyzed on images taken at the end of each light pulse. Results demonstrated that the migratory activity decreased with the advancement of neuronal differentiation regardless of stimulation. Light-sensitive cells, however, responded on a differentiation-dependent way. In non-differentiated ChR2-expressing stem cell populations, the motility did not change significantly in response to light-stimulation. The displacement activity of migrating progenitors was enhanced, while the motility of differentiating neuronal precursors was markedly reduced by illumination.
Rami J Oweis
Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.
Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen
Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.
Murphy, Anna; Nestor, Liam J; McGonigle, John; Paterson, Louise; Boyapati, Venkataramana; Ersche, Karen D; Flechais, Remy; Kuchibatla, Shankar; Metastasio, Antonio; Orban, Csaba; Passetti, Filippo; Reed, Laurence; Smith, Dana; Suckling, John; Taylor, Eleanor; Robbins, Trevor W; Lingford-Hughes, Anne; Nutt, David J; Deakin, John Fw; Elliott, Rebecca
Evidence suggests that disturbances in neurobiological mechanisms of reward and inhibitory control maintain addiction and provoke relapse during abstinence. Abnormalities within the dopamine system may contribute to these disturbances and pharmacologically targeting the D3 dopamine receptor (DRD3) is therefore of significant clinical interest. We used functional magnetic resonance imaging to investigate the acute effects of the DRD3 antagonist GSK598809 on anticipatory reward processing, using the monetary incentive delay task (MIDT), and response inhibition using the Go/No-Go task (GNGT). A double-blind, placebo-controlled, crossover design approach was used in abstinent alcohol dependent, abstinent poly-drug dependent and healthy control volunteers. For the MIDT, there was evidence of blunted ventral striatal response to reward in the poly-drug-dependent group under placebo. GSK598809 normalized ventral striatal reward response and enhanced response in the DRD3-rich regions of the ventral pallidum and substantia nigra. Exploratory investigations suggested that the effects of GSK598809 were mainly driven by those with primary dependence on alcohol but not on opiates. Taken together, these findings suggest that GSK598809 may remediate reward deficits in substance dependence. For the GNGT, enhanced response in the inferior frontal cortex of the poly-drug group was found. However, there were no effects of GSK598809 on the neural network underlying response inhibition nor were there any behavioral drug effects on response inhibition. GSK598809 modulated the neural network underlying reward anticipation but not response inhibition, suggesting that DRD3 antagonists may restore reward deficits in addiction.
Full Text Available As a type of the spectrally efficient modulation, the m-ary phase position shift keying (MPPSK has been considered to meet the increasing spectrum requirement in the future wireless system. To limit the signal bandwidth and cancel the out-band interference the band-pass filters are used, which introduce the waveform distortion and inter-symbol interference (ISI. Therefore, a single hidden-layer neural network (NN-based receiver is proposed to jointly equalize and demodulate the received signal. The impulse response of the system is static and the network parameters can be obtained after off-line training. The number of the hidden nodes is also determined through simulations. Simulation results show that the NN-based receiver works well in the communication system with different allocated bandwidths. By observing the modified confusion matrix, the false symbol decision is relevant to modulation index, waveform distortions and the ISI.
Davis, Matthew H; Gaskell, M Gareth
In this paper we present a novel theory of the cognitive and neural processes by which adults learn new spoken words. This proposal builds on neurocomputational accounts of lexical processing and spoken word recognition and complementary learning systems (CLS) models of memory. We review evidence from behavioural studies of word learning that, consistent with the CLS account, show two stages of lexical acquisition: rapid initial familiarization followed by slow lexical consolidation. These stages map broadly onto two systems involved in different aspects of word learning: (i) rapid, initial acquisition supported by medial temporal and hippocampal learning, (ii) slower neocortical learning achieved by offline consolidation of previously acquired information. We review behavioural and neuroscientific evidence consistent with this account, including a meta-analysis of PET and functional Magnetic Resonance Imaging (fMRI) studies that contrast responses to spoken words and pseudowords. From this meta-analysis we derive predictions for the location and direction of cortical response changes following familiarization with pseudowords. This allows us to assess evidence for learning-induced changes that convert pseudoword responses into real word responses. Results provide unique support for the CLS account since hippocampal responses change during initial learning, whereas cortical responses to pseudowords only become word-like if overnight consolidation follows initial learning.
Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the
Guyer, Amanda E.; Jarcho, Johanna M.; Pérez-Edgar, Koraly; Degnan, Kathryn A.; Pine, Daniel S.; Fox, Nathan A.; Nelson, Eric E.
Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children’s caregiving context. The convergence of a child’s temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The p...
Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.
Full Text Available The questions whether and how empathy for pain can be modulated by acute alcohol intoxication in the non-dependent population remain unanswered. To address these questions, a double-blind, placebo-controlled, within-subject study design was adopted in this study, in which healthy social drinkers were asked to complete a pain-judgment task using pictures depicting others' body parts in painful or non-painful situations during fMRI scanning, either under the influence of alcohol intoxication or placebo conditions. Empathic neural activity for pain was reduced by alcohol intoxication only in the dorsal anterior cingulate cortex (dACC. More interestingly, we observed that empathic neural activity for pain in the right anterior insula (rAI was significantly correlated with trait empathy only after alcohol intoxication, along with impaired functional connectivity between the rAI and the fronto-parietal attention network. Our results reveal that alcohol intoxication not only inhibits empathic neural responses for pain but also leads to trait empathy inflation, possibly via impaired top-down attentional control. These findings help to explain the neural mechanism underlying alcohol-related social problems.
Joni N Saby
Full Text Available Human infants rapidly learn new skills and customs via imitation, but the neural linkages between action perception and production are not well understood. Neuroscience studies in adults suggest that a key component of imitation-identifying the corresponding body part used in the acts of self and other-has an organized neural signature. In adults, perceiving someone using a specific body part (e.g., hand vs. foot is associated with activation of the corresponding area of the sensory and/or motor strip in the observer's brain-a phenomenon called neural somatotopy. Here we examine whether preverbal infants also exhibit somatotopic neural responses during the observation of others' actions. 14-month-old infants were randomly assigned to watch an adult reach towards and touch an object using either her hand or her foot. The scalp electroencephalogram (EEG was recorded and event-related changes in the sensorimotor mu rhythm were analyzed. Mu rhythm desynchronization was greater over hand areas of sensorimotor cortex during observation of hand actions and was greater over the foot area for observation of foot actions. This provides the first evidence that infants' observation of someone else using a particular body part activates the corresponding areas of sensorimotor cortex. We hypothesize that this somatotopic organization in the developing brain supports imitation and cultural learning. The findings connect developmental cognitive neuroscience, adult neuroscience, action representation, and behavioral imitation.
ter Hofstede, Hannah M; Goerlitz, Holger R; Montealegre-Z, Fernando; Robert, Daniel; Holderied, Marc W
Ears evolved in many groups of moths to detect the echolocation calls of predatory bats. Although the neurophysiology of bat detection has been intensively studied in moths for decades, the relationship between sound-induced movement of the noctuid tympanic membrane and action potentials in the auditory sensory cells (A1 and A2) has received little attention. Using laser Doppler vibrometry, we measured the velocity and displacement of the tympanum in response to pure tone pulses for moths that were intact or prepared for neural recording. When recording from the auditory nerve, the displacement of the tympanum at the neural threshold remained constant across frequencies, whereas velocity varied with frequency. This suggests that the key biophysical parameter for triggering action potentials in the sensory cells of noctuid moths is tympanum displacement, not velocity. The validity of studies on the neurophysiology of moth hearing rests on the assumption that the dissection and recording procedures do not affect the biomechanics of the ear. There were no consistent differences in tympanal velocity or displacement when moths were intact or prepared for neural recordings for sound levels close to neural threshold, indicating that this and other neurophysiological studies provide good estimates of what intact moths hear at threshold.
Adams, Meghan Sara; Bronner-Fraser, Marianne
The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.
Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.
Samantha J Brooks
Full Text Available BACKGROUND: Previous fMRI studies show that women with eating disorders (ED have differential neural activation to viewing food images. However, despite clinical differences in their responses to food, differential neural activation to thinking about eating food, between women with anorexia nervosa (AN and bulimia nervosa (BN is not known. METHODS: We compare 50 women (8 with BN, 18 with AN and 24 age-matched healthy controls [HC] while they view food images during functional Magnetic Resonance Imaging (fMRI. RESULTS: In response to food (vs non-food images, women with BN showed greater neural activation in the visual cortex, right dorsolateral prefrontal cortex, right insular cortex and precentral gyrus, women with AN showed greater activation in the right dorsolateral prefrontal cortex, cerebellum and right precuneus. HC women activated the cerebellum, right insular cortex, right medial temporal lobe and left caudate. Direct comparisons revealed that compared to HC, the BN group showed relative deactivation in the bilateral superior temporal gyrus/insula, and visual cortex, and compared to AN had relative deactivation in the parietal lobe and dorsal posterior cingulate cortex, but greater activation in the caudate, superior temporal gyrus, right insula and supplementary motor area. CONCLUSIONS: Women with AN and BN activate top-down cognitive control in response to food images, yet women with BN have increased activation in reward and somatosensory regions, which might impinge on cognitive control over food consumption and binge eating.
Dean, Z; Horndasch, S; Giannopoulos, P; McCabe, C
We have previously shown that the selective serotonergic reuptake inhibitor, citalopram, reduces the neural response to reward and aversion in healthy volunteers. We suggest that this inhibitory effect might underlie the emotional blunting reported by patients on these medications. Bupropion is a dopaminergic and noradrenergic reuptake inhibitor and has been suggested to have more therapeutic effects on reward-related deficits. However, how bupropion affects the neural responses to reward and aversion is unclear. Seventeen healthy volunteers (9 female, 8 male) received 7 days bupropion (150 mg/day) and 7 days placebo treatment, in a double-blind crossover design. Our functional magnetic resonance imaging task consisted of three phases; an anticipatory phase (pleasant or unpleasant cue), an effort phase (button presses to achieve a pleasant taste or to avoid an unpleasant taste) and a consummatory phase (pleasant or unpleasant tastes). Volunteers also rated wanting, pleasantness and intensity of the tastes. Relative to placebo, bupropion increased activity during the anticipation phase in the ventral medial prefrontal cortex (vmPFC) and caudate. During the effort phase, bupropion increased activity in the vmPFC, striatum, dorsal anterior cingulate cortex and primary motor cortex. Bupropion also increased medial orbitofrontal cortex, amygdala and ventral striatum activity during the consummatory phase. Our results are the first to show that bupropion can increase neural responses during the anticipation, effort and consummation of rewarding and aversive stimuli. This supports the notion that bupropion might be beneficial for depressed patients with reward-related deficits and blunted affect.
Burkhouse, Katie L; Kujawa, Autumn; Kennedy, Amy E; Shankman, Stewart A; Langenecker, Scott A; Phan, K Luan; Klumpp, Heide
Cognitive behavioral therapy (CBT) is a well-established treatment for anxiety and depression; however, response to CBT is heterogeneous across patients and many remain symptomatic after therapy, raising the need to identify prospective predictors for treatment planning. Altered neural processing of reward has been implicated in both depression and anxiety, and improving hedonic capacity is a goal of CBT. However, little is known about how neural response to reward relates to CBT outcomes in depression and anxiety. The current study used the reward positivity (RewP) event-related potential (ERP) component to examine whether neural reactivity to reward would predict CBT response in a sample of patients with anxiety without depression (n = 30) and comorbid anxiety and depression (CAD, n = 22). Participants completed a guessing reward ERP paradigm before completing 12 weeks of standard CBT. The majority of the sample (68%; 35 out of 52 patients) responded to treatment, and those with a reduced RewP at baseline were more likely to respond to treatment. A reduced RewP was also associated with a greater pre-to-post CBT reduction in depressive symptoms among individuals with CAD, but not among individuals with pure anxiety. CBT may be most beneficial in reducing depressive symptoms for individuals who demonstrate decreased reward reactivity prior to treatment. CBT may target reward brain function, leading to greater improvement in symptoms. These effects may be strongest, and therefore most meaningful, for individuals with reward-processing deficits prior to treatment. © 2016 Wiley Periodicals, Inc.
Full Text Available Recent research suggests that conceptual or emotional factors could influence the perceptual processing of stimuli. In this article, we aimed to evaluate the effect of social information (positive, negative, or no information related to the character of the target on subjective (perceived and felt valence and arousal, physiological (facial mimicry as well as on neural (P100 and N170 responses to dynamic emotional facial expressions (EFE that varied from neutral to one of the six basic emotions. Across three studies, the results showed reduced ratings of valence and arousal of EFE associated with incongruent social information (Study 1, increased electromyographical responses (Study 2, and significant modulation of P100 and N170 components (Study 3 when EFE were associated with social (positive and negative information (vs. no information. These studies revealed that positive or negative social information reduces subjective responses to incongruent EFE and produces a similar neural and physiological boost of the early perceptual processing of EFE irrespective of their congruency. In conclusion, the article suggests that the presence of positive or negative social context modulates early physiological and neural activity preceding subsequent behavior.
Brooks, Samantha J.; O′Daly, Owen G.; Uher, Rudolf; Friederich, Hans-Christoph; Giampietro, Vincent; Brammer, Michael; Williams, Steven C. R.; Schiöth, Helgi B.; Treasure, Janet; Campbell, Iain C.
Background Previous fMRI studies show that women with eating disorders (ED) have differential neural activation to viewing food images. However, despite clinical differences in their responses to food, differential neural activation to thinking about eating food, between women with anorexia nervosa (AN) and bulimia nervosa (BN) is not known. Methods We compare 50 women (8 with BN, 18 with AN and 24 age-matched healthy controls [HC]) while they view food images during functional Magnetic Resonance Imaging (fMRI). Results In response to food (vs non-food) images, women with BN showed greater neural activation in the visual cortex, right dorsolateral prefrontal cortex, right insular cortex and precentral gyrus, women with AN showed greater activation in the right dorsolateral prefrontal cortex, cerebellum and right precuneus. HC women activated the cerebellum, right insular cortex, right medial temporal lobe and left caudate. Direct comparisons revealed that compared to HC, the BN group showed relative deactivation in the bilateral superior temporal gyrus/insula, and visual cortex, and compared to AN had relative deactivation in the parietal lobe and dorsal posterior cingulate cortex, but greater activation in the caudate, superior temporal gyrus, right insula and supplementary motor area. Conclusions Women with AN and BN activate top-down cognitive control in response to food images, yet women with BN have increased activation in reward and somatosensory regions, which might impinge on cognitive control over food consumption and binge eating. PMID:21799807
Brooks, Samantha J; O'Daly, Owen G; Uher, Rudolf; Friederich, Hans-Christoph; Giampietro, Vincent; Brammer, Michael; Williams, Steven C R; Schiöth, Helgi B; Treasure, Janet; Campbell, Iain C
Previous fMRI studies show that women with eating disorders (ED) have differential neural activation to viewing food images. However, despite clinical differences in their responses to food, differential neural activation to thinking about eating food, between women with anorexia nervosa (AN) and bulimia nervosa (BN) is not known. We compare 50 women (8 with BN, 18 with AN and 24 age-matched healthy controls [HC]) while they view food images during functional Magnetic Resonance Imaging (fMRI). In response to food (vs non-food) images, women with BN showed greater neural activation in the visual cortex, right dorsolateral prefrontal cortex, right insular cortex and precentral gyrus, women with AN showed greater activation in the right dorsolateral prefrontal cortex, cerebellum and right precuneus. HC women activated the cerebellum, right insular cortex, right medial temporal lobe and left caudate. Direct comparisons revealed that compared to HC, the BN group showed relative deactivation in the bilateral superior temporal gyrus/insula, and visual cortex, and compared to AN had relative deactivation in the parietal lobe and dorsal posterior cingulate cortex, but greater activation in the caudate, superior temporal gyrus, right insula and supplementary motor area. Women with AN and BN activate top-down cognitive control in response to food images, yet women with BN have increased activation in reward and somatosensory regions, which might impinge on cognitive control over food consumption and binge eating.
Full Text Available The trial-and-error method is widely used for the current optimization of the steel casting feeding system, which is highly random, subjective and thus inefficient. In the present work, both the theoretical and the experimental research on the modeling and optimization methods of the process are studied. An approximate alternative model is established based on the Back Propagation (BP neural network and experimental design. The process parameters of the feeding system are taken as the input, the volumes of shrinkage cavities and porosities calculated by simulation are simultaneously taken as the output. Thus, a mathematical model is established by the BP neural network to combine the input variables with the output response. Then, this model is optimized by the nonlinear optimization function of the genetic algorithm. Finally, a feeding system optimization of a steel traveling wheel is conducted. No shrinkage cavities and porosities are induced through the optimization. Compared to the initial design scheme, the process yield is increased by 4.1% and the volume of the riser is decreased by 5.48×106 mm3.
Dipla, Konstantina; Kousoula, Dimitra; Zafeiridis, Andreas; Karatrantou, Konstantina; Nikolaidis, Michalis G; Kyparos, Antonios; Gerodimos, Vassilis; Vrabas, Ioannis S
What is the central question of this study? In obesity, the exaggerated blood pressure response to voluntary exercise is linked to hypertension, yet the mechanisms are not fully elucidated. We examined whether involuntary contractions elicit greater haemodynamic responses and altered neural control of blood pressure in normotensive obese versus lean women. What is the main finding and its importance? During involuntary contractions induced by whole-body vibration, there were augmented blood pressure and spontaneous baroreflex responses in obese compared with lean women. This finding is suggestive of an overactive mechanoreflex in the exercise-induced hypertensive response in obesity. Passive contractions did not elicit differential heart rate responses in obese compared with lean women, implying other mechanisms for the blunted heart rate response reported during voluntary exercise in obesity. In obesity, the exaggerated blood pressure (BP) response to exercise is linked to hypertension, yet the mechanisms are not fully elucidated. In this study, we examined whether involuntary mechanical oscillations, induced by whole-body vibration (WBV), elicit greater haemodynamic responses and altered neural control of BP in obese versus lean women. Twenty-two normotensive, premenopausal women (12 lean and 10 obese) randomly underwent a passive WBV (25 Hz) and a control protocol (similar posture without WVB). Beat-by-beat BP, heart rate, stroke volume, systemic vascular resistance, cardiac output, parasympathetic output (evaluated by heart rate variability) and spontaneous baroreceptor sensitivity (sBRS) were assessed. We found that during WBV, obese women exhibited an augmented systolic BP response compared with lean women that was correlated with body fat percentage (r = 0.77; P obese versus lean women, associated with a greater stroke volume index in obese women. Involuntary contractions did not elicit a differential magnitude of responses in heart rate, heart rate
Burger, Kyle S; Stice, Eric
Although soft drinks are heavily advertised, widely consumed, and have been associated with obesity, little is understood regarding neural responsivity to soft drink intake, anticipated intake, and advertisements. Functional MRI was used to assess examine neural response to carbonated soft drink intake, anticipated intake and advertisement exposure as well as milkshake intake in 27 adolescents that varied on soft drink consumer status. Intake and anticipated intake of carbonated Coke® activated regions implicated in gustatory, oral somatosensory, and reward processing, yet high-fat/sugar milkshake intake elicited greater activation in these regions vs. Coke intake. Advertisements highlighting the Coke product vs. nonfood control advertisements, but not the Coke logo, activated gustatory and visual brain regions. Habitual Coke consumers vs. nonconsumers showed greater posterior cingulate responsivity to Coke logo ads, suggesting that the logo is a conditioned cue. Coke consumers exhibited less ventrolateral prefrontal cortex responsivity during anticipated Coke intake relative to nonconsumers. Results indicate that soft drinks activate reward and gustatory regions, but are less potent in activating these regions than high-fat/sugar beverages, and imply that habitual soft drink intake promotes hyper-responsivity of regions encoding salience/attention toward brand specific cues and hypo-responsivity of inhibitory regions while anticipating intake. Copyright © 2013 The Obesity Society.
Maupin, Angela N; Rutherford, Helena J V; Landi, Nicole; Potenza, Marc N; Mayes, Linda C
Understanding the maternal neural response to infant affective cues has important implications for parent-child relationships. The current study employed event-related potentials (ERPs) to examine patterns in mothers' responses to infant affective cues, and evaluated the influence of maternal experience, defined by parity (i.e., the number of children a mother has) on ERP responses. Eighty-three mothers, three months postpartum, viewed photographs of displays of infant emotional faces (sad or happy) and listened to infant cries of different distress levels and a control tone. Maternal neural response was modulated by the emotional content of the auditory stimulus, as indexed by the N100 amplitude and latency. However, response to infant faces was not modulated by the emotional content of the stimuli as indexed by the N170. Neither N100 nor N170 were affected by parity. Maternal engagement with auditory stimuli, as indexed by the P300, was modulated by the emotional content of the cry and was affected by parity. A similar parity effect was observed for the P300 response to infant faces. Results suggest that parity may play an important role at later stages of maternal infant cue perception.
Miskowiak, K W; Glerup, L; Vestbo, C; Harmer, C J; Reinecke, A; Macoveanu, J; Siebner, H R; Kessing, L V; Vinberg, M
Negative cognitive bias and aberrant neural processing of emotional faces are trait-marks of depression. Yet it is unclear whether these changes constitute an endophenotype for depression and are also present in healthy individuals with hereditary risk for depression. Thirty healthy, never-depressed monozygotic (MZ) twins with a co-twin history of depression (high risk group: n = 13) or without co-twin history of depression (low-risk group: n = 17) were enrolled in a functional magnetic resonance imaging (fMRI) study. During fMRI, participants viewed fearful and happy faces while performing a gender discrimination task. After the scan, they were given a faces dot-probe task, a facial expression recognition task and questionnaires assessing mood, personality traits and coping strategies. High-risk twins showed increased neural response to happy and fearful faces in dorsal anterior cingulate cortex (ACC), dorsomedial prefrontal cortex (dmPFC), pre-supplementary motor area and occipito-parietal regions compared to low-risk twins. They also displayed stronger negative coupling between amygdala and pregenual ACC, dmPFC and temporo-parietal regions during emotional face processing. These task-related changes in neural responses in high-risk twins were accompanied by impaired gender discrimination performance during face processing. They also displayed increased attention vigilance for fearful faces and were slower at recognizing facial expressions relative to low-risk controls. These effects occurred in the absence of differences between groups in mood, subjective state or coping. Different neural response and functional connectivity within fronto-limbic and occipito-parietal regions during emotional face processing and enhanced fear vigilance may be key endophenotypes for depression.
Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen
This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.
Full Text Available For humans and animals, the ability to discriminate speech and conspecific vocalizations is an important physiological assignment of the auditory system. To reveal the underlying neural mechanism, many electrophysiological studies have investigated the neural responses of the auditory cortex to conspecific vocalizations in monkeys. The data suggest that vocalizations may be hierarchically processed along an anterior/ventral stream from the primary auditory cortex (A1 to the ventral prefrontal cortex. To date, the organization of vocalization processing has not been well investigated in the auditory cortex of other mammals. In this study, we examined the spike activities of single neurons in two early auditory cortical regions with different anteroposterior locations: anterior auditory field (AAF and posterior auditory field (PAF in awake cats, as the animals were passively listening to forward and backward conspecific calls (meows and human vowels. We found that the neural response patterns in PAF were more complex and had longer latency than those in AAF. The selectivity for different vocalizations based on the mean firing rate was low in both AAF and PAF, and not significantly different between them; however, more vocalization information was transmitted when the temporal response profiles were considered, and the maximum transmitted information by PAF neurons was higher than that by AAF neurons. Discrimination accuracy based on the activities of an ensemble of PAF neurons was also better than that of AAF neurons. Our results suggest that AAF and PAF are similar with regard to which vocalizations they represent but differ in the way they represent these vocalizations, and there may be a complex processing stream between them.
grid, using an Advanced Brain Monitoring (ABM) ×24 system configured with the single-trial event - related potential (ERP) sensor strip and operating...ROC curve BCI brain-computer interface EEG electroencephalogram ERP event - related potential EVUS estimated volume under the surface FOV field of...stations. 15. SUBJECT TERMS rapid serial visual presentation, RSVP, EEG, neural classification, P300 , brain-computer interface 16. SECURITY
Feng, C; Lori, A; Waldman, I. D; Binder, E. B; Haroon, E; Rilling, J. K
.... However, OT effects are often heterogeneous across individuals. Here we explore individual differences in OT effects on the neural response to social cooperation as a function of the rs53576 polymorphism of the oxytocin receptor gene ( OXTR...
Vinther, Kasper; Green, Torben; Østergaard, Søren
This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...
Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient
unit, the CPU , whereas neural networks utilize the effects of many, simple processing elements. Traditional computing is done in a step-by-step, serial...Nielsen Neurocomputers (HNC). The ANZA-Plus coprocessor is part of an 80386 -based computer system which is optimized for training and executing neural...host computer for this program is a Zenith 386/16 system running under the DOS 3.31 operating system. The 80386 microprocessor in this machine operates
Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian
For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.
Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.
Full Text Available Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.
Reed, Darrin K.; Tóth, Brigitta; Best, Virginia; Majdak, Piotr; Colburn, H. Steven; Shinn-Cunningham, Barbara
Studies of auditory looming bias have shown that sources increasing in intensity are more salient than sources decreasing in intensity. Researchers have argued that listeners are more sensitive to approaching sounds compared with receding sounds, reflecting an evolutionary pressure. However, these studies only manipulated overall sound intensity; therefore, it is unclear whether looming bias is truly a perceptual bias for changes in source distance, or only in sound intensity. Here we demonstrate both behavioral and neural correlates of looming bias without manipulating overall sound intensity. In natural environments, the pinnae induce spectral cues that give rise to a sense of externalization; when spectral cues are unnatural, sounds are perceived as closer to the listener. We manipulated the contrast of individually tailored spectral cues to create sounds of similar intensity but different naturalness. We confirmed that sounds were perceived as approaching when spectral contrast decreased, and perceived as receding when spectral contrast increased. We measured behavior and electroencephalography while listeners judged motion direction. Behavioral responses showed a looming bias in that responses were more consistent for sounds perceived as approaching than for sounds perceived as receding. In a control experiment, looming bias disappeared when spectral contrast changes were discontinuous, suggesting that perceived motion in distance and not distance itself was driving the bias. Neurally, looming bias was reflected in an asymmetry of late event-related potentials associated with motion evaluation. Hence, both our behavioral and neural findings support a generalization of the auditory looming bias, representing a perceptual preference for approaching auditory objects. PMID:28827336
Baumgartner, Robert; Reed, Darrin K; Tóth, Brigitta; Best, Virginia; Majdak, Piotr; Colburn, H Steven; Shinn-Cunningham, Barbara
Studies of auditory looming bias have shown that sources increasing in intensity are more salient than sources decreasing in intensity. Researchers have argued that listeners are more sensitive to approaching sounds compared with receding sounds, reflecting an evolutionary pressure. However, these studies only manipulated overall sound intensity; therefore, it is unclear whether looming bias is truly a perceptual bias for changes in source distance, or only in sound intensity. Here we demonstrate both behavioral and neural correlates of looming bias without manipulating overall sound intensity. In natural environments, the pinnae induce spectral cues that give rise to a sense of externalization; when spectral cues are unnatural, sounds are perceived as closer to the listener. We manipulated the contrast of individually tailored spectral cues to create sounds of similar intensity but different naturalness. We confirmed that sounds were perceived as approaching when spectral contrast decreased, and perceived as receding when spectral contrast increased. We measured behavior and electroencephalography while listeners judged motion direction. Behavioral responses showed a looming bias in that responses were more consistent for sounds perceived as approaching than for sounds perceived as receding. In a control experiment, looming bias disappeared when spectral contrast changes were discontinuous, suggesting that perceived motion in distance and not distance itself was driving the bias. Neurally, looming bias was reflected in an asymmetry of late event-related potentials associated with motion evaluation. Hence, both our behavioral and neural findings support a generalization of the auditory looming bias, representing a perceptual preference for approaching auditory objects.
Yang, Shufan; Wu, Qiang; Li, Renfa
Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.
Liu, Zhong-Xu; Lishak, Victoria; Tannock, Rosemary; Woltering, Steven
Working memory and response control are conceptualized as functions that are part of a closely connected and integrated executive function system mediated by the prefrontal cortex and other related brain structures. In the present paper, we asked whether effects of intensive and adaptive computerized working memory training (CWMT) would generalize to enhancements in response control at behavioral and neural levels. A total of 135 postsecondary students with Attention-Deficit/Hyperactivity Disorder (ADHD), a condition associated with executive function impairments, were randomized into a Standard-length CWMT (45-min /session, 25 sessions), Shortened-length CWMT (15min/session, 25 sessions), and a waitlist group. Both training groups received CWMT for 5 days a week for 5 weeks long. All participants completed a Go-Nogo task while neural activity was measured using Electroencephalography (EEG), before and after CWMT. Behavioral results showed trend level evidence (p=0.061) for benefits of CWMT on response control (i.e., improved accuracy of Go responses). Among several neural measures results showed statistically significant changes after CWMT only for the Go trial ERP N2 and P3 in frontal electrodes (p=0.039 and 0.001, respectively). However, given the lack of relationship between behavioral and neural changes and especially the clear lack of predicted does effects (i.e., standard length > short length > control), we conclude that there is no convincing evidence that the working memory training per se changes neural activation patterns in untrained executive functions. The positive finding of general training related changes in this study should have no clinical implications, but may contribute to the literature in better understanding the relationship between neural plasticity and transfer. Published by Elsevier Ltd.
Lueken, Ulrike; Straube, Benjamin; Konrad, Carsten; Wittchen, Hans-Ulrich; Ströhle, Andreas; Wittmann, André; Pfleiderer, Bettina; Uhlmann, Christina; Arolt, Volker; Jansen, Andreas; Kircher, Tilo
Although exposure-based cognitive-behavioral therapy (CBT) is an effective treatment option for panic disorder with agoraphobia, the neural substrates of treatment response remain unknown. Evidence suggests that panic disorder with agoraphobia is characterized by dysfunctional safety signal processing. Using fear conditioning as a neurofunctional probe, the authors investigated neural baseline characteristics and neuroplastic changes after CBT that were associated with treatment outcome in patients with panic disorder with agoraphobia. Neural correlates of fear conditioning and extinction were measured using functional MRI before and after a manualized CBT program focusing on behavioral exposure in 49 medication-free patients with a primary diagnosis of panic disorder with agoraphobia. Treatment response was defined as a reduction exceeding 50% in Hamilton Anxiety Rating Scale scores. At baseline, nonresponders exhibited enhanced activation in the right pregenual anterior cingulate cortex, the hippocampus, and the amygdala in response to a safety signal. While this activation pattern partly resolved in nonresponders after CBT, successful treatment was characterized by increased right hippocampal activation when processing stimulus contingencies. Treatment response was associated with an inhibitory functional coupling between the anterior cingulate cortex and the amygdala that did not change over time. This study identified brain activation patterns associated with treatment response in patients with panic disorder with agoraphobia. Altered safety signal processing and anterior cingulate cortex-amygdala coupling may indicate individual differences among these patients that determine the effectiveness of exposure-based CBT and associated neuroplastic changes. Findings point to brain networks by which successful CBT in this patient population is mediated.
Bunford, Nora; Kujawa, Autumn; Fitzgerald, Kate D; Swain, James E; Hanna, Gregory L; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S; Phan, K Luan
Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7-19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post- CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment.
Full Text Available Infants' sensitivity to ostensive signals, such as direct eye contact and infant-directed speech, is well documented in the literature. We investigated how infants interpret such signals by assessing common processing mechanisms devoted to them and by measuring neural responses to their compounds. In Experiment 1, we found that ostensive signals from different modalities display overlapping electrophysiological activity in 5-month-old infants, suggesting that these signals share neural processing mechanisms independently of their modality. In Experiment 2, we found that the activation to ostensive signals from different modalities is not additive to each other, but rather reflects the presence of ostension in either stimulus stream. These data support the thesis that ostensive signals obligatorily indicate to young infants that communication is directed to them.
Ceylan, Halil; Gopalakrishnan, Kasthurirangan; Birkan Bayrak, Mustafa; Guclu, Alper
The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement
Carrus, Elisa; Pearce, Marcus T; Bhattacharya, Joydeep
Current behavioural and electrophysiological evidence suggests that music and language syntactic processing depends on at least partly shared neural resources. Existing studies using a simultaneous presentation paradigm are limited to the effects of violations of harmonic structure in Western tonal music on processing of single syntactic or semantic violations. Because melody is a universal property of music as it is emphasized also by non-western musical traditions, it is fundamental to investigate interactions between melodic expectation and language processing. The present study investigates the effect of melodically unexpected notes on neural responses elicited by linguistic violations. Sentences with or without a violation in the last word were presented on screen simultaneously with melodies whose last note had a high- or low-probability, as estimated by a computational model of melodic expectation. Violations in language could be syntactic, semantic or combined. The electroencephalogram (EEG) was recorded while participants occasionally responded to language stimuli. Confirming previous studies, low-probability notes elicited an enhanced N1 compared to high-probability notes. Further, syntactic violations elicited a left anterior negativity (LAN) and P600 component, and semantic violations elicited an N400. Combined violations elicited components which resembled neural responses to both syntactic and semantic incongruities. The LAN amplitude was decreased when language syntactic violations were presented simultaneously with low-probability notes compared to when they were presented with high-probability notes. The N400 was not influenced by the note-probability. These findings show support for the neural interaction between language and music processing, including novel evidence for melodic processing which can be incorporated in a computational framework of melodic expectation. Copyright © 2012 Elsevier Ltd. All rights reserved.
Liu, Taosheng; Cable, Dylan; Gardner, Justin L
Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums of groups of neurons (channels) tuned for visual stimulus properties. Inverting these models to form predicted channel responses from population measurements in humans seemingly offers the potential to infer neuronal tuning properties. Here, we test the ability to make inferences about neural tuning width from inverted encoding models. We examined contrast invariance of orientation selectivity in human V1 (both sexes) and found that inverting the encoding model resulted in channel response functions that became broader with lower contrast, thus apparently violating contrast invariance. Simulations showed that this broadening could be explained by contrast-invariant single-unit tuning with the measured decrease in response amplitude at lower contrast. The decrease in response lowers the signal-to-noise ratio of population responses that results in poorer population representation of orientation. Simulations further showed that increasing signal to noise makes channel response functions less sensitive to underlying neural tuning width, and in the limit of zero noise will reconstruct the channel function assumed by the model regardless of the bandwidth of single units. We conclude that our data are consistent with contrast-invariant orientation tuning in human V1. More generally, our results demonstrate that population selectivity measures obtained by encoding models can deviate substantially from the behavior of single units because they conflate neural tuning width and noise and are therefore better used to estimate the uncertainty of decoded stimulus properties.SIGNIFICANCE STATEMENT It is widely recognized that perceptual experience arises from large populations of neurons, rather than a few single units. Yet, much theory and experiment have examined links between single
Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)
An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)
Kam, Julia W Y; Boyd, Lara A; Hsu, Chun L; Liu-Ambrose, Teresa; Handy, Todd C; Lim, Howard J; Hayden, Sherri; Campbell, Kristin L
While impairments in executive functions have been reported in breast cancer survivors (BCS) who have undergone adjuvant chemotherapy, only a limited number of functional neuroimaging studies have associated alterations in cerebral activity with executive functions deficits in BCS. Using fMRI, the current study assessed the neural basis underlying a specific facet of executive function, namely prepotent response inhibition. 12 BCS who self-reported cognitive problems up to 3 years following cancer treatment and 12 female healthy comparisons (HC) performed the Stroop task. We compared their neural activation between the incongruent and neutral experimental conditions. Relative to the HC group, BCS showed lower blood-oxygen level dependent signal in several frontal regions, including the anterior cingulate cortex, a region critical for response inhibition. Our data indicates reduced neural activation in BCS during a prepotent response inhibition task, providing support for the prevailing notion of neural alterations observed in BCS treated with chemotherapy.
Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
Kim, Junkyeong; Lee, Chaggil; Park, Seunghee
Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.
Dedovic, Katarina; Duchesne, Annie; Engert, Veronika; Lue, Sonja Damika; Andrews, Julie; Efanov, Simona I; Beaudry, Thomas; Pruessner, Jens C
This study aimed to identify vulnerability patterns in psychological, physiological and neural responses to mild psychosocial challenge in a population that is at a direct risk of developing depression, but who has not as yet succumbed to the full clinical syndrome. A group of healthy and a group of subclinically depressed participants underwent a modified Montreal Imaging Stress task (MIST), a mild neuroimaging psychosocial task and completed state self-esteem and mood measures. Cortisol levels were assessed throughout the session. All participants showed a decrease in performance self-esteem levels following the MIST. Yet, the decline in performance self-esteem levels was associated with increased levels of anxiety and confusion in the healthy group, but increased levels of depression in the subclinical group, following the MIST. The subclinical group showed overall lower cortisol levels compared with the healthy group. The degree of change in activity in the subgenual anterior cingulate cortex in response to negative evaluation was associated with increased levels of depression in the whole sample. Findings suggest that even in response to a mild psychosocial challenge, those individuals vulnerable to depression already show important maladaptive response patterns at psychological and neural levels. The findings point to important targets for future interventions. © The Author (2013). Published by Oxford University Press. For Permissions, please email: email@example.com.
Guyer, Amanda E; Jarcho, Johanna M; Pérez-Edgar, Koraly; Degnan, Kathryn A; Pine, Daniel S; Fox, Nathan A; Nelson, Eric E
Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children's caregiving context. The convergence of a child's temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The present study used functional neuroimaging to assess the moderating effects of different parenting styles on neural response to peer rejection in two groups of adolescents characterized by their early childhood temperament (M(age) = 17.89 years, N = 39, 17 males, 22 females; 18 with BI; 21 without BI). The moderating effects of authoritarian and authoritative parenting styles were examined in three brain regions linked with social anxiety: ventrolateral prefrontal cortex (vlPFC), striatum, and amygdala. In youth characterized with BI in childhood, but not in those without BI, diminished responses to peer rejection in vlPFC were associated with higher levels of authoritarian parenting. In contrast, all youth showed decreased caudate response to peer rejection at higher levels of authoritative parenting. These findings indicate that BI in early life relates to greater neurobiological sensitivity to variance in parenting styles, particularly harsh parenting, in late adolescence. These results are discussed in relation to biopsychosocial models of development.
Grummett, T S; Leibbrandt, R E; Lewis, T W; DeLosAngeles, D; Powers, D M W; Willoughby, J O; Pope, K J; Fitzgibbon, S P
Electroencephalography (EEG) is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. We compared EEG recordings obtained from three systems that are inexpensive, wireless, and/or dry (no gel), against recordings made with a traditional, research-grade EEG system, in order to investigate the ability of these 'non-traditional' systems to produce recordings of comparable quality to a research-grade system. The systems compared were: Emotiv EPOC (inexpensive and wireless), B-Alert (wireless), g.Sahara (dry) and g.HIamp (research-grade). We compared the ability of the systems to demonstrate five well-studied neural phenomena: (1) enhanced alpha activity with eyes closed versus open; (2) visual steady-state response (VSSR); (3) mismatch negativity; (4) P300; and (5) event-related desynchronization/synchronization. All systems measured significant alpha augmentation with eye closure, and were able to measure VSSRs (although these were smaller with g.Sahara). The B-Alert and g.Sahara were able to measure the three time-locked phenomena equivalently to the g.HIamp. The Emotiv EPOC did not have suitably located electrodes for two of the tasks and synchronization considerations meant that data from the time-locked tasks were not assessed. The results show that inexpensive, wireless, or dry systems may be suitable for experimental studies using EEG, depending on the research paradigm, and within the constraints imposed by their limited electrode placement and number.
Liu, Da; Li, Muguo
This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics ...
Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung
Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.
Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung
Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.
Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)
This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)
Full Text Available In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.
Chervyakov, Alexander V; Sinitsyn, Dmitry O; Piradov, Michael A
HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.
de Water, Erik; Mies, Gabry W; Ma, Ili; Mennes, Maarten; Cillessen, Antonius H N; Scheres, Anouk
We examined whether adolescents' neural responses to social exclusion and inclusion are influenced by their own popularity and acceptance and by the popularity of their excluders and includers. Accepted adolescents are highly prosocial. In contrast, popular adolescents, who are central and influential, show prosocial as well as antisocial behaviors, such as peer exclusion. Fifty-two 12-16 year-old adolescents underwent an functional magnetic resonance imaging (fMRI) scan while playing the ball-tossing game Cyberball in which they received or did not receive the ball from other virtual players. The other virtual players were described as either highly popular or average in popularity. Participants' own popularity and acceptance were assessed with peer nominations at school (n = 31). Participants' acceptance was positively correlated with activity of the dorsal anterior cingulate cortex (ACC) during exclusion. Participants' popularity was positively associated with ventral striatum and medial prefrontal cortex activity during exclusion, but only when the excluders were popular virtual players. Participants showed increased rostral ACC activation to inclusion by players who were average in popularity. These findings indicate that peer status plays an important role in adolescents' neural processing of social exclusion and inclusion. Moreover, these findings underscore that popularity and acceptance are distinct types of high peer status in adolescence, with not only distinct behavioral correlates, but also distinct neural correlates. Copyright © 2017 Elsevier Ltd. All rights reserved.
Li, Bo; Rui, Xiaoting
Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.
Background Social anxiety disorder (SAD) is thought to involve deficits in emotion regulation, and more specifically, deficits in cognitive reappraisal. However, evidence for such deficits is mixed. Methods Using functional magnetic resonance imaging (fMRI) of blood oxygen-level dependent (BOLD) signal, we examined reappraisal-related behavioral and neural responses in 27 participants with generalized SAD and 27 healthy controls (HC) during three socio-emotional tasks: (1) looming harsh faces (Faces); (2) videotaped actors delivering social criticism (Criticism); and (3) written autobiographical negative self-beliefs (Beliefs). Results Behaviorally, compared to HC, participants with SAD had lesser reappraisal-related reduction in negative emotion in the Beliefs task. Neurally, compared to HC, participants with SAD had lesser BOLD responses in reappraisal-related brain regions when reappraising faces, in visual and attention related regions when reappraising criticism, and in the left superior temporal gyrus when reappraising beliefs. Examination of the temporal dynamics of BOLD responses revealed late reappraisal-related increased responses in HC, compared to SAD. In addition, the dorsomedial prefrontal cortex (DMPFC), which showed reappraisal-related increased activity in both groups, had similar temporal dynamics in SAD and HC during the Faces and Criticism tasks, but greater late response increases in HC, compared to SAD, during the Beliefs task. Reappraisal-related greater late DMPFC responses were associated with greater percent reduction in negative emotion ratings in SAD patients. Conclusions These results suggest a dysfunction of cognitive reappraisal in SAD patients, with overall reduced late brain responses in prefrontal regions, particularly when reappraising faces. Decreased late activity in the DMPFC might be associated with deficient reappraisal and greater negative reactivity. Trial registration ClinicalTrials.gov identifier: NCT00380731 PMID
Keller, James M; Fogel, David B
This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...
Long, Lijun; Zhao, Jun
This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
Clinard, Christopher G; Hodgson, Sarah L; Scherer, Mary Ellen
The binaural masking level difference (BMLD) is an auditory phenomenon where binaural tone-in-noise detection is improved when the phase of either signal or noise is inverted in one of the ears (SπNo or SoNπ, respectively), relative to detection when signal and noise are in identical phase at each ear (SoNo). Processing related to BMLDs and interaural time differences has been confirmed in the auditory brainstem of non-human mammals; in the human auditory brainstem, phase-locked neural responses elicited by BMLD stimuli have not been systematically examined across signal-to-noise ratio. Behavioral and physiological testing was performed in three binaural stimulus conditions: SoNo, SπNo, and SoNπ. BMLDs at 500 Hz were obtained from 14 young, normal-hearing adults (ages 21-26). Physiological BMLDs used the frequency-following response (FFR), a scalp-recorded auditory evoked potential dependent on sustained phase-locked neural activity; FFR tone-in-noise detection thresholds were used to calculate physiological BMLDs. FFR BMLDs were significantly smaller (poorer) than behavioral BMLDs, and FFR BMLDs did not reflect a physiological release from masking, on average. Raw FFR amplitude showed substantial reductions in the SπNo condition relative to SoNo and SoNπ conditions, consistent with negative effects of phase summation from left and right ear FFRs. FFR amplitude differences between stimulus conditions (e.g., SoNo amplitude-SπNo amplitude) were significantly predictive of behavioral SπNo BMLDs; individuals with larger amplitude differences had larger (better) behavioral B MLDs and individuals with smaller amplitude differences had smaller (poorer) behavioral B MLDs. These data indicate a role for sustained phase-locked neural activity in BMLDs of humans and are the first to show predictive relationships between behavioral BMLDs and human brainstem responses.
Shin, Na Young; Park, Hye Yoon; Jung, Wi Hoon; Park, Jin Woo; Yun, Je-Yeon; Jang, Joon Hwan; Kim, Sung Nyun; Han, Hyun Jung; Kim, So-Yeon; Kang, Do-Hyung; Kwon, Jun Soo
Impaired facial emotion recognition is a core deficit in schizophrenia. Oxytocin has been shown to improve social perception in patients with schizophrenia; however, the effect of oxytocin on the neural activity underlying facial emotion recognition has not been investigated. This study was aimed to assess the effect of a single dose of intranasal oxytocin on brain activity in patients with schizophrenia using an implicit facial emotion-recognition paradigm. Sixteen male patients with schizophrenia and 16 age-matched healthy male control subjects participated in a randomized, double-blind, placebo-controlled crossover trial at Seoul National University Hospital. Delivery of a single dose of 40 IU intranasal oxytocin and the placebo was separated by 1 week. Drug conditions were compared by performing a region of interest (ROI) analysis of the bilateral amygdala on responses to the emotion recognition test. It was found that nasal spray decreased amygdala activity for fearful emotion and increased activity for happy faces. Further, oxytocin elicited differential effects between the patient and control groups. Intranasal oxytocin attenuated amygdala activity for emotional faces in patients with schizophrenia, whereas intranasal oxytocin significantly increased amygdala activity in healthy controls. Oxytocin-induced BOLD signal changes in amygdala in response to happy faces was related to attachment style in the control group. Our result provides new evidence of a modulatory effect of oxytocin on neural response to emotional faces for patients with schizophrenia. Future studies are needed to investigate the effectiveness of long-term treatment with intranasal oxytocin on neural activity in patients with schizophrenia.
Zeng, Y; Zhang, J; Yin, H; Pan, Y
Visual evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). In this paper, an adaptive noise cancellation with neural network-based fuzzy inference system (NNFIS) was used and the NNFIS was carefully designed to model the VEP signal. It is assumed that VEP responses can be modelled by NNFIS with the centres of its membership functions evenly distributed over time. The weights of NNFIS are adaptively determined by minimizing the variance of the error signal using the least mean squares (LMS) algorithm. As the NNFIS is dynamic to any change of VEP, the non-stationary characteristics of VEP can be tracked. Thus, this method should be able to track the VEP. Four sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method.
Full Text Available Application of neural networks technologies effectively decides the task of synthesis of origin of accident risk and gives out the vector of managing signals of network on incomplete and distorted information about the phenomena, events and processes which influence on safety flights.
In this paper, we proposed an architecture which uses the theory of artificial neural networks and business rules to correctly determine whether a customer is good or bad. In the first step, by using clustering algorithm, clients are segmented into groups with similar features. In the second step, decision trees are built based ...
Miskowiak, K W; Glerup, L; Vestbo, C
healthy, never-depressed monozygotic (MZ) twins with a co-twin history of depression (high risk group: n = 13) or without co-twin history of depression (low-risk group: n = 17) were enrolled in a functional magnetic resonance imaging (fMRI) study. During fMRI, participants viewed fearful and happy faces...... while performing a gender discrimination task. After the scan, they were given a faces dot-probe task, a facial expression recognition task and questionnaires assessing mood, personality traits and coping strategies. RESULTS: High-risk twins showed increased neural response to happy and fearful faces...
Will, G.J.; Van, Lier P.A.; Crone, E.A.; Guroglu, B.
This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12?15) who either had a stable accepted (n?=?27; 14 males) or a chronic rejected (n?=?19; 12 males) status among peers from age 6 to 12. Both groups of adolescents reported similar increases in distress after being excluded in a virtual ball-tossing game (Cyberball), but adolescents with a history of chronic peer rejection showed higher activity in brain reg...
Baseler, Heidi A; Harris, Richard J; Young, Andrew W; Andrews, Timothy J
Neural models of human face perception propose parallel pathways. One pathway (including posterior superior temporal sulcus, pSTS) is responsible for processing changeable aspects of faces such as gaze and expression, and the other pathway (including the fusiform face area, FFA) is responsible for relatively invariant aspects such as identity. However, to be socially meaningful, changes in expression and gaze must be tracked across an individual face. Our aim was to investigate how this is achieved. Using functional magnetic resonance imaging, we found a region in pSTS that responded more to sequences of faces varying in gaze and expression in which the identity was constant compared with sequences in which the identity varied. To determine whether this preferential response to same identity faces was due to the processing of identity in the pSTS or was a result of interactions between pSTS and other regions thought to code face identity, we measured the functional connectivity between face-selective regions. We found increased functional connectivity between the pSTS and FFA when participants viewed same identity faces compared with different identity faces. Together, these results suggest that distinct neural pathways involved in expression and identity interact to process the changeable features of the face in a socially meaningful way.
Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina
Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.
Garrido, Marta I; Rowe, Elise G; Halász, Veronika; Mattingley, Jason B
Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between 2 alternative computational models. The "Opposition" model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the "Interaction" model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modeling showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org.
Oldfield, Ronald G; Harris, Rayna M; Hofmann, Hans A
The ultimate-level factors that drive the evolution of mating systems have been well studied, but an evolutionarily conserved neural mechanism involved in shaping behaviour and social organization across species has remained elusive. Here, we review studies that have investigated the role of neural arginine vasopressin (AVP), vasotocin (AVT), and their receptor V1a in mediating variation in territorial behaviour. First, we discuss how aggression and territoriality are a function of population density in an inverted-U relationship according to resource defence theory, and how territoriality influences some mating systems. Next, we find that neural AVP, AVT, and V1a expression, especially in one particular neural circuit involving the lateral septum of the forebrain, are associated with territorial behaviour in males of diverse species, most likely due to their role in enhancing social cognition. Then we review studies that examined multiple species and find that neural AVP, AVT, and V1a expression is associated with territory size in mammals and fishes. Because territoriality plays an important role in shaping mating systems in many species, we present the idea that neural AVP, AVT, and V1a expression that is selected to mediate territory size may also influence the evolution of different mating systems. Future research that interprets proximate-level neuro-molecular mechanisms in the context of ultimate-level ecological theory may provide deep insight into the brain-behaviour relationships that underlie the diversity of social organization and mating systems seen across the animal kingdom.
Zhong, Ziwei; Henry, Kenneth S; Heinz, Michael G
People with sensorineural hearing loss often have substantial difficulty understanding speech under challenging listening conditions. Behavioral studies suggest that reduced sensitivity to the temporal structure of sound may be responsible, but underlying neurophysiological pathologies are incompletely understood. Here, we investigate the effects of noise-induced hearing loss on coding of envelope (ENV) structure in the central auditory system of anesthetized chinchillas. ENV coding was evaluated noninvasively using auditory evoked potentials recorded from the scalp surface in response to sinusoidally amplitude modulated tones with carrier frequencies of 1, 2, 4, and 8 kHz and a modulation frequency of 140 Hz. Stimuli were presented in quiet and in three levels of white background noise. The latency of scalp-recorded ENV responses was consistent with generation in the auditory midbrain. Hearing loss amplified neural coding of ENV at carrier frequencies of 2 kHz and above. This result may reflect enhanced ENV coding from the periphery and/or an increase in the gain of central auditory neurons. In contrast to expectations, hearing loss was not associated with a stronger adverse effect of increasing masker intensity on ENV coding. The exaggerated neural representation of ENV information shown here at the level of the auditory midbrain helps to explain previous findings of enhanced sensitivity to amplitude modulation in people with hearing loss under some conditions. Furthermore, amplified ENV coding may potentially contribute to speech perception problems in people with cochlear hearing loss by acting as a distraction from more salient acoustic cues, particularly in fluctuating backgrounds. Copyright © 2013 Elsevier B.V. All rights reserved.
Kim Elmer K
Full Text Available Abstract Background The next generation of prosthetic limbs will restore sensory feedback to the nervous system by mimicking how skin mechanoreceptors, innervated by afferents, produce trains of action potentials in response to compressive stimuli. Prior work has addressed building sensors within skin substitutes for robotics, modeling skin mechanics and neural dynamics of mechanotransduction, and predicting response timing of action potentials for vibration. The effort here is unique because it accounts for skin elasticity by measuring force within simulated skin, utilizes few free model parameters for parsimony, and separates parameter fitting and model validation. Additionally, the ramp-and-hold, sustained stimuli used in this work capture the essential features of the everyday task of contacting and holding an object. Methods This systems integration effort computationally replicates the neural firing behavior for a slowly adapting type I (SAI afferent in its temporally varying response to both intensity and rate of indentation force by combining a physical force sensor, housed in a skin-like substrate, with a mathematical model of neuronal spiking, the leaky integrate-and-fire. Comparison experiments were then conducted using ramp-and-hold stimuli on both the spiking-sensor model and mouse SAI afferents. The model parameters were iteratively fit against recorded SAI interspike intervals (ISI before validating the model to assess its performance. Results Model-predicted spike firing compares favorably with that observed for single SAI afferents. As indentation magnitude increases (1.2, 1.3, to 1.4 mm, mean ISI decreases from 98.81 ± 24.73, 54.52 ± 6.94, to 41.11 ± 6.11 ms. Moreover, as rate of ramp-up increases, ISI during ramp-up decreases from 21.85 ± 5.33, 19.98 ± 3.10, to 15.42 ± 2.41 ms. Considering first spikes, the predicted latencies exhibited a decreasing trend as stimulus rate increased, as is
Liu, Hua-Kuang; Diep, J.; Huang, K.
Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.
Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.
A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.
Full Text Available This paper deals with the design of sliding mode control and neural network compensation for a sensorless permanent magnet synchronous motor (PMSM controlled system that is able to improve both power consumption and speed response performance. The position sensor of PMSM is unreliable in harsh environments. Therefore, the sensorless control technique is widely proposed in industry. A sliding mode observer can estimate the rotor angle and has the robustness to load disturbance and parameter variations. However, the sliding mode observer is not conducive to standstill and low speed conditions because the amplitude of the back EMF is almost zero. As a result, this paper combines an iterative sliding mode observer (ISMO and neural networks (NNs as an angle compensator to improve the above problems. A dsPIC30F6010A-based PMSM sensorless drive system is implemented to validate the proposed algorithm. The simulation and experimental results prove its effectiveness.
Harriet A Allen
response to parental teaching and modelling of behaviour. Parental restrictive feeding and parental teaching and modelling affected neural responses to food cues in different ways, depending on motivations and diagnoses, illustrating a social influence on neural responses to food cues.
Allen, Harriet A; Chambers, Alison; Blissett, Jacqueline; Chechlacz, Magdalena; Barrett, Timothy; Higgs, Suzanne; Nouwen, Arie
parental teaching and modelling of behaviour. Parental restrictive feeding and parental teaching and modelling affected neural responses to food cues in different ways, depending on motivations and diagnoses, illustrating a social influence on neural responses to food cues.
Mascaro, Jennifer S; Hackett, Patrick D; Rilling, James K
Despite the well-documented importance of paternal caregiving for positive child development, little is known about the neural changes that accompany the transition to fatherhood in humans, or about how changes in hormone levels affect paternal brain function. We compared fathers of children aged 1-2 with non-fathers in terms of hormone levels (oxytocin and testosterone), neural responses to child picture stimuli, and neural responses to visual sexual stimuli. Compared to non-fathers, fathers had significantly higher levels of plasma oxytocin and lower levels of plasma testosterone. In response to child picture stimuli, fathers showed stronger activation than non-fathers within regions important for face emotion processing (caudal middle frontal gyrus [MFG]), mentalizing (temporo-parietal junction [TPJ]) and reward processing (medial orbitofrontal cortex [mOFC]). On the other hand, non-fathers had significantly stronger neural responses to sexually provocative images in regions important for reward and approach-related motivation (dorsal caudate and nucleus accumbens). Testosterone levels were negatively correlated with responses to child stimuli in the MFG. Surprisingly, neither testosterone nor oxytocin levels predicted neural responses to sexual stimuli. Our results suggest that the decline in testosterone that accompanies the transition to fatherhood may be important for augmenting empathy toward children. Copyright © 2014 Elsevier Ltd. All rights reserved.
Ereifej, Evon S.
Neural electrode devices hold great promise to help people with the restoration of lost functions, however, research is lacking in the biomaterial design of a stable, long-term device. Current devices lack long term functionality, most have been found unable to record neural activity within weeks after implantation due to the development of glial scar tissue (Polikov et al., 2006; Zhong and Bellamkonda, 2008). The long-term effect of chronically implanted electrodes is the formation of a glial scar made up of reactive astrocytes and the matrix proteins they generate (Polikov et al., 2005; Seil and Webster, 2008). Scarring is initiated when a device is inserted into brain tissue and is associated with an inflammatory response. Activated astrocytes are hypertrophic, hyperplastic, have an upregulation of intermediate filaments GFAP and vimentin expression, and filament formation (Buffo et al., 2010; Gervasi et al., 2008). Current approaches towards inhibiting the initiation of glial scarring range from altering the geometry, roughness, size, shape and materials of the device (Grill et al., 2009; Kotov et al., 2009; Kotzar et al., 2002; Szarowski et al., 2003). Literature has shown that surface topography modifications can alter cell alignment, adhesion, proliferation, migration, and gene expression (Agnew et al., 1983; Cogan et al., 2005; Cogan et al., 2006; Merrill et al., 2005). Thus, the goals of the presented work are to study the cellular response to biomaterials used in neural electrode fabrication and assess surface topography effects on minimizing astrogliosis. Initially, to examine astrocyte response to various materials used in neural electrode fabrication, astrocytes were cultured on platinum, silicon, PMMA, and SU-8 surfaces, with polystyrene as the control surface. Cell proliferation, viability, morphology and gene expression was measured for seven days in vitro. Results determined the cellular characteristics, reactions and growth rates of astrocytes
Castillo, Oscar; Kacprzyk, Janusz
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Park, Hyeong-Dong; Bernasconi, Fosco; Bello-Ruiz, Javier; Pfeiffer, Christian; Salomon, Roy; Blanke, Olaf
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
Mitchell, Jere H
During both dynamic (e.g., endurance) and static (e.g., strength) exercise there are exaggerated cardiovascular responses in hypertension. This includes greater increases in blood pressure, heart rate, and efferent sympathetic nerve activity than in normal controls. Two of the known neural factors that contribute to this abnormal cardiovascular response are the exercise pressor reflex (EPR) and functional sympatholysis. The EPR originates in contracting skeletal muscle and reflexly increases sympathetic efferent nerve activity to the heart and blood vessels as well as decreases parasympathetic efferent nerve activity to the heart. These changes in autonomic nerve activity cause an increase in blood pressure, heart rate, left ventricular contractility, and vasoconstriction in the arterial tree. However, arterial vessels in the contracting skeletal muscle have a markedly diminished vasoconstrictor response. The markedly diminished vasoconstriction in contracting skeletal muscle has been termed functional sympatholysis. It has been shown in hypertension that there is an enhanced EPR, including both its mechanoreflex and metaboreflex components, and an impaired functional sympatholysis. These conditions set up a positive feedback or vicious cycle situation that causes a progressively greater decrease in the blood flow to the exercising muscle. Thus these two neural mechanisms contribute significantly to the abnormal cardiovascular response to exercise in hypertension. In addition, exercise training in hypertension decreases the enhanced EPR, including both mechanoreflex and metaboreflex function, and improves the impaired functional sympatholysis. These two changes, caused by exercise training, improve the muscle blood flow to exercising muscle and cause a more normal cardiovascular response to exercise in hypertension. Copyright © 2017 the American Physiological Society.
Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as
Simons, Laura; Elman, Igor; Borsook, David
Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383
Bruce, Amanda S; Bruce, Jared M; Black, William R; Lepping, Rebecca J; Henry, Janice M; Cherry, Joseph Bradley C; Martin, Laura E; Papa, Vlad B; Davis, Ann M; Brooks, William M; Savage, Cary R
Branding and advertising have a powerful effect on both familiarity and preference for products, yet no neuroimaging studies have examined neural response to logos in children. Food advertising is particularly pervasive and effective in manipulating choices in children. The purpose of this study was to examine how healthy children's brains respond to common food and other logos. A pilot validation study was first conducted with 32 children to select the most culturally familiar logos, and to match food and non-food logos on valence and intensity. A new sample of 17 healthy weight children were then scanned using functional magnetic resonance imaging. Food logos compared to baseline were associated with increased activation in orbitofrontal cortex and inferior prefrontal cortex. Compared to non-food logos, food logos elicited increased activation in posterior cingulate cortex. Results confirmed that food logos activate some brain regions in children known to be associated with motivation. This marks the first study in children to examine brain responses to culturally familiar logos. Considering the pervasiveness of advertising, research should further investigate how children respond at the neural level to marketing.
Riem, Madelon M E; van IJzendoorn, Marinus H; De Carli, Pietro; Vingerhoets, Ad J J M; Bakermans-Kranenburg, Marian J
The current study examined behavioral and neural responses to infant and adult tears, taking into account childhood experiences with parental love-withdrawal. With functional MRI (fMRI), we measured neural reactivity to pictures of infants and adults with and without tears on their faces in nulliparous women with varying childhood experiences of maternal use of love withdrawal. Behavioral responses to infant and adult tears were measured with an approach-avoidance task. We found that individuals with experiences of love withdrawal showed less amygdala and insula reactivity to adult tears, but love withdrawal did not affect amygdala and insula reactivity to infant tears. During the approach-avoidance task, individuals responded faster to adult tears in the approach condition compared with the avoidance condition, indicating that adult tears facilitate approach behavior. Individuals responded faster to infant tears than to adult tears, regardless of approach or avoidance condition. Our findings suggest that infant tears are highly salient and may, therefore, overrule the effects of contextual and personal characteristics that influence the perception of adult crying. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Will, Geert-Jan; van Lier, Pol A C; Crone, Eveline A; Güroğlu, Berna
This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12-15) who either had a stable accepted (n = 27; 14 males) or a chronic rejected (n = 19; 12 males) status among peers from age 6 to 12. Both groups of adolescents reported similar increases in distress after being excluded in a virtual ball-tossing game (Cyberball), but adolescents with a history of chronic peer rejection showed higher activity in brain regions previously linked to the detection of, and the distress caused by, social exclusion. Specifically, compared with stably accepted adolescents, chronically rejected adolescents displayed: 1) higher activity in the dorsal anterior cingulate cortex (dACC) during social exclusion and 2) higher activity in the dACC and anterior prefrontal cortex when they were incidentally excluded in a social interaction in which they were overall included. These findings demonstrate that chronic childhood peer rejection is associated with heightened neural responses to social exclusion during adolescence, which has implications for understanding the processes through which peer rejection may lead to adverse effects on mental health over time.
Wang, Xiaoyan; Zheng, Li; Cheng, Xuemei; Li, Lin; Sun, Lining; Wang, Qianfeng; Guo, Xiuyan
People often take either the role of an actor or that of recipient in positive and negative interpersonal events when they interact with others. The present study investigated how the actor-recipient role affected the neural responses to self in emotional situations. Twenty-five participants were scanned while they were presented with positive and negative interpersonal events and were asked to rate the degree to which the actor/the recipient was that kind of person who caused the interpersonal event. Half of the trials were self-relevant events and the other half were other-relevant events. Results showed that people were more likely to isolate self from negative events when they played the role of actor relative to recipient. Pregenual anterior cingulate cortex (pgACC) and posterior dorsal anterior cingulate cortex (pdACC) were more active for self than other only in negative events. More importantly, also in negative interpersonal events, dorsal medial prefrontal cortex (dmPFC) showed greater self-related activations (self-other) when participants played the role of recipient relative to actor, while activities in orbitofrontal cortex (OFC) were greater for self than other only when the evaluation target played the role of recipient. These results showed that the actor-recipient role affected neural responses to self in emotional situations, especially when a recipient role was played in negative situations.
Full Text Available Previous studies have demonstrated that the brain responds differentially to others' gains and losses relative to one's own, moderated by social context factors such as competition and interpersonal relationships. In the current study, we tested the hypothesis that the neural response to others' outcomes could be modulated by a short-term induced affective preference. We engaged 17 men and 18 women in a social-exchange game, in which two confederates played fairly or unfairly. Both men and women rated the fair player as likable and the unfair players as unlikable. Afterwards, ERPs were recorded while participants observed each confederates playing a gambling game individually. This study examines feedback related negativity (FRN, an ERP component sensitive to negative feedback. ANOVA showed a significant interaction in which females but not males displayed stronger FRNs when observing likable players' outcomes compared to unlikable ones'. However, males did not respond differently under either circumstance. These findings suggest that, at least in females, the neural response is influenced by a short-term induced affective preference.
Wang, Yang; Qu, Chen; Luo, Qiuling; Qu, Lulu; Li, Xuebing
Previous studies have demonstrated that the brain responds differentially to others' gains and losses relative to one's own, moderated by social context factors such as competition and interpersonal relationships. In the current study, we tested the hypothesis that the neural response to others' outcomes could be modulated by a short-term induced affective preference. We engaged 17 men and 18 women in a social-exchange game, in which two confederates played fairly or unfairly. Both men and women rated the fair player as likable and the unfair players as unlikable. Afterwards, ERPs were recorded while participants observed each confederates playing a gambling game individually. This study examines feedback related negativity (FRN), an ERP component sensitive to negative feedback. ANOVA showed a significant interaction in which females but not males displayed stronger FRNs when observing likable players' outcomes compared to unlikable ones'. However, males did not respond differently under either circumstance. These findings suggest that, at least in females, the neural response is influenced by a short-term induced affective preference.
Peng, Jinzhu; Dubay, Rickey
In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert
Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a
Robin L. Aupperle
Conclusions: Results support a relationship between anxiety and depressive symptoms and prefrontal-amygdala responses to maternal feedback. The lateralization of amygdala findings suggests separate neural targets for interventions reducing reactivity to negative feedback or increasing salience of positive feedback. Exploratory analyses suggest that parents' OXTR genetic profile influences parent-child interactions and related adolescent brain responses.
Goodman, Jarid; Gabriele, Amanda; Packard, Mark G
The present study examined the role of the dorsolateral striatum (DLS) in extinction behavior. Male Long-Evans rats were initially trained on the straight alley maze, in which they were reinforced to traverse a straight runway and retrieve food reward at the opposite end of the maze. After initial acquisition, animals were given extinction training using 1 of 2 distinct protocols: response extinction or latent extinction. For response extinction, the animal was released from the same starting position and had the opportunity to perform the originally reinforced approach response to the goal end of the maze, which no longer contained food. For latent extinction, the animal was confined to the original goal location without food, allowing the animal to form a new cognitive expectation (i.e., that the goal location is no longer reinforced). Immediately before response or latent extinction training, animals received bilateral intra-DLS administration of the sodium channel blocker bupivacaine or control injections of physiological saline. Results indicated that neural inactivation of the DLS with bupivacaine impaired response extinction, but did not influence latent extinction. The dissociation observed indicates that the DLS selectively mediates extinction mechanisms involving suppression of the original response, as opposed to cognitive mechanisms involving a change in expectation. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.
Full Text Available An important feature of addiction is the high drug craving that may promote the continuation of consumption. Environmental stimuli classically conditioned to drug-intake have a strong motivational power for addicts and can elicit craving. However, addicts differ in the attitudes towards their own consumption behavior: some are content with drug taking (consonant users whereas others are discontent (dissonant users. Such differences may be important for clinical practice because the experience of dissonance might enhance the likelihood to consider treatment. This fMRI study investigated in smokers whether these different attitudes influence subjective and neural responses to smoking stimuli. Based on self-characterization, smokers were divided into consonant and dissonant smokers. These two groups were presented smoking stimuli and neutral stimuli. Former studies have suggested differences in the impact of smoking stimuli depending on the temporal stage of the smoking ritual they are associated with. Therefore, we used stimuli associated with the beginning (BEGIN-smoking-stimuli and stimuli associated with the terminal stage (END-smoking-stimuli of the smoking ritual as distinct stimulus categories. Stimulus ratings did not differ between both groups. Brain data showed that BEGIN-smoking-stimuli led to enhanced mesolimbic responses (amygdala, hippocampus, insula in dissonant compared to consonant smokers. In response to END-smoking-stimuli, dissonant smokers showed reduced mesocortical responses (orbitofrontal cortex, subcallosal cortex compared to consonant smokers. These results suggest that smoking stimuli with a high incentive value (BEGIN-smoking-stimuli are more appetitive for dissonant than consonant smokers at least on the neural level. To the contrary, smoking stimuli with low incentive value (END-smoking-stimuli seem to be less appetitive for dissonant smokers than consonant smokers. These differences might be one reason why dissonant
Kate A. Woodcock
Full Text Available Background: Emotional responding is sensitive to social context; however, little emphasis has been placed on the mechanisms by which social context effects changes in emotional responding. Objective: We aimed to investigate the effects of social context on neural responses to emotional stimuli to inform on the mechanisms underpinning context-linked changes in emotional responding. Design: We measured event-related potential (ERP components known to index specific emotion processes and self-reports of explicit emotion regulation strategies and emotional arousal. Female Chinese university students observed positive, negative, and neutral photographs, whilst alone or accompanied by a culturally similar (Chinese or dissimilar researcher (British. Results: There was a reduction in the positive versus neutral differential N1 amplitude (indexing attentional capture by positive stimuli in the dissimilar relative to alone context. In this context, there was also a corresponding increase in amplitude of a frontal late positive potential (LPP component (indexing engagement of cognitive control resources. In the similar relative to alone context, these effects on differential N1 and frontal LPP amplitudes were less pronounced, but there was an additional decrease in the amplitude of a parietal LPP component (indexing motivational relevance in response to positive stimuli. In response to negative stimuli, the differential N1 component was increased in the similar relative to dissimilar and alone (trend context. Conclusion: These data suggest that neural processes engaged in response to emotional stimuli are modulated by social context. Possible mechanisms for the social-context-linked changes in attentional capture by emotional stimuli include a context-directed modulation of the focus of attention, or an altered interpretation of the emotional stimuli based on additional information proportioned by the context.
Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.
Schulte, T; Jung, Y-C; Sullivan, E V; Pfefferbaum, A; Serventi, M; Müller-Oehring, E M
Emotional dysregulation in alcoholism (ALC) may result from disturbed inhibitory mechanisms. We therefore tested emotion and alcohol cue reactivity and inhibitory processes using negative priming. To test the neural correlates of cue reactivity and negative priming, 26 ALC and 26 age-matched controls underwent functional MRI performing a Stroop color match-to-sample task. In cue reactivity trials, task-irrelevant emotion and alcohol-related pictures were interspersed between color samples and color words. In negative priming trials, pictures primed the semantic content of an alcohol or emotion Stroop word. Behaviorally, both groups showed response facilitation to picture cue trials and response inhibition to primed trials. For cue reactivity to emotion and alcohol pictures, ALC showed midbrain-limbic activation. By contrast, controls activated frontoparietal executive control regions. Greater midbrain-hippocampal activation in ALC correlated with higher amounts of lifetime alcohol consumption and higher anxiety. With negative priming, ALC exhibited frontal cortical but not midbrain-hippocampal activation, similar to the pattern observed in controls. Higher frontal activation to alcohol-priming correlated with less craving and to emotion-priming with fewer depressive symptoms. The findings suggest that neurofunctional systems in ALC can be primed to deal with upcoming emotion- and alcohol-related conflict and can overcome the prepotent midbrain-limbic cue reactivity response.
Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.
Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.
Yan, Yuanwei; Bejoy, Julie; Xia, Junfei; Guan, Jingjiao; Zhou, Yi; Li, Yan
Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells/tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capacity of signaling factors that regulate 3-D neural tissue patterning in vitro and differential responses of the resulting neural populations to various biomolecules have not yet been fully understood. By tuning neural patterning of hiPSCs with small molecules targeting sonic hedgehog (SHH) signaling, this study generated different 3-D neuronal cultures that were mainly comprised of either cortical glutamatergic neurons or motor neurons. Abundant glutamatergic neurons were observed following the treatment with an antagonist of SHH signaling, cyclopamine, while Islet-1 and HB9-expressing motor neurons were enriched by an SHH agonist, purmorphamine. In neurons derived with different neural patterning factors, whole-cell patch clamp recordings showed similar voltage-gated Na(+)/K(+) currents, depolarization-evoked action potentials and spontaneous excitatory post-synaptic currents. Moreover, these different neuronal populations exhibited differential responses to three classes of biomolecules, including (1) matrix metalloproteinase inhibitors that affect extracellular matrix remodeling; (2) N-methyl-d-aspartate that induces general neurotoxicity; and (3) amyloid β (1-42) oligomers that cause neuronal subtype-specific neurotoxicity. This study should advance our understanding of hiPSC self-organization and neural tissue development and provide a transformative approach to establish 3-D models for neurological disease modeling and drug discovery. Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells, tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capability of sonic hedgehog-related small molecules to tune
Flores, Agustín; Morant, Francisco
This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897
Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.
Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza
In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.
characteristics: reproducibility, accuracy, selectivity, aging, and resolution. Artificial neural network (ANN), a mathematical model formed by mimicking the human nervous system, was used to predict the sensor response. Qwiknet (version 2.23) software was used to develop ANNs and according to the results of Qwiknet the prediction performances for training and testing data sets were 75%, and 83.33% respectively. In this dissertation, Chapter 1 describes the worldwide plastic optical fiber (POF) and fiber optic sensor markets, and the existing textile structures used in fiber optic sensing design particularly for the applications of biomedical and structural health monitoring (SHM). Chapter 2 provides a literature review in detail on polymer optical fibers, fiber optic sensors, and occupancy sensing in the passenger seats of automobiles. Chapter 3 includes the research objectives. Chapter 4 presents the response of POF to tensile loading, bending, and cyclic tensile loading with discussion parts. Chapter 5 includes an e-mail based survey to prioritize customer needs in a Quality Function Deployment (QFD) format utilizing Analytic Hierarchy Process (AHP) and survey results. Chapter 6 describes the POF sensor design and the behavior of it under pressure. Chapter 7 provides a data analysis based on the experimental results of Chapter 6. Chapter 8 presents the summary of this study and recommendations for future work.
Full Text Available The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (~100 ms stop-to-restart intervals (SRSI, and an increased probability of difficulties after longer (>200 ms SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms, the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM excitability. Finally, we recorded electroencephalogram (EEG activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms, weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150-200 ms, because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results
Yamanaka, Kentaro; Nozaki, Daichi
The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time) of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (~100 ms) stop-to-restart intervals (SRSI), and an increased probability of difficulties after longer (>200 ms) SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs) in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms), the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM) excitability. Finally, we recorded electroencephalogram (EEG) activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms), weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150-200 ms), because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results suggest that
Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A
Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.
Tsuda, Ichiro; Yamaguchi, Yoko; Hashimoto, Takashi; Okuda, Jiro; Kawasaki, Masahiro; Nagasaka, Yasuo
The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.
Vargas, Lorena P [Lorena Vargas Quintero, Optic and Computer Science Group - Universidad Popular del Cesar (Colombia); Barba, Leiner; Torres, C O; Mattos, L, E-mail: email@example.com [Optic and Computer Science Group - Popular of Cesar University, Km 12, Valledupar (Colombia)
This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.
Demos, Kathryn E; Sweet, Lawrence H; Hart, Chantelle N; McCaffery, Jeanne M; Williams, Samantha E; Mailloux, Kimberly A; Trautvetter, Jennifer; Owens, Max M; Wing, Rena R
Despite growing literature on neural food cue responsivity in obesity, little is known about how the brain processes food cues following partial sleep deprivation and whether short sleep leads to changes similar to those observed in obesity. We used functional magnetic resonance imaging (fMRI) to test the hypothesis that short sleep leads to increased reward-related and decreased inhibitory control-related processing of food cues.In a within-subject design, 30 participants (22 female, mean age = 36.7 standard deviation = 10.8 years, body mass index range 20.4-40.7) completed four nights of 6 hours/night time-in-bed (TIB; short sleep) and four nights of 9 hours/night TIB (long sleep) in random counterbalanced order in their home environments. Following each sleep condition, participants completed an fMRI scan while viewing food and nonfood images.A priori region of interest analyses revealed increased activity to food in short versus long sleep in regions of reward processing (eg, nucleus accumbens/putamen) and sensory/motor signaling (ie, right paracentral lobule, an effect that was most pronounced in obese individuals). Contrary to the hypothesis, whole brain analyses indicated greater food cue responsivity during short sleep in an inhibitory control region (right inferior frontal gyrus) and ventral medial prefrontal cortex, which has been implicated in reward coding and decision-making (false discovery rate corrected q = 0.05).These findings suggest that sleep restriction leads to both greater reward and control processing in response to food cues. Future research is needed to understand the dynamic functional connectivity between these regions during short sleep and whether the interplay between these neural processes determines if one succumbs to food temptation. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail firstname.lastname@example.org.
Louise R Manfredi
Full Text Available Because tactile perception relies on the response of large populations of receptors distributed across the skin, we seek to characterize how a mechanical deformation of the skin at one location affects the skin at another. To this end, we introduce a novel non-contact method to characterize the surface waves produced in the skin under a variety of stimulation conditions. Specifically, we deliver vibrations to the fingertip using a vibratory actuator and measure, using a laser Doppler vibrometer, the surface waves at different distances from the locus of stimulation. First, we show that a vibration applied to the fingertip travels at least the length of the finger and that the rate at which it decays is dependent on stimulus frequency. Furthermore, the resonant frequency of the skin matches the frequency at which a subpopulation of afferents, namely Pacinian afferents, is most sensitive. We show that this skin resonance can lead to a two-fold increase in the strength of the response of a simulated afferent population. Second, the rate at which vibrations propagate across the skin is dependent on the stimulus frequency and plateaus at 7 m/s. The resulting delay in neural activation across locations does not substantially blur the temporal patterning in simulated populations of afferents for frequencies less than 200 Hz, which has important implications about how vibratory frequency is encoded in the responses of somatosensory neurons. Third, we show that, despite the dependence of decay rate and propagation speed on frequency, the waveform of a complex vibration is well preserved as it travels across the skin. Our results suggest, then, that the propagation of surface waves promotes the encoding of spectrally complex vibrations as the entire neural population is exposed to essentially the same stimulus. We also discuss the implications of our results for biomechanical models of the skin.
Full Text Available Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.
Shepanski, J. F.; Macy, S. A.
A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.
Full Text Available BackgroundPatients with obsessive-compulsive disorder (OCD have highly idiosyncratic triggers. To fully understand which role this idiosyncrasy plays in the neurobiological mechanisms behind OCD, it is necessary to elucidate the impact of individualization regarding the applied investigation methods.This functional magnetic resonance imaging (fMRI study explores the neural correlates of contamination/washing-related OCD with a highly individualized symptom provocation paradigm. Additionally, it is the first study to directly compare individualized and standardized symptom provocation. MethodsNineteen patients with washing compulsions created individual OCD hierarchies, which later served as instructions to photograph their own individualized stimulus sets. The patients and 19 case-by-case matched healthy controls participated in a symptom provocation fMRI experiment with individualized and standardized stimulus sets created for each patient. ResultsOCD patients compared to healthy controls displayed stronger activation in the basal ganglia (nucleus accumbens, nucleus caudatus, pallidum for individualized symptom provocation. Using standardized symptom provocation, this group comparison led to stronger activation in the nucleus caudatus. The direct comparison of between-group effects for both symptom provocation approaches revealed stronger activation of the orbitofronto-striatal network for individualized symptom provocation.ConclusionsThe present study provides insight into the differential impact of individualized and standardized symptom provocation on the orbitofronto-striatal network of OCD washers. Behavioral and neural responses imply a higher symptom-specificity of individualized symptom provocation.
Kapania, Rakesh K.; Liu, Youhua
At the preliminary design stage of a wing structure, an efficient simulation, one needing little computation but yielding adequately accurate results for various response quantities, is essential in the search of optimal design in a vast design space. In the present paper, methods of using sensitivities up to 2nd order, and direct application of neural networks are explored. The example problem is how to decide the natural frequencies of a wing given the shape variables of the structure. It is shown that when sensitivities cannot be obtained analytically, the finite difference approach is usually more reliable than a semi-analytical approach provided an appropriate step size is used. The use of second order sensitivities is proved of being able to yield much better results than the case where only the first order sensitivities are used. When neural networks are trained to relate the wing natural frequencies to the shape variables, a negligible computation effort is needed to accurately determine the natural frequencies of a new design.
Erguzel, Turker Tekin; Ozekes, Serhat; Gultekin, Selahattin; Tarhan, Nevzat; Hizli Sayar, Gokben; Bayram, Ali
The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.
Bolling, Danielle Z; Pitskel, Naomi B; Deen, Ben; Crowley, Michael J; McPartland, James C; Kaiser, Martha D; Wyk, Brent C Vander; Wu, Jia; Mayes, Linda C; Pelphrey, Kevin A
The present study aimed to explore the neural correlates of two characteristic deficits in autism spectrum disorders (ASD); social impairment and restricted, repetitive behavior patterns. To this end, we used comparable experiences of social exclusion and rule violation to probe potentially atypical neural networks in ASD. In children and adolescents with and without ASD, we used the interactive ball-toss game (Cyberball) to elicit social exclusion and a comparable game (Cybershape) to elicit a non-exclusive rule violation. Using functional magnetic resonance imaging (fMRI), we identified group differences in brain responses to social exclusion and rule violation. Though both groups reported equal distress following exclusion, the right insula and ventral anterior cingulate cortex were hypoactive during exclusion in children with ASD. In rule violation, right insula and dorsal prefrontal cortex were hyperactive in ASD. Right insula showed a dissociation in activation; it was hypoactive to social exclusion and hyperactive to rule violation in the ASD group. Further probed, different regions of right insula were modulated in each game, highlighting differences in regional specificity for which subsequent analyses revealed differences in patterns of functional connectivity. These results demonstrate neurobiological differences in processing social exclusion and rule violation in children with ASD.
Fusaro, Robert L.
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.
Hoyniak, Caroline P; Bates, John E; Petersen, Isaac T; Yang, Chung-Lin; Darcy, Isabelle; Fontaine, Nathalie M G
Callous-unemotional (CU) traits are characterized by a lack of guilt and empathy, and low responsiveness to distress and fear in others. Children with CU traits are at-risk for engaging in early and persistent conduct problems. Individuals showing CU traits have been shown to have reduced neural responses to others' distress (e.g., fear). However, the neural components of distress responses in children with CU traits have not been investigated in early childhood. In the current study, we examined neural responses that underlie the processing of emotionally valenced vocal stimuli using the event-related potential technique in a group of preschoolers. Participants between 2 and 5 years old took part in an auditory oddball task containing English-based pseudowords spoken with either a fearful, happy, or a neutral prosody while electroencephalography data were collected. The mismatch negativity (MMN) component, an index of the automatic detection of deviant stimuli within a series of stimuli, was examined in association with two dimensions of CU traits (i.e., callousness-uncaring and unemotional dimensions) reported by primary caregivers. Findings suggest that the callousness-uncaring dimension of CU traits in early childhood is associated with reduced responses to fearful vocal stimuli. Reduced neural responses to vocal fear could be a biomarker for callous-uncaring traits in early childhood. These findings are relevant for clinicians and researchers attempting to identify risk factors for early callous-uncaring traits. © 2017 John Wiley & Sons Ltd.
Barbey, Aron K; Colom, Roberto; Grafman, Jordan
Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.
Colom, Roberto; Grafman, Jordan
Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618
Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A
White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.
This paper presents a computer-based decision support system for automated interpretation of diagnostic heart images (called WeAidU), which is made available via the Internet. The system is based on image processing techniques, artificial neural networks (ANNs) and large well-validated medical databases. We present results using artificial neural networks, and compare with two other classification methods, on a retrospective data set containing 1320 images from the clinical routine. The performance of the artificial neural networks detecting infarction and ischemia in different parts of the heart, measured as areas under the receiver operating characteristic curves, is in the range 0.83-0.96. These results indicate a high potential for the tool as a clinical decision support system.
This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...
This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...
Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.
Singer, Tania; Seymour, Ben; O'Doherty, John P.; Stephan, Klaas E.; Dolan, Raymond J.; Frith, Chris D.
The neural processes underlying empathy are a subject of intense interest within the social neurosciences1-3. However, very little is known about how brain empathic responses are modulated by the affective link between individuals. We show here that empathic responses are modulated by learned preferences, a result consistent with economic models of social preferences4-7. We engaged male and female volunteers in an economic game, in which two confederates played fairly or unfairly, and then measured brain activity with functional magnetic resonance imaging while these same volunteers observed the confederates receiving pain. Both sexes exhibited empathy-related activation in pain-related brain areas (fronto-insular and anterior cingulate cortices) towards fair players. However, these empathy-related responses were significantly reduced in males when observing an unfair person receiving pain. This effect was accompanied by increased activation in reward-related areas, correlated with an expressed desire for revenge. We conclude that in men (at least) empathic responses are shaped by valuation of other people's social behaviour, such that they empathize with fair opponents while favouring the physical punishment of unfair opponents, a finding that echoes recent evidence for altruistic punishment. PMID:16421576
Larsen, Peter E; Cseke, Leland J; Miller, R Michael; Collart, Frank R
Rising atmospheric levels of carbon dioxide and ozone will impact productivity and carbon sequestration in forest ecosystems. The scale of this process and the potential economic consequences provide an incentive for the development of models to predict the types and rates of ecosystem responses and feedbacks that result from and influence of climate change. In this paper, we use phenotypic and molecular data derived from the Aspen Free Air CO2 Enrichment site (Aspen-FACE) to evaluate modeling approaches for ecosystem responses to changing conditions. At FACE, it was observed that different aspen clones exhibit clone-specific responses to elevated atmospheric levels of carbon dioxide and ozone. To identify the molecular basis for these observations, we used artificial neural networks (ANN) to examine above and below-ground community phenotype responses to elevated carbon dioxide, elevated ozone and gene expression profiles. The aspen community models generated using this approach identified specific genes and subnetworks of genes associated with variable sensitivities for aspen clones. The ANN model also predicts specific co-regulated gene clusters associated with differential sensitivity to elevated carbon dioxide and ozone in aspen species. The results suggest ANN is an effective approach to predict relevant gene expression changes resulting from environmental perturbation and provides useful information for the rational design of future biological experiments. Copyright © 2014 Elsevier Ltd. All rights reserved.
Chakraverty, S.; Sahoo, Deepti Moyi
Present paper uses powerful technique of interval neural network (INN) to simulate and estimate structural response of multi-storey shear buildings subject to earthquake motion. The INN is first trained for a real earthquake data, viz., the ground acceleration as input and the numerically generated responses of different floors of multi-storey buildings as output. Till date, no model exists to handle positive and negative data in the INN. As such here, the bipolar data in [ -1, 1] are converted first to unipolar form, i.e., to [0, 1] by means of a novel transformation for the first time to handle the above training patterns in normalized form. Once the training is done, again the unipolar data are converted back to its bipolar form by using the inverse transformation. The trained INN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by INN model are good for practical purposes.
Full Text Available Relief fits the definition of a reward. Unlike other reward types the pleasantness of relief depends on the violation of a negative expectation, yet this has not been investigated using neuroimaging approaches. We hypothesized that the degree of negative expectation depends on state (dread and trait (pessimism sensitivity. Of the brain regions that are involved in mediating pleasure, the nucleus accumbens also signals unexpected reward and positive prediction error. We hypothesized that accumbens activity reflects the level of negative expectation and subsequent pleasant relief. Using fMRI and two purpose-made tasks, we compared hedonic and BOLD responses to relief with responses during an appetitive reward task in 18 healthy volunteers. We expected some similarities in task responses, reflecting common neural substrates implicated across reward types. However, we also hypothesized that relief responses would differ from appetitive rewards in the nucleus accumbens, since only relief pleasantness depends on negative expectations. The results confirmed these hypotheses. Relief and appetitive reward task activity converged in the ventromedial prefrontal cortex, which also correlated with appetitive reward pleasantness ratings. In contrast, dread and pessimism scores correlated with relief but not with appetitive reward hedonics. Moreover, only relief pleasantness covaried with accumbens activation. Importantly, the accumbens signal appeared to specifically reflect individual differences in anticipation of the adverse event (dread, pessimism but was uncorrelated to appetitive reward hedonics. In conclusion, relief differs from appetitive rewards due to its reliance on negative expectations, the violation of which is reflected in relief-related accumbens activation.
Reifman, Jaques; Wei, Thomas Y. C.
A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.
Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.
This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.
Reifman, J.; Wei, T.Y.C.
A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.
Moura, Amanda Christina Gomes de
Full Text Available Introduction Currently the cochlear implant allows access to sounds in individuals with profound hearing loss. The objective methods used to verify the integrity of the cochlear device and the electrophysiologic response of users have noted these improvements. Objective To establish whether the evoked compound action potential of the auditory nerve can appear after electrical stimulation when it is absent intraoperatively. Methods The clinical records of children implanted with the Nucleus Freedom (Cochlear Ltd., Australia (CI24RE cochlear implant between January 2009 and January 2010 with at least 6 months of use were evaluated. The neural response telemetry (NRT thresholds of electrodes 1, 6, 11, 16, and 22 during surgery and after at least 3 months of implant use were analyzed and correlated with etiology, length of auditory deprivation, and chronological age. These data were compared between a group of children exhibiting responses in all of the tested electrodes and a group of children who had at least one absent response. Results The sample was composed of clinical records of 51 children. From these, 21% (11 showed no NRT in at least one of the tested electrodes. After an average of 4.9 months of stimulation, the number of individuals exhibiting absent responses decreased from 21 to 11% (n = 6. Conclusion It is feasible that absent responses present after a period of electrical stimulation. In our sample, 45% (n = 5 of the patients with intraoperative absence exhibited a positive response after an average of 4.9 months of continued electrical stimulation.
van der Vegt, Joyce P M; Hulme, Oliver J; Zittel, Simone
resulting in impulse control disorders. To circumvent this treatment confound, we assayed the neural basis of reward processing in a group of newly diagnosed patients with Parkinson's disease that had never been treated with dopaminergic drugs. Thirteen drug-naive patients with Parkinson's disease and 12......Parkinson's disease results from the degeneration of dopaminergic neurons in the substantia nigra, manifesting as a spectrum of motor, cognitive and affective deficits. Parkinson's disease also affects reward processing, but disease-related deficits in reinforcement learning are thought to emerge...... at a slower pace than motor symptoms as the degeneration progresses from dorsal to ventral striatum. Dysfunctions in reward processing are difficult to study in Parkinson's disease as most patients have been treated with dopaminergic drugs, which sensitize reward responses in the ventral striatum, commonly...
Miskowiak, Kamilla W; Kessing, Lars V; Ott, Caroline V
Negative neurocognitive bias is a core feature of major depressive disorder that is reversed by pharmacological and psychological treatments. This double-blind functional magnetic resonance imaging study investigated for the first time whether electroconvulsive therapy modulates negative neurocog......Negative neurocognitive bias is a core feature of major depressive disorder that is reversed by pharmacological and psychological treatments. This double-blind functional magnetic resonance imaging study investigated for the first time whether electroconvulsive therapy modulates negative...... to fearful versus happy faces as well as in fear-specific functional connectivity between amygdala and occipito-temporal regions. Across all patients, greater fear-specific amygdala - occipital coupling correlated with lower fear vigilance. Despite no statistically significant shift in neural response...
Beaton, Elliott A; Schmidt, Louis A; Schulkin, Jay; Antony, Martin M; Swinson, Richard P; Hall, Geoffrey B
The shy-bold continuum is a fundamental behavioral trait conserved across human and nonhuman animals. Individual differences along the shy-bold continuum are presumed to arise from, and are maintained by, differences in the excitability of forebrain limbic areas involved in the evaluation of stimulus saliency. To test this hypothesis, the authors conducted an event-related functional MRI (fMRI) study in which brain scans were acquired on shy and bold adults during the presentation of neutral stranger and personally familiar faces. Shy adults exhibited greater bilateral amygdala activation during the presentation of stranger faces and greater left amygdala activation during personally familiar faces than their bold counterparts. Bold adults exhibited greater bilateral nucleus accumbens activation in response to stranger and personally familiar faces than shy adults. Findings suggest that there are distinct neural substrates underlying and maintaining individual differences along a shy-bold continuum in humans. (Copyright) 2008 APA, all rights reserved.
Full Text Available Adult neurogenesis plays increasingly recognized roles in brain homeostasis and repair and is profoundly affected by energy balance and nutrients. We found that the expression of Hes-1 (hairy and enhancer of split 1 is modulated in neural stem and progenitor cells (NSCs by extracellular glucose through the coordinated action of CREB (cyclic AMP responsive element binding protein and Sirt-1 (Sirtuin 1, two cellular nutrient sensors. Excess glucose reduced CREB-activated Hes-1 expression and results in impaired cell proliferation. CREB-deficient NSCs expanded poorly in vitro and did not respond to glucose availability. Elevated glucose also promoted Sirt-1-dependent repression of the Hes-1 promoter. Conversely, in low glucose, CREB replaced Sirt-1 on the chromatin associated with the Hes-1 promoter enhancing Hes-1 expression and cell proliferation. Thus, the glucose-regulated antagonism between CREB and Sirt-1 for Hes-1 transcription participates in the metabolic regulation of neurogenesis.
Rutherford, Helena J V; Byrne, Simon P; Austin, Grace M; Lee, Jonathan D; Crowley, Michael J; Mayes, Linda C
Women are vulnerable to anxiety during pregnancy and postpartum. However, little is known about antenatal anxiety and neural processing of infant-relevant information. In this experiment, the N170, P300, and LPP (late positive potential) event-related potentials were measured from 43 pregnant women as they viewed infant and adult faces, which were either neutral or distressed in expression. Mother's self-reported anxiety levels were also assessed. The N170 was comparable across face conditions and was not associated with anxiety. However, our central finding was that greater levels of antenatal anxiety were associated with a larger LPP, but only for neutral infant faces. Results suggest that antenatal anxiety may result in deeper processing of neutral, emotionally ambiguous, infant faces during pregnancy. These findings are discussed in light of other work indicating an interpretive bias toward threat in response to neutral stimuli in anxiety. Copyright © 2017 Elsevier B.V. All rights reserved.
Jonathan D. Victor; Nirenberg, Sheila
One of the most critical challenges in systems neuroscience is determining the neural code. A principled framework for addressing this can be found in information theory. With this approach, one can determine whether a proposed code can account for the stimulus-response relationship. Specifically, one can compare the transmitted information between the stimulus and the hypothesized neural code with the transmitted information between the stimulus and the behavioral response. If the former is ...
Zhu, Ning; Lin, Jizong; Wang, Kewan; Wei, Meidan; Chen, Qingzhuang; Wang, Yong
This study aims to explore whether Huperzine A (HupA) could protect neural stem cells against amyloid beta-peptide Aβ induced apoptosis in a neural stem cells (NSCs) and microglia co-culture system. Rat NSCs and microglial cells were isolated, cultured and identified with immunofluorescence Assays (IFA). Co-culture systems of NSCs and microglial cells were employed using Transwell Permeable Supports. The effects of Aβ1-42 on NSCs were studied in 4 groups using co-culture systems: NSCs, Aβ+NSCs, co-culture and Aβ+co-culture groups. Bromodeoxyuridine (BrdU) incorporation and flow cytometry were utilized to assess the differences of proliferation, differentiation and apoptosis of NSCs between the groups. LQ test was performed to assess the amounts of IL-6, TNF-α and MIP-α secreted, and flow cytometry and Western blotting were used to assess apoptosis of NSCs and the expressions of Bcl-2 and Bax in each group. IFA results showed that isolated rat NSCs were nestin-positive and microglial cells were CD11b/c-positive. Among all the groups, the Aβ+co-culture group has the lowest BrdU expression level, the lowest MAP2-positive, ChAT-positive cell counts and the highest NSC apoptosis rate. Smaller amounts of IL-6, TNF-α and MIP-α were being secreted by microglial cells in the HupA+Aβ+co-culture group compared with those in the Aβ+ co-culture group. Also the Bcl-2: Bax ratio was much higher in the HupA+Aβ+co-culture group than in the Aβ+co-culture group. HupA inhibits cell apoptosis through restraining microglia's inflammatory response induced by Aβ1-42.
Richardson; Mbanefo; Aboofazeli; Lawrence; Barlow
Preliminary investigations have been conducted to assess the potential for using (back-propagation, feed-forward) artificial neural networks to predict the phase behavior of quaternary microemulsion-forming systems, with a view to employing this type of methodology in the evaluation of novel cosurfactants for the formulation of pharmaceutically acceptable drug-delivery systems. The data employed in training the neural networks related to microemulsion systems containing lecithin, isopropyl myristate, and water, together with different types of cosurfactants, including short- and medium-chain alcohols, amines, acids, and ethylene glycol monoalkyl ethers. Previously unpublished phase diagrams are presented for four systems involving the cosurfactants 2-methyl-2-butanol, 2-methyl-1-propanol, 2-methyl-1-butanol, and isopropanol, which, along with eight other published sets of data, are used to test the predictive ability of the trained networks. The pseudo-ternary phase diagrams for these systems are predicted using only four computed physicochemical properties for the cosurfactants involved. The artificial neural networks are shown to be highly successful in predicting phase behavior for these systems, achieving mean success rates of 96.7 and 91.6% for training and test data, respectively. The conclusion is reached that artificial neural networks can provide useful tools for the development of microemulsion-based drug-delivery systems.
concluded that the rising phase of action potential frequency was significantly sharper (steeper) than the falling phase in cutoff responses, but that this was not the case of all-round-firing responses.
Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod
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.
Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)
The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.
Bendtsen, Jan Dimon; Stoustrup, Jakob
This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training sam...... control can be achieved by interpolating between each controller.In this paper, we propose to use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a smooth scheduling between the operating points with certain stability guarantees....
Wang, L.; Zhang, Y. Y.; Ding, L.
The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.
Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman
A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Hildebrandt, K Jannis
The convergent evolution of hearing in insects and vertebrates raises the question about similarity of the central representation of sound in these distant animal groups. Topographic representations of spectral, spatial and temporal cues have been widely described in mammals, but evidence for such maps is scarce in insects. Recent data on insect sound encoding provides evidence for an early integration of sound parameters to form highly-specific representation that predict behavioral output. In mammals, new studies investigating neural representation of perceptual features in behaving animals allow asking similar questions. A comparative approach may help in understanding principles underlying the formation of perceptual categories and behavioral plasticity. Copyright © 2013 Elsevier Ltd. All rights reserved.
J. Humberto Pérez-Cruz
Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.
Jiang, Xiaoming; Sanford, Ryan; Pell, Marc D
Our voice provides salient cues about how confident we sound, which promotes inferences about how believable we are. However, the neural mechanisms involved in these social inferences are largely unknown. Employing functional magnetic resonance imaging, we examined the brain networks and individual differences underlying the evaluation of speaker believability from vocal expressions. Participants (n = 26) listened to statements produced in a confident, unconfident, or "prosodically unmarked" (neutral) voice, and judged how believable the speaker was on a 4-point scale. We found frontal-temporal networks were activated for different levels of confidence, with the left superior and inferior frontal gyrus more activated for confident statements, the right superior temporal gyrus for unconfident expressions, and bilateral cerebellum for statements in a neutral voice. Based on listener's believability judgment, we observed increased activation in the right superior parietal lobule (SPL) associated with higher believability, while increased left posterior central gyrus (PoCG) was associated with less believability. A psychophysiological interaction analysis found that the anterior cingulate cortex and bilateral caudate were connected to the right SPL when higher believability judgments were made, while supplementary motor area was connected with the left PoCG when lower believability judgments were made. Personal characteristics, such as interpersonal reactivity and the individual tendency to trust others, modulated the brain activations and the functional connectivity when making believability judgments. In sum, our data pinpoint neural mechanisms that are involved when inferring one's believability from a speaker's voice and establish ways that these mechanisms are modulated by individual characteristics of a listener. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Miller, Alan D.
Initial studies re-examine the role of certain central nervous system structures in the production of vestibular-induced vomiting and vomiting in general. All experiments were conducted using cats. Since these studies demonstrated that the essential role of various central structures in vestibular-induced vomiting is only poorly understood, efforts were re-directed to study the control of the effector muscles (diaphragm and abdominal muscles) that produce the pressure changes responsible for vomiting, with the goal of determining how this control mechanism is engaged during motion sickness. Experiments were conducted to localize the motoneurons that innervate the individual abdominal muscles and the portion of the diaphragm that surrounds the esophagus. A central question regarding respiratory muscle control during vomiting is whether these muscles are activated via the same brain stem pre-motor neurons that provide descending respiratory drive and/or by other descending input(s). In other experiments, the use of a combination of pitch and roll motions to produce motion sickness in unrestrained cats was evaluated. This stimulus combination can produce vomiting in only the most susceptible cats and is thus not as provacative a stimulus for cats as vertical linear acceleration.
Smolders, J W; Aertsen, A M; Johannesma, P I
Cats were stimulated with tones and with natural sounds selected from the normal acoustic environment of the animal. Neural activity evoked by the natural sounds and tones was recorded in the cochlear nucleus and in the medial geniculate body. The set of biological sounds proved to be effective in influencing neural activity of single cells at both levels in the auditory system. At the level of the cochlear nucleus the response of a neuron evoked by a natural sound stimulus could be understood reasonably well on the basis of the structure of the spectrograms of the natural sounds and the unit's responses to tones. At the level of the medial geniculate body analysis with tones did not provide sufficient information to explain the responses to natural sounds. At this level the use of an ensemble of natural sound stimuli allows the investigation of neural properties, which are not seen by analysis with simple artificial stimuli. Guidelines for the construction of an ensemble of complex natural sound stimuli, based on the ecology and ethology of the animal under investigation are discussed. This stimulus ensemble is defined as the Acoustic Biotope.
Wood, Kimberly H; Ver Hoef, Lawrence W; Knight, David C
Recognizing cues that predict an aversive event allows one to react more effectively under threatening conditions, and minimizes the reaction to the threat itself. This is demonstrated during Pavlovian fear conditioning when the unconditioned response (UCR) to a predictable unconditioned stimulus (UCS) is diminished compared to the UCR to an unpredictable UCS. The present study investigated the functional magnetic resonance imaging (fMRI) signal response associated with Pavlovian conditioned UCR diminution to better understand the relationship between individual differences in behavior and the neural mechanisms of the threat-related emotional response. Healthy volunteers participated in a fear conditioning study in which trait anxiety, skin conductance response (SCR), UCS expectancy, and the fMRI signal were assessed. During acquisition trials, a tone (CS+) was paired with a white noise UCS and a second tone (CS-) was presented without the UCS. Test trials consisted of the CS+ paired with the UCS, CS- paired with the UCS, and presentations of the UCS alone to assess conditioned UCR diminution. UCR diminution was observed within the dorsolateral PFC, dorsomedial PFC, cingulate cortex, inferior parietal lobule (IPL), anterior insula, and amygdala. The threat-related activity within the dorsolateral PFC, dorsomedial PFC, posterior cingulate cortex, and IPL varied with individual differences in trait anxiety. In addition, anticipatory (i.e. CS elicited) activity within the PFC showed an inverse relationship with threat-related (i.e. UCS elicited) activity within the PFC, IPL, and amygdala. Further, the emotional response (indexed via SCR) elicited by the threat was closely linked to amygdala activity. These findings are consistent with the view that the amygdala and PFC support learning-related processes that influence the emotional response evoked by a threat. Copyright © 2011 Elsevier Inc. All rights reserved.
Full Text Available This paper presents a radial basis function (RBF neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.
Artrith, Nongnuch; Morawietz, Tobias; Behler, Jörg
Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potential-energy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.
Vlatkovic, Matea; Collins, Beatrice S. L.; Feringa, Ben L.
Responsive systems have recently gained much interest in the scientific community in attempts to mimic dynamic functions in biological systems. One of the fascinating potential applications of responsive systems lies in catalysis. Inspired by nature, novel responsive catalytic systems have been
Xu, Bin; Yang, Chenguang; Pan, Yongping
This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.
Di Russo, Francesco; Taddei, Francesco; Apnile, Teresa; Spinelli, Donatella
Flexible adaptation of behaviour is highly required in some sports, such as fencing. In particular, stimulus discrimination and motor response selection and inhibition processes are crucial. We investigated the neural mechanisms responsible for fencers' fast and flexible behaviour recording event-related potentials (ERPs) in discriminative reaction task (DRT, Go/No-go task) and simple reaction task (SRT) to visual stimuli. In the DRT, in addition to faster RTs measured in fencers with respect to control subjects, three main electrophysiological differences were found. First, attentional modulation of the visual processing taking place in the occipital lobes and reaching a peak at 170 ms was enhanced in the athletes group. Second, the activity in the posterior cingulate gyrus, associated with the stimulus discrimination stage, started earlier in fencers than controls (150 ms versus 200 ms) and the peak had larger amplitude. Third, the activity at the level of the prefrontal cortex (time range: 250-350 ms), associated with response selection stage and particularly with motor inhibition process, was stronger in fencers. No differences between athletes and controls were found in the SRT for both ERPs and RTs. Concluding, the fencers' ability to cope to the opponent feint switching quickly from an intended action to a new more appropriate action is likely due to a faster stimulus discrimination facilitated by higher attention and by stronger inhibition activity in prefrontal cortex.
Goerlich-Dobre, Katharina Sophia; Witteman, Jurriaan; Schiller, Niels O; van Heuven, Vincent J P; Aleman, André; Martens, Sander
How we perceive emotional signals from our environment depends on our personality. Alexithymia, a personality trait characterized by difficulties in emotion regulation has been linked to aberrant brain activity for visual emotional processing. Whether alexithymia also affects the brain's perception of emotional speech prosody is currently unknown. We used functional magnetic resonance imaging to investigate the impact of alexithymia on hemodynamic activity of three a priori regions of the prosody network: the superior temporal gyrus (STG), the inferior frontal gyrus and the amygdala. Twenty-two subjects performed an explicit task (emotional prosody categorization) and an implicit task (metrical stress evaluation) on the same prosodic stimuli. Irrespective of task, alexithymia was associated with a blunted response of the right STG and the bilateral amygdalae to angry, surprised and neutral prosody. Individuals with difficulty describing feelings deactivated the left STG and the bilateral amygdalae to a lesser extent in response to angry compared with neutral prosody, suggesting that they perceived angry prosody as relatively more salient than neutral prosody. In conclusion, alexithymia may be associated with a generally blunted neural response to speech prosody. Such restricted prosodic processing may contribute to problems in social communication associated with this personality trait. © The Author (2013). Published by Oxford University Press. For Permissions, please email: email@example.com.
Full Text Available Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other’s actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children’s interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction.
Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S
Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Ross, Muriel D.
One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.
Kobayashi, Kenta; Kato, Shigeki; Kobayashi, Kazuto
To understand the mechanisms underlying higher brain functions, we need to analyze the roles of specific neuronal pathways or cell types forming the complex neural networks. In the neuroscience field, the transgenic approach has provided a useful gene engineering tool for experimental studies of neural functions. The conventional transgenic technique requires the appropriate promoter regions that drive a neuronal type-specific gene expression, but the promoter sequences specifically functioning in each neuronal type are limited. Previously, we developed novel types of lentiviral vectors showing high efficiency of retrograde gene transfer in the central nervous system, termed highly efficient retrograde gene transfer (HiRet) vector and neuron-specific retrograde gene transfer (NeuRet) vector. The HiRet and NeuRet vectors enable genetical manipulation of specific neural pathways in diverse model animals in combination with conditional cell targeting, synaptic transmission silencing, and gene expression systems. These newly developed vectors provide powerful experimental strategies to investigate, more precisely, the machineries exerting various neural functions. In this review, we give an outline of the HiRet and NeuRet vectors and describe recent representative applications of these viral vectors for studies on neural circuits.
Nayar, Priya; Singh, Bhim; Mishra, Sukumar
An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Schlund, Michael W; Magee, Sandy; Hudgins, Caleb D
Adaptive functioning is thought to reflect a balance between approach and avoidance neural systems with imbalances often producing pathological forms of avoidance. Yet little evidence is available in healthy adults demonstrating a balance between approach and avoidance neural systems and modulation in avoidance neurocircuitry by vulnerability factors for avoidance. Consequently, we used functional magnetic resonance imaging (fMRI) to compare changes in brain activation associated with human avoidance and approach learning and modulation of avoidance neurocircuitry by experiential avoidance. fMRI tracked trial-by-trial increases in activation while adults learned through trial and error an avoidance response that prevented money loss and an approach response that produced money gain. Avoidance and approach cues elicited similar experience-dependent increases in activation in a fronto-limbic-striatal network. Positive and negative reinforcing outcomes (i.e., money gain and avoidance of loss) also elicited similar increases in activation in frontal and striatal regions. Finally, increased experiential avoidance and self-punishment coping was associated with decreased activation in medial/superior frontal regions, anterior cingulate, amygdala and hippocampus. These findings suggest avoidance and approach learning recruit a similar fronto-limbic-striatal network in healthy adults. Increased experiential avoidance also appears to be associated with reduced frontal and limbic reactivity in avoidance, establishing an important link between maladaptive avoidance coping and altered responses in avoidance neurocircuitry. Copyright © 2011 Elsevier B.V. All rights reserved.
Jeong, Gi Seok; Chang, Joon Young; Park, Ji Soo; Lee, Seung-A; Park, DoYeun; Woo, Junsung; An, Heeyoung; Lee, C Justin; Lee, Sang-Hoon
In most animals, the nervous system consists of the central nervous system (CNS) and the peripheral nervous system (PNS), the latter of which connects the CNS to all parts of the body. Damage and/or malfunction of the nervous system causes serious pathologies, including neurodegenerative disorders, spinal cord injury, and Alzheimer's disease. Thus, not surprising, considerable research effort, both in vivo and in vitro, has been devoted to studying the nervous system and signal transmission through it. However, conventional in vitro cell culture systems do not enable control over diverse aspects of the neural microenvironment. Moreover, formation of certain nervous system growth patterns in vitro remains a challenge. In this study, we developed a deep hemispherical, microchannel-networked, concave array system and applied it to generate three-dimensional nerve-like neural bundles. The deep hemicylindrical channel network was easily fabricated by exploiting the meniscus induced by the surface tension of a liquid poly(dimethylsiloxane) (PDMS) prepolymer. Neurospheroids spontaneously aggregated in each deep concave microwell and were networked to neighboring spheroids through the deep hemicylindrical channel. Notably, two types of satellite spheroids also formed in deep hemispherical microchannels through self-aggregation and acted as an anchoring point to enhance formation of nerve-like networks with neighboring spheroids. During neural-network formation, neural progenitor cells successfully differentiated into glial and neuronal cells. These cells secreted laminin, forming an extracellular matrix around the host and satellite spheroids. Electrical stimuli were transmitted between networked neurospheroids in the resulting nerve-like neural bundle, as detected by imaging Ca(2+) signals in responding cells.
Hillyard, Stanley D; Baula, Victor; Tuttle, Wendy
low, V(b) transiently hyperpolarized to values near the equilibrium potential for K(+) and corresponded with the reduced neural response. These results support the hypothesis that chemosensory function of the skin is analogous to that of mammalian taste cells but utilizes paracellular ion transport...
Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.
Nobrega, Antonio C L; O'Leary, Donal; Silva, Bruno Moreira; Marongiu, Elisabetta; Piepoli, Massimo F; Crisafulli, Antonio
During dynamic exercise, mechanisms controlling the cardiovascular apparatus operate to provide adequate oxygen to fulfill metabolic demand of exercising muscles and to guarantee metabolic end-products washout. Moreover, arterial blood pressure is regulated to maintain adequate perfusion of the vital organs without excessive pressure variations. The autonomic nervous system adjustments are characterized by a parasympathetic withdrawal and a sympathetic activation. In this review, we briefly summarize neural reflexes operating during dynamic exercise. The main focus of the present review will be on the central command, the arterial baroreflex and chemoreflex, and the exercise pressure reflex. The regulation and integration of these reflexes operating during dynamic exercise and their possible role in the pathophysiology of some cardiovascular diseases are also discussed.
Nobrega, Antonio C. L.; O'Leary, Donal; Silva, Bruno Moreira; Piepoli, Massimo F.; Crisafulli, Antonio
During dynamic exercise, mechanisms controlling the cardiovascular apparatus operate to provide adequate oxygen to fulfill metabolic demand of exercising muscles and to guarantee metabolic end-products washout. Moreover, arterial blood pressure is regulated to maintain adequate perfusion of the vital organs without excessive pressure variations. The autonomic nervous system adjustments are characterized by a parasympathetic withdrawal and a sympathetic activation. In this review, we briefly summarize neural reflexes operating during dynamic exercise. The main focus of the present review will be on the central command, the arterial baroreflex and chemoreflex, and the exercise pressure reflex. The regulation and integration of these reflexes operating during dynamic exercise and their possible role in the pathophysiology of some cardiovascular diseases are also discussed. PMID:24818143
Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo
This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...
Rilling, James K; Demarco, Ashley C; Hackett, Patrick D; Chen, Xu; Gautam, Pritam; Stair, Sabrina; Haroon, Ebrahim; Thompson, Richmond; Ditzen, Beate; Patel, Rajan; Pagnoni, Giuseppe
Both oxytocin (OT) and vasopressin (AVP) are known to modulate social behavior, and dysfunction in both systems has been postulated as a potential cause of certain psychiatric disorders that involve social behavioral deficits. In particular, there is growing interest in intranasal OT as a potential treatment for certain psychiatric disorders, and preliminary pre-clinical and clinical studies suggest efficacy in alleviating some of the associated symptoms. However, the vast majority of research participants in these studies have been male, and there is evidence for sexually differentiated effects of nonapeptides in both humans and non-human animals. To date, no study has investigated the effect of intranasal OT on brain function in human males and females within the same paradigm. Previously, in a randomized, placebo-controlled, double-blind fMRI study, we reported effects of intranasal OT and AVP on behavior and brain activity of human males as they played an interactive social game known as the Prisoner's Dilemma Game. Here, we present findings from an identical study in human females, and compare these with our findings from males. Overall, we find that both behavioral and neural responses to intranasal OT and AVP are highly sexually differentiated. In women, AVP increased conciliatory behavior, and both OT and AVP caused women to treat computer partners more like humans. In men, AVP increased reciprocation of cooperation from both human and computer partners. However, no specific drug effects on behavior were shared between men and women. During cooperative interactions, both OT and AVP increased brain activity in men within areas rich in OT and AVP receptors and in areas playing a key role in reward, social bonding, arousal and memory (e.g., the striatum, basal forebrain, insula, amygdala and hippocampus), whereas OT and AVP either had no effect or in some cases actually decreased brain activity in these regions in women. OT treatment rendered neural responses
Morgan-Short, Kara; Finger, Ingrid; Grey, Sarah; Ullman, Michael T
Although learning a second language (L2) as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2--particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP) study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes--including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research program with
Patel, Sona; Lodhavia, Anjli; Frankford, Saul; Korzyukov, Oleg; Larson, Charles R
It is known that singers are able to control their voice to maintain a relatively constant vocal quality while transitioning between vocal registers; however, the neural mechanisms underlying this effect are not understood. It was hypothesized that greater attention to the acoustical feedback of the voice and increased control of the vocal musculature during register transitions compared with singing within a register would be represented as neurological differences in event-related potentials. Nine singers sang musical notes at the high end of the modal register (the boundary between the modal and the head/falsetto registers) and at the low end (the boundary between the modal and the fry/pulse registers). While singing, the pitch of the voice auditory feedback was unexpectedly shifted either into the adjacent register ("toward" the register boundary) or within the modal register ("away from" the boundary). Singers were instructed to maintain a constant pitch and ignore any changes to their voice feedback. Vocal response latencies and magnitude of the accompanying N1 and P2 event-related potentials were greatest at the lower (modal-to-fry) boundary when the pitch shift carried the subjects' voices into the fry register as opposed to remaining within the modal register. These findings suggest that when a singer lowers the pitch of his or her voice such that it enters the fry register from the modal register, there is increased sensory-motor control of the voice, reflected as increased magnitude of the neural potentials to help minimize qualitative changes in the voice. Copyright Â© 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Full Text Available Although learning a second language (L2 as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2--particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes--including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research
Patel, Sona; Lodhavia, Anjli; Frankford, Saul; Korzyukov, Oleg; Larson, Charles R.
Objective/Hypothesis It is known that singers are able to control their voice to maintain a relatively constant vocal quality while transitioning between vocal registers; however, the neural mechanisms underlying this effect are not understood. It was hypothesized that greater attention to the acoustical feedback of the voice and increased control of the vocal musculature during register transitions compared to singing within a register would be represented as neurological differences in event-related potentials (ERPs). Study Design/Methods Nine singers sang musical notes at the high end of the modal register (the boundary between the modal and head/falsetto registers) and at the low end (the boundary between the modal and fry/pulse registers). While singing, the pitch of the voice auditory feedback was unexpectedly shifted either into the adjacent register (“toward” the register boundary) or within the modal register (“away from” the boundary). Singers were instructed to maintain a constant pitch and ignore any changes to their voice feedback. Results Vocal response latencies and magnitude of the accompanying N1 and P2 ERPs were greatest at the lower (modal-fry) boundary when the pitch shift carried the subjects’ voices into the fry register as opposed to remaining within the modal register. Conclusions These findings suggest that when a singer lowers the pitch of their voice such that it enters the fry register from the modal register, there is increased sensory-motor control of the voice, reflected as increased magnitude of the neural potentials to help minimize qualitative changes in the voice. PMID:26739860
Carrie J Mcadams
Full Text Available Self-evaluation closely dependent upon body shape and weight is one of the defining criteria for bulimia nervosa. We studied 53 adult women, 17 with bulimia nervosa, 18 with a recent history of anorexia nervosa, and 18 healthy comparison women, using three different fMRI tasks that required thinking about self-knowledge and social interactions: the Social Identity task, the Physical Identity task, and the Social Attribution task. Previously, we identified regions of interest (ROI in the same tasks using whole brain voxel-wise comparisons of the healthy comparison women and women with a recent history of anorexia nervosa. Here, we report on the neural activations in those ROIs in subjects with bulimia nervosa. In the Social Attribution task, we examined activity in the right temporoparietal junction, an area frequently associated with mentalization. In the Social Identity task, we examined activity in the precuneus and dorsal anterior cingulate. In the Physical Identity task, we examined activity in a ventral region of the dorsal anterior cingulate. Interestingly, in all tested regions, the average activation in subjects with bulimia was more than the average activation levels seen in the subjects with a history of anorexia but less than that seen in healthy subjects. In three regions, the right temporoparietal junction, the precuneus, and the dorsal anterior cingulate, group responses in the subjects with bulimia were significantly different from healthy subjects but not subjects with anorexia. The neural activations of people with bulimia nervosa performing fMRI tasks engaging social processing are more similar to people with anorexia nervosa than healthy people. This suggests biological measures of social processes may be helpful in characterizing individuals with eating disorders.
Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.
Yan, Ji-Geng; Zhang, Lin-ling; Agresti, Michael; LoGiudice, John; Sanger, James R; Matloub, Hani S; Havlik, Robert
Insidious brain microinjury from motor vehicle-induced whole-body vibration (WBV) has not yet been investigated. For a long time we have believed that WBV would cause cumulative brain microinjury and impair cerebral function, which suggests an important risk factor for motor vehicle accidents and secondary cerebral vascular diseases. Fifty-six Sprague-Dawley rats were divided into seven groups (n = 8): 1) 2-week normal control group, 2) 2-week sham control group (restrained in the tube without vibration), 3) 2-week vibration group (exposed to whole-body vibration at 30 Hz and 0.5g acceleration for 4 hr/day, 5 days/week, for 2 weeks), 4) 4-week sham control group, 5) 4-week vibration group, 6) 8-week sham control group, and 7) 8-week vibration group. At the end point, all rats were evaluated in behavior, physiological, and brain histopathological studies. The cerebral injury from WBV is a cumulative process starting with vasospasm squeezing of the endothelial cells, followed by constriction of the cerebral arteries. After the 4-week vibration, brain neuron apoptosis started. After the 8-week vibration, vacuoles increased further in the brain arteries. Brain capillary walls thickened, mean neuron size was obviously reduced, neuron necrosis became prominent, and wide-ranging chronic cerebral edema was seen. These pathological findings are strongly correlated with neural functional impairments. © 2014 Wiley Periodicals, Inc.
Chen, David Zhekai
A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…
Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.
In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.
A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input
Schie, H.T. van; Toni, I.; Bekkering, H.
In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and
van Duin, F.; Rosier, P. F.; Bemelmans, B. L.; Wijkstra, H.; Debruyne, F. M.; van Oosterom, A.
This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of
Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh
Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild