Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.
Goffart, Laurent; Bourrelly, Clara; Quinet, Julie
In primates, the appearance of an object moving in the peripheral visual field elicits an interceptive saccade that brings the target image onto the foveae. This foveation is then maintained more or less efficiently by slow pursuit eye movements and subsequent catch-up saccades. Sometimes, the tracking is such that the gaze direction looks spatiotemporally locked onto the moving object. Such a spatial synchronism is quite spectacular when one considers that the target-related signals are transmitted to the motor neurons through multiple parallel channels connecting separate neural populations with different conduction speeds and delays. Because of the delays between the changes of retinal activity and the changes of extraocular muscle tension, the maintenance of the target image onto the fovea cannot be driven by the current retinal signals as they correspond to past positions of the target. Yet, the spatiotemporal coincidence observed during pursuit suggests that the oculomotor system is driven by a command estimating continuously the current location of the target, i.e., where it is here and now. This inference is also supported by experimental perturbation studies: when the trajectory of an interceptive saccade is experimentally perturbed, a correction saccade is produced in flight or after a short delay, and brings the gaze next to the location where unperturbed saccades would have landed at about the same time, in the absence of visual feedback. In this chapter, we explain how such correction can be supported by previous visual signals without assuming "predictive" signals encoding future target locations. We also describe the basic neural processes which gradually yield the synchronization of eye movements with the target motion. When the process fails, the gaze is driven by signals related to past locations of the target, not by estimates to its upcoming locations, and a catch-up is made to reinitiate the synchronization. © 2017 Elsevier B.V. All rights
Reyes, O M; Kopitzke, I; Zimmermann, K-H
Synchronization of neural networks has been studied in recent years as an alternative to cryptographic applications such as the realization of symmetric key exchange protocols. This paper presents a first view of the so-called permutation parity machine, an artificial neural network proposed as a binary variant of the tree parity machine. The dynamics of the synchronization process by mutual learning between permutation parity machines is analytically studied and the results are compared with those of tree parity machines. It will turn out that for neural synchronization, permutation parity machines form a viable alternative to tree parity machines
Emami, Seyede Faranak; Daneshi, Ahmad
Objectives. Vestibular hearing as an auditory sensitivity of the saccule in the human ear is revealed by cervical vestibular evoked myogenic potentials (cVEMPs). The range of the vestibular hearing lies in the low frequency. Also, the amplitude of an auditory brainstem response component depends on the amount of synchronized neural activity, and the auditory nerve fibers' responses have the best synchronization with the low frequency. Thus, the aim of this study was to investigate correlation between vestibular hearing using cVEMPs and neural synchronization via slow wave Auditory Brainstem Responses (sABR). Study Design. This case-control survey was consisted of twenty-two dizzy patients, compared to twenty healthy controls. Methods. Intervention comprised of Pure Tone Audiometry (PTA), Impedance acoustic metry (IA), Videonystagmography (VNG), fast wave ABR (fABR), sABR, and cVEMPs. Results. The affected ears of the dizzy patients had the abnormal findings of cVEMPs (insecure vestibular hearing) and the abnormal findings of sABR (decreased neural synchronization). Comparison of the cVEMPs at affected ears versus unaffected ears and the normal persons revealed significant differences (P < 0.05). Conclusion. Safe vestibular hearing was effective in the improvement of the neural synchronization.
Pettersen Stanford Ur.iversity Artificial Intelligence Laboratory ABSTRACT Th oaoer gives an overview of commonly used synchronization primitives and...wr.ters . ut.l.z.ng the DroDo4d synchronization primitive . The solution is simpler and shorter than other known S’ms The first sections of the paper...un reicr»» side il nrcttaary and Identity by block number) Scheduling, process scheduling, synchronization , mutual exclusion, semaphores , critical
Yang Xinsong; Cao Jinde
In this Letter, we study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Weiner process, and adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this Letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.
Postnov, Dmitry E; Ryazanova, Ludmila S; Mosekilde, Erik
Using a relatively simple model we examine how variations of the extracellular potassium concentration can give rise to synchronization of two nearby pacemaker cells. With the volume of the extracellular space and the rate of potassium diffusion as control parameters, the dual nature of this reso...
Jiang, Jing; Chen, Chuansheng; Dai, Bohan; Shi, Guang; Ding, Guosheng; Liu, Li; Lu, Chunming
The neural mechanism of leader emergence is not well understood. This study investigated (i) whether interpersonal neural synchronization (INS) plays an important role in leader emergence, and (ii) whether INS and leader emergence are associated with the frequency or the quality of communications. Eleven three-member groups were asked to perform a leaderless group discussion (LGD) task, and their brain activities were recorded via functional near infrared spectroscopy (fNIRS)-based hyperscanning. Video recordings of the discussions were coded for leadership and communication. Results showed that the INS for the leader-follower (LF) pairs was higher than that for the follower-follower (FF) pairs in the left temporo-parietal junction (TPJ), an area important for social mentalizing. Although communication frequency was higher for the LF pairs than for the FF pairs, the frequency of leader-initiated and follower-initiated communication did not differ significantly. Moreover, INS for the LF pairs was significantly higher during leader-initiated communication than during follower-initiated communications. In addition, INS for the LF pairs during leader-initiated communication was significantly correlated with the leaders' communication skills and competence, but not their communication frequency. Finally, leadership could be successfully predicted based on INS as well as communication frequency early during the LGD (before half a minute into the task). In sum, this study found that leader emergence was characterized by high-level neural synchronization between the leader and followers and that the quality, rather than the frequency, of communications was associated with synchronization. These results suggest that leaders emerge because they are able to say the right things at the right time.
Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.
Kim, Sang-Yoon; Lim, Woochang
Synchronized brain rhythms, associated with diverse cognitive functions, have been observed in electrical recordings of brain activity. Neural synchronization may be well described by using the population-averaged global potential VG in computational neuroscience. The time-averaged fluctuation of VG plays the role of a "thermodynamic" order parameter O used for describing the synchrony-asynchrony transition in neural systems. Population spike synchronization may be well visualized in the raster plot of neural spikes. The degree of neural synchronization seen in the raster plot is well measured in terms of a "statistical-mechanical" spike-based measure Ms introduced by considering the occupation and the pacing patterns of spikes. The global potential VG is also used to give a reference global cycle for the calculation of Ms. Hence, VG becomes an important collective quantity because it is associated with calculation of both O and Ms. However, it is practically difficult to directly get VG in real experiments. To overcome this difficulty, instead of VG, we employ the instantaneous population spike rate (IPSR) which can be obtained in experiments, and develop realistic thermodynamic and statistical-mechanical measures, based on IPSR, to make practical characterization of the neural synchronization in both computational and experimental neuroscience. Particularly, more accurate characterization of weak sparse spike synchronization can be achieved in terms of realistic statistical-mechanical IPSR-based measure, in comparison with the conventional measure based on VG. Copyright © 2014. Published by Elsevier B.V.
Lu Hongtao; Leeuwen, C. van
We consider the problem of global exponential synchronization between two identical chaotic neural networks that are linearly and unidirectionally coupled. We formulate a general framework for the synchronization problem in which one chaotic neural network, working as the driving system (or master), sends its output or state values to the other, which serves as the response system (or slave). We use Lyapunov functions to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global exponential synchronization regardless of their initial states. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws
Jiang, Jing; Dai, Bohan; Peng, Danling; Zhu, Chaozhe; Liu, Li; Lu, Chunming
Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., telephone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face communication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-face dialog between partners but none during a back-to-back dialog, a face-to-face monologue, or a back-to-back monologue. Moreover, the neural synchronization between partners during the face-to-face dialog resulted primarily from the direct interactions between the partners, including multimodal sensory information integration and turn-taking behavior. The communicating behavior during the face-to-face dialog could be predicted accurately based on the neural synchronization level. These results suggest that face-to-face communication, particularly dialog, has special neural features that other types of communication do not have and that the neural synchronization between partners may underlie successful face-to-face communication.
Chen, Hanshuang; He, Gang; Huang, Feng; Shen, Chuansheng; Hou, Zhonghuai
It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev. Lett. 106, 128701 (2011)] and chaotic oscillators [Leyva et al. Phys. Rev. Lett. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if natural frequencies of the oscillations are positively correlated with node degrees and the width of the frequency distribution is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks, the synchronization transition is always of continuous type regardless of the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.
Abstract Our brains integrate information across sensory modalities to generate perceptual experiences and form memories. However, it is difficult to determine the conditions under which multisensory stimulation will benefit or hinder the retrieval of everyday experiences. We hypothesized that the determining factor is the reliability of information processing during stimulus presentation, which can be measured through intersubject correlation of stimulus-evoked activity. We therefore presented biographical auditory narratives and visual animations to 72 human subjects visually, auditorily, or combined, while neural activity was recorded using electroencephalography. Memory for the narrated information, contained in the auditory stream, was tested 3 weeks later. While the visual stimulus alone led to no meaningful retrieval, this related stimulus improved memory when it was combined with the story, even when it was temporally incongruent with the audio. Further, individuals with better subsequent memory elicited neural responses during encoding that were more correlated with their peers. Surprisingly, portions of this predictive synchronized activity were present regardless of the sensory modality of the stimulus. These data suggest that the strength of sensory and supramodal activity is predictive of memory performance after 3 weeks, and that neural synchrony may explain the mnemonic benefit of the functionally uninformative visual context observed for these real-world stimuli. PMID:27844062
Wang, Ling; Zhao, Hongyong, E-mail: firstname.lastname@example.org
The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability.
Wang, Ling; Zhao, Hongyong
The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability
Carrillo, Richard R; Ros, Eduardo; Barbour, Boris; Boucheny, Christian; Coenen, Olivier
Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.
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.
Jiang, J.; Dai, B.; Peng, D.; Zhu, C.; Liu, L.; Lu, C.
Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., telephone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face communication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-...
Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C
Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.
Full Text Available Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval. This study explores potential mechanisms and functional advantages of this short desynchronizations dynamics using computational neuroscience techniques. We show that short desynchronizations are exhibited in coupled neurons if their delayed rectifier potassium current has relatively large values of the voltage-dependent activation time-constant. The delayed activation of potassium current is associated with generation of quickly-rising action potential. This “spikiness” is a very general property of neurons. This may explain why very different neural systems exhibit short desynchronization dynamics. We also show how the distribution of desynchronization durations may be independent of the synchronization strength. Finally, we show that short desynchronization dynamics requires weaker synaptic input to reach a pre-set synchrony level. Thus, this dynamics allows for efficient regulation of synchrony and may promote efficient formation of synchronous neural assemblies.
Wan, Ying; Cao, Jinde; Wen, Guanghui
In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control
Qian, Tang; Xing-Yuan, Wang
The problem of chaos control and complete synchronization of cellular neural network with delays is studied. Based on the open plus nonlinear closed loop (OPNCL) method, the control scheme and synchronization scheme are designed. Both the schemes can achieve the chaos control and complete synchronization of chaotic neural network respectively, and their validity is further verified by numerical simulation experiments. (general)
Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian
Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and di...
Cui Baotong; Lou Xuyang
In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme
Full Text Available Musical ensemble performance requires temporally precise interpersonal action coordination. To play in synchrony, ensemble musicians presumably rely on anticipatory mechanisms that enable them to predict the timing of sounds produced by co-performers. Previous studies have shown that individuals differ in their ability to predict upcoming tempo changes in paced finger-tapping tasks (indexed by cross-correlations between tap timing and pacing events and that the degree of such prediction influences the accuracy of sensorimotor synchronization (SMS and interpersonal coordination in dyadic tapping tasks. The current functional magnetic resonance imaging study investigated the neural correlates of auditory temporal predictions during SMS in a within-subject design. Hemodynamic responses were recorded from 18 musicians while they tapped in synchrony with auditory sequences containing gradual tempo changes under conditions of varying cognitive load (achieved by a simultaneous visual n-back working-memory task comprising three levels of difficulty: observation only, 1-back, and 2-back object comparisons. Prediction ability during SMS decreased with increasing cognitive load. Results of a parametric analysis revealed that the generation of auditory temporal predictions during SMS recruits (1 a distributed network in cortico-cerebellar motor-related brain areas (left dorsal premotor and motor cortex, right lateral cerebellum, SMA proper and bilateral inferior parietal cortex and (2 medial cortical areas (medial prefrontal cortex, posterior cingulate cortex. While the first network is presumably involved in basic sensory prediction, sensorimotor integration, motor timing, and temporal adaptation, activation in the second set of areas may be related to higher-level social-cognitive processes elicited during action coordination with auditory signals that resemble music performed by human agents.
Smeal, Roy M; Ermentrout, G Bard; White, John A
We review the principal assumptions underlying the application of phase-response curves (PRCs) to synchronization in neuronal networks. The PRC measures how much a given synaptic input perturbs spike timing in a neural oscillator. Among other applications, PRCs make explicit predictions about whether a given network of interconnected neurons will synchronize, as is often observed in cortical structures. Regarding the assumptions of the PRC theory, we conclude: (i) The assumption of noise-tolerant cellular oscillations at or near the network frequency holds in some but not all cases. (ii) Reduced models for PRC-based analysis can be formally related to more realistic models. (iii) Spike-rate adaptation limits PRC-based analysis but does not invalidate it. (iv) The dependence of PRCs on synaptic location emphasizes the importance of improving methods of synaptic stimulation. (v) New methods can distinguish between oscillations that derive from mutual connections and those arising from common drive. (vi) It is helpful to assume linear summation of effects of synaptic inputs; experiments with trains of inputs call this assumption into question. (vii) Relatively subtle changes in network structure can invalidate PRC-based predictions. (viii) Heterogeneity in the preferred frequencies of component neurons does not invalidate PRC analysis, but can annihilate synchronous activity.
Sheng Li; Yang Huizhong; Lou Xuyang
This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.
Lincoln J. Colling
Full Text Available Tapping in time to a metronome beat (hereafter beat synchronization shows considerable variability in child populations, and individual differences in beat synchronization are reliably related to reading development. Children with developmental dyslexia show impairments in beat synchronization. These impairments may reflect deficiencies in auditory perception of the beat which in turn affect auditory-motor mapping, or may reflect an independent motor deficit. Here, we used a new methodology in EEG based on measuring beat-related steady-state evoked potentials (SS-EPs, Nozaradan et al., 2015 in an attempt to disentangle neural sensory and motor contributions to behavioral beat synchronization in children with dyslexia. Children tapped with both their left and right hands to every second beat of a metronome pulse delivered at 2.4 Hz, or listened passively to the beat. Analyses of preferred phase in EEG showed that the children with dyslexia had a significantly different preferred phase compared to control children in all conditions. Regarding SS-EPs, the groups differed significantly for the passive Auditory listening condition at 2.4 Hz, and showed a trend toward a difference in the Right hand tapping condition at 3.6 Hz (sensorimotor integration measure. The data suggest that neural rhythmic entrainment is atypical in children with dyslexia for both an auditory beat and during sensorimotor coupling (tapping. The data are relevant to a growing literature suggesting that rhythm-based interventions may help language processing in children with developmental disorders of language learning.
Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen
This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.
Lakshmanan, Shanmugam; Prakash, Mani; Lim, Chee Peng; Rakkiyappan, Rajan; Balasubramaniam, Pagavathigounder; Nahavandi, Saeid
In this paper, synchronization of an inertial neural network with time-varying delays is investigated. Based on the variable transformation method, we transform the second-order differential equations into the first-order differential equations. Then, using suitable Lyapunov-Krasovskii functionals and Jensen's inequality, the synchronization criteria are established in terms of linear matrix inequalities. Moreover, a feedback controller is designed to attain synchronization between the master and slave models, and to ensure that the error model is globally asymptotically stable. Numerical examples and simulations are presented to indicate the effectiveness of the proposed method. Besides that, an image encryption algorithm is proposed based on the piecewise linear chaotic map and the chaotic inertial neural network. The chaotic signals obtained from the inertial neural network are utilized for the encryption process. Statistical analyses are provided to evaluate the effectiveness of the proposed encryption algorithm. The results ascertain that the proposed encryption algorithm is efficient and reliable for secure communication applications.
Full Text Available This paper addresses the finite-time synchronizing problem for a class of chaotic neural networks. In a real communication network, parameters of the master system may be time-varying and the system may be perturbed by external disturbances. A simple high-gain observer is designed to track all the nonlinearities, unknown system functions, and disturbances. Then, a dynamic active compensatory controller is proposed and by using the singular perturbation theory, the control method can guarantee the finite-time stability of the error system between the master system and the slave system. Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed scheme.
Wan, Ying; Cao, Jinde; Wen, Guanghui; Yu, Wenwu
The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Gan, Qintao; Lv, Tianshi; Fu, Zhenhua
In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.
Ghosh, Dibakar; Banerjee, Santo
Highlights: • Projective synchronization in coupled delayed neural chaotic systems with modulated delay time is introduced. • An adaptive rule for the scaling factors is introduced. • This scheme is highly applicable in secure communication. -- Abstract: In this work, the projective synchronization between two continuous time delayed neural systems with time varying delay is investigated. A sufficient condition for synchronization for the coupled systems with modulated delay is presented analytically with the help of the Krasovskii–Lyapunov approach. The effect of adaptive scaling factors on synchronization are also studied in details. Numerical simulations verify the effectiveness of the analytic results
Sheng, Yin; Zhang, Hao; Zeng, Zhigang
This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.
Henson, Michael A.
The circadian clock generates 24 h rhythms that drive physiological and behavioral processes in a diverse range of organisms including microbes, plants, insects, and mammals. Recent experimental advances have produced improved understanding of the molecular mechanisms involved in circadian rhythm generation at the single cell level. However, the intercellular mechanisms that allow large populations of coupled pacemaker cells to synchronize and coordinate their rhythms remain poorly understood. The purpose of this article is to review recent progress in dynamic modeling of the circadian clock with a focus on multicellular models required to describe cell population synchronization. Mammalian systems are emphasized to illustrate the highly heterogeneous structure and rich dynamical behavior of multicellular circadian systems. Available multicellular models are characterized with respect to their single cell descriptions, intercellular coupling mechanisms, and network topologies. Examples drawn from our own research are used to demonstrate the advantages associated with integrating detailed single cell models within realistic multicellular networks for prediction of mammalian system dynamics. Mathematical modeling is shown to represent a powerful tool for understanding the intracellular and intercellular mechanisms utilized to robustly synchronize large populations of highly heterogeneous and sparsely coupled single cell oscillators. The article concludes with some possible directions for future research
Shi, J. C.; Luo, M.; Huang, C. S.
This work investigates the synchronization transitions in two-way coupled neural system by mean field approach. Results show that, there exists a critical noise intensity for the synchronization transitions, i.e., above (or below) the critical noise intensity, the synchronization transitions are decreased (or hardly change) with increasing the noise intensity. Meanwhile, the heterogeneity effect plays a negative role for the synchronization transitions, and above critical coupling strength, the heterogeneity effect on synchronization transitions can be negligible. Furthermore, when an external signal is introduced into the coupled system, the novel frequency-induced and amplitude-induced synchronization transitions are found, and there exist an optimal frequency and an optimal amplitude of external signal which makes the system to display the best synchronization transitions. In particular, it is observed that the synchronization transitions can not be further affected above critical frequency of external signal.
Prakash, M; Balasubramaniam, P; Lakshmanan, S
This study is mainly concerned with the problem on synchronization criteria for Markovian jumping time delayed bidirectional associative memory neural networks and their applications in secure image communications. Based on the variable transformation method, the addressed second order differential equations are transformed into first order differential equations. Then, by constructing a suitable Lyapunov-Krasovskii functional and based on integral inequalities, the criteria which ensure the synchronization between the uncontrolled system and controlled system are established through designed feedback controllers and linear matrix inequalities. Further, the proposed results proved that the error system is globally asymptotically stable in the mean square. Moreover, numerical illustrations are provided to validate the effectiveness of the derived analytical results. Finally, the application of addressed system is explored via image encryption/decryption process. Copyright © 2016 Elsevier Ltd. All rights reserved.
Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don’t include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results. PMID:28931066
Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian
Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.
Full Text Available Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs with both discrete delay and distributed delay (mixed delays. By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.
, 172 ... the effect of disturbance to an H∞ norm constraint. It is shown that ... unknown chaotic systems; linear matrix inequality (LMI); learning law. 1. Introduction .... (9) is RBFNN H∞ synchronized if the synchronization error e(t) satisfies. ∫ ∞.
Li Yanlong; Ma Jun; Xu Wenke; Li Hongbo; Wu Min
The critical features of a coupling parameter in the synchronization of small world neural networks are investigated. A power law decay form is observed in this spatially extended system, the larger linked degree becomes, the larger critical coupling intensity. There exists maximal and minimal critical coupling intensity for synchronization in the extended system. An approximate synchronization diagram has been constructed. In the case of partial coupling, a primary result is presented about the critical coupling fraction for various linked degree of networks
Park, Ju H.
In this paper, we aim to study global synchronization for neural networks with neutral delay. A dynamic feedback control scheme is proposed to achieve the synchronization between drive network and response network. By utilizing the Lyapunov function and linear matrix inequalities (LMIs), we derive simple and efficient criterion in terms of LMIs for synchronization. The feedback controllers can be easily obtained by solving the derived LMIs.
The readers-writers problem is one of the very well known problems in concurrency theory. It was first introduced by Courtois et.al. in 1971  and requires the synchronization of processes trying to read and write a shared resource. Several readers are allowed to access the resource simultaneously, but a writer must be given exclusive access to that resource. Courtois et.al. gave semaphore-based solutions to what they called the first and second readers-writers problems. Both of their solut...
Yan, J.-J.; Lin, J.-S.; Hung, M.-L.; Liao, T.-L.
We present a systematic design procedure for synchronization of neural networks subject to time-varying delays and sector nonlinearity in the control input. Based on the drive-response concept and the Lyapunov stability theorem, a memoryless decentralized control law is proposed which guarantees exponential synchronization even when input nonlinearity is present. The supplementary requirement that the time-derivative of time-varying delays must be smaller than one is released for the proposed control scheme. A four-dimensional Hopfield neural network with time-varying delays is presented as the illustrative example to demonstrate the effectiveness of the proposed synchronization scheme
Fang Xiaoling; Yu Hongjie; Jiang Zonglai
The chaotic synchronization of Hindmarsh-Rose neural networks linked by a nonlinear coupling function is discussed. The HR neural networks with nearest-neighbor diffusive coupling form are treated as numerical examples. By the construction of a special nonlinear-coupled term, the chaotic system is coupled symmetrically. For three and four neurons network, a certain region of coupling strength corresponding to full synchronization is given, and the effect of network structure and noise position are analyzed. For five and more neurons network, the full synchronization is very difficult to realize. All the results have been proved by the calculation of the maximum conditional Lyapunov exponent.
Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng
In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Mou Chen [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)], E-mail: email@example.com; Jiang Changsheng; Bin Jiang; Wu Qingxian [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)
A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.
Lowet, Eric; Roberts, Mark J.; Bonizzi, Pietro; Karel, Joël; De Weerd, Peter
Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information
Full Text Available Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT preceded by Singular Spectrum Decomposition (SSD of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization
Wang, Dongshu; Huang, Lihong; Tang, Longkun
This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.
Gong, Shuqing; Yang, Shaofu; Guo, Zhenyuan; Huang, Tingwen
The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronization of the derive and response neural networks are derived based on Lyapunov stability theory and some inequality techniques. Finally, several numerical simulations are provided to substantiate the effectiveness of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Yang, Shiju; Li, Chuandong; Huang, Tingwen
The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Zhou Jin; Chen Tianping; Xiang Lan
This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique
Wei, Ruoyu; Cao, Jinde; Alsaedi, Ahmed
This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.
Menendez de la Prida, L; Sanchez-Andres, J V
Synchronization is one of the mechanisms by which the brain encodes information. The observed synchronization of neuronal activity has, however, several levels of fluctuations, which presumably regulate local features of specific areas. This means that biological neural networks should have an intrinsic mechanism able to synchronize the neuronal activity but also to preserve the firing capability of individual cells. Here, we investigate the input-output relationship of a biological neural network from developing mammalian brain, i.e., the hippocampus. We show that the probability of occurrence of synchronous output activity (which consists in stereotyped population bursts recorded throughout the hippocampus) is encoded by a sigmoidal transfer function of the input frequency. Under this scope, low-frequency inputs will not produce any coherent output while high-frequency inputs will determine a synchronous pattern of output activity (population bursts). We analyze the effect of the network size (N) on the parameters of the transfer function (threshold and slope). We found that sigmoidal functions realistically simulate the synchronous output activity of hippocampal neural networks. This outcome is particularly important in the application of results from neural network models to neurobiology.
Pereira, T.; Baptista, M. S.; Kurths, J.
We present an approach which enables to state about the existence of phase synchronization in coupled chaotic oscillators without having to measure the phase. This is done by observing the oscillators at special times, and analyzing whether this set of points is localized. In particular, we show that this approach is fruitful to analyze the onset of phase synchronization in chaotic attractors whose phases are not well defined, as well as, in networks of non-identical spiking/bursting neurons ...
Li, Jiarong; Jiang, Haijun; Hu, Cheng; Yu, Zhiyong
This paper is devoted to the exponential synchronization, finite time synchronization, and fixed-time synchronization of Cohen-Grossberg neural networks (CGNNs) with discontinuous activations and time-varying delays. Discontinuous feedback controller and Novel adaptive feedback controller are designed to realize global exponential synchronization, finite time synchronization and fixed-time synchronization by adjusting the values of the parameters ω in the controller. Furthermore, the settling time of the fixed-time synchronization derived in this paper is less conservative and more accurate. Finally, some numerical examples are provided to show the effectiveness and flexibility of the results derived in this paper. Copyright © 2018 Elsevier Ltd. All rights reserved.
Karageorgiou, Elissaios; Lewis, Scott M.; Riley McCarten, J.; Leuthold, Arthur C.; Hemmy, Laura S.; McPherson, Susan E.; Rottunda, Susan J.; Rubins, David M.; Georgopoulos, Apostolos P.
In previous work (Georgopoulos et al 2007 J. Neural Eng. 4 349-55) we reported on the use of magnetoencephalographic (MEG) synchronous neural interactions (SNI) as a functional biomarker in Alzheimer's dementia (AD) diagnosis. Here we report on the application of canonical correlation analysis to investigate the relations between SNI and cognitive neuropsychological (NP) domains in AD patients. First, we performed individual correlations between each SNI and each NP, which provided an initial link between SNI and specific cognitive tests. Next, we performed factor analysis on each set, followed by a canonical correlation analysis between the derived SNI and NP factors. This last analysis optimally associated the entire MEG signal with cognitive function. The results revealed that SNI as a whole were mostly associated with memory and language, and, slightly less, executive function, processing speed and visuospatial abilities, thus differentiating functions subserved by the frontoparietal and the temporal cortices. These findings provide a direct interpretation of the information carried by the SNI and set the basis for identifying specific neural disease phenotypes according to cognitive deficits.
Sameiro-Barbosa, Catia M; Geiser, Eveline
The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system.
Wang, Weiping; Yuan, Manman; Luo, Xiong; Liu, Linlin; Zhang, Yao
Proportional delay is a class of unbounded time-varying delay. A class of bidirectional associative memory (BAM) memristive neural networks with multiple proportional delays is concerned in this paper. First, we propose the model of BAM memristive neural networks with multiple proportional delays and stochastic perturbations. Furthermore, by choosing suitable nonlinear variable transformations, the BAM memristive neural networks with multiple proportional delays can be transformed into the BAM memristive neural networks with constant delays. Based on the drive-response system concept, differential inclusions theory and Lyapunov stability theory, some anti-synchronization criteria are obtained. Finally, the effectiveness of proposed criteria are demonstrated through numerical examples.
Sameiro-Barbosa, Catia M.; Geiser, Eveline
The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system. PMID:27559306
Yuan, Manman; Wang, Weiping; Luo, Xiong; Li, Lixiang; Kurths, Jürgen; Wang, Xiao
This paper is concerned with the exponential lag function projective synchronization of memristive multidirectional associative memory neural networks (MMAMNNs). First, we propose a new model of MMAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying discrete delays and distributed time delays. Second, we design two kinds of hybrid controllers. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the controllers are carefully designed to confirm the process of different types of synchronization in the MMAMNNs. Third, sufficient criteria guaranteeing the synchronization of system are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.
Boaretto, B. R. R.; Budzinski, R. C.; Prado, T. L.; Kurths, J.; Lopes, S. R.
It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider a network of 2000 thermally sensitive identical neurons, based on the model of Hodgkin-Huxley in a small-world topology, with the probability of adding non local connection equal to p = 0 . 001. Based on experimental protocols to suppress anomalous synchronization, as well as nonstationary behavior of the neural network dynamics, we make use of (i) external stimulus (pulsed current); (ii) biologic parameters changing (neuron membrane conductance changes); and (iii) body temperature changes. Quantification analysis to evaluate phase synchronization makes use of the Kuramoto's order parameter, while recurrence quantification analysis, particularly the determinism, computed over the easily accessible mean field of network, the local field potential (LFP), is used to evaluate nonstationary states. We show that the methods proposed can control the anomalous synchronization and nonstationarity occurring for weak coupling parameter without any effect on the individual neuron dynamics, neither in the expected asymptotic synchronized states occurring for large values of the coupling parameter.
Yang, Shuai; Yu, Juan; Hu, Cheng; Jiang, Haijun
In this paper, without separating the complex-valued neural networks into two real-valued systems, the quasi-projective synchronization of fractional-order complex-valued neural networks is investigated. First, two new fractional-order inequalities are established by using the theory of complex functions, Laplace transform and Mittag-Leffler functions, which generalize traditional inequalities with the first-order derivative in the real domain. Additionally, different from hybrid control schemes given in the previous work concerning the projective synchronization, a simple and linear control strategy is designed in this paper and several criteria are derived to ensure quasi-projective synchronization of the complex-valued neural networks with fractional-order based on the established fractional-order inequalities and the theory of complex functions. Moreover, the error bounds of quasi-projective synchronization are estimated. Especially, some conditions are also presented for the Mittag-Leffler synchronization of the addressed neural networks. Finally, some numerical examples with simulations are provided to show the effectiveness of the derived theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Cao Jinde; Li Ping; Wang Weiwei
This Letter investigates the global exponential synchronization in arrays of coupled identical delayed neural networks (DNNs) with constant and delayed coupling. By referring to Lyapunov functional method and Kronecker product technique, some sufficient conditions are derived for global synchronization of such systems. These new synchronization criteria offer some adjustable matrix parameters, which is of important significance in the design and applications of such coupled DNNs, and the results improve and extend the earlier works. Finally, an example is given to illustrate the theoretical results
Lu Jianquan; Ho, Daniel W.C.; Liu Ming
In this Letter, we study the globally exponential synchronization in an array of linearly coupled neural networks with delayed coupling. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the real-world network. The difficulty arising from the asymmetry of the coupling matrix has been overcame in this work. Some synchronization criteria are given in terms of strict linear matrix inequalities (LMIs), which can be efficiently solved by using interior point algorithm. Some previous synchronization results are generalized. Numerical simulation is also given to verify our theoretical analysis
Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)
Wei Duqu; Luo Xiaoshu
In this paper, we investigate coherence resonance (CR) and noise-induced synchronization in Hindmarsh-Rose (HR) neural network with three different types of topologies: regular, random, and small-world. It is found that the additive noise can induce CR in HR neural network with different topologies and its coherence is optimized by a proper noise level. It is also found that as coupling strength increases the plateau in the measure of coherence curve becomes broadened and the effects of network topology is more pronounced simultaneously. Moreover, we find that increasing the probability p of the network topology leads to an enhancement of noise-induced synchronization in HR neurons network.
Ding, Xiaoshuai; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar
This paper is concerned with the fixed-time synchronization for a class of complex-valued neural networks in the presence of discontinuous activation functions and parameter uncertainties. Fixed-time synchronization not only claims that the considered master-slave system realizes synchronization within a finite time segment, but also requires a uniform upper bound for such time intervals for all initial synchronization errors. To accomplish the target of fixed-time synchronization, a novel feedback control procedure is designed for the slave neural networks. By means of the Filippov discontinuity theories and Lyapunov stability theories, some sufficient conditions are established for the selection of control parameters to guarantee synchronization within a fixed time, while an upper bound of the settling time is acquired as well, which allows to be modulated to predefined values independently on initial conditions. Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system. An example is included to illustrate the proposed methodologies. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.
Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum
Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
... will not cause any race condition if all other threads are accessing from the same context. The result of our experiment shows that context-based synchronization performs better than Java given the same number of threads. Keywords: Aspect-oriented programming, synchronization, resource, and concurrent process.
Chen, Wu-Hua; Lu, Xiaomei; Zheng, Wei Xing
This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.
V. F. Golikov
Full Text Available One of the most efficient ways for identical binary se quences generation is using methods of neural cryptography. The initial weight vestors values influence on speed of synchronization is analized. Equal probability of initial weight vestors motion directions is great advantage. On this base authors suppose new line of research conserned with improvement of network architecture and correction algorithm.
Full Text Available Without assuming the symmetry and irreducibility of the outer-coupling weight configuration matrices, we investigate the pinning synchronization of delayed neural networks with nonlinear inner-coupling. Some delay-dependent controlled stability criteria in terms of linear matrix inequality (LMI are obtained. An example is presented to show the application of the criteria obtained in this paper.
Karimi, Hamid Reza; Maass, Peter
This article aims to present a multiple delayed state-feedback control design for exponential H ∞ synchronization problem of a class of delayed neural networks with multiple time-varying discrete delays. On the basis of the drive-response concept and by introducing a descriptor technique and using Lyapunov-Krasovskii functional, new delay-range-dependent sufficient conditions for exponential H ∞ synchronization of the drive-response structure of neural networks are driven in terms of linear matrix inequalities (LMIs). The explicit expression of the controller gain matrices are parameterized based on the solvability conditions such that the drive system and the response system can be exponentially synchronized. A numerical example is included to illustrate the applicability of the proposed design method.
Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun; Zhongyang Fei; Chaoxu Guan; Huijun Gao; Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun
This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended ( , , )-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.
Full Text Available The generalized projective synchronization (GPS between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.
Seelen, Werner v
In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...
Hua-Guang, Zhang; Tie-Dong, Ma; Jie, Fu; Shao-Cheng, Tong
In this paper, the global impulsive exponential synchronization problem of a class of chaotic delayed neural networks (DNNs) with stochastic perturbation is studied. Based on the Lyapunov stability theory, stochastic analysis approach and an efficient impulsive delay differential inequality, some new exponential synchronization criteria expressed in the form of the linear matrix inequality (LMI) are derived. The designed impulsive controller not only can globally exponentially stabilize the error dynamics in mean square, but also can control the exponential synchronization rate. Furthermore, to estimate the stable region of the synchronization error dynamics, a novel optimization control algorithm is proposed, which can deal with the minimum problem with two nonlinear terms coexisting in LMIs effectively. Simulation results finally demonstrate the effectiveness of the proposed method
Bonacini, E.; Burioni, R.; Di Volo, M.; Groppi, M.; Soresina, C.; Vezzani, A.
The stability of the completely synchronous state in neural networks with electrical coupling is analytically investigated applying both the Master Stability Function approach (MSF), developed by Pecora and Carroll (1998), and the Connection Graph Stability method (CGS) proposed by Belykh et al. (2004). The local dynamics is described by Morris–Lecar model for spiking neurons and by Hindmarsh–Rose model in spike, burst, irregular spike and irregular burst regimes. The combined application of both CGS and MSF methods provides an efficient estimate of the synchronization thresholds, namely bounds for the coupling strength ranges in which the synchronous state is stable. In all the considered cases, we observe that high values of coupling strength tend to synchronize the system. Furthermore, we observe a correlation between the single node attractor and the local stability properties given by MSF. The analytical results are compared with numerical simulations on a sample network, with excellent agreement.
Chen, Jiejie; Chen, Boshan; Zeng, Zhigang
This paper investigates O(t -α )-synchronization and adaptive Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations. Firstly, based on the framework of Filippov solution and differential inclusion theory, using a Razumikhin-type method, some sufficient conditions ensuring the global O(t -α )-synchronization of considered networks are established via a linear-type discontinuous control. Next, a new fractional differential inequality is established and two new discontinuous adaptive controller is designed to achieve Mittag-Leffler synchronization between the drive system and the response systems using this inequality. Finally, two numerical simulations are given to show the effectiveness of the theoretical results. Our approach and theoretical results have a leading significance in the design of synchronized fractional-order memristive neural networks circuits involving discontinuous activations and time-varying delays. Copyright © 2018 Elsevier Ltd. All rights reserved.
Wu, Wei; Cui, Bao-Tong
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H ∞ norm constraint. It is shown that ﬁnding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved ...
Zoë A Englander
Full Text Available Most research investigating the neural basis of social emotions has examined emotions that give rise to negative evaluations of others (e.g. anger, disgust. Emotions triggered by the virtues and excellences of others have been largely ignored. Using fMRI, we investigated the neural basis of two "other-praising" emotions--Moral Elevation (a response to witnessing acts of moral beauty, and Admiration (which we restricted to admiration for physical skill.Ten participants viewed the same nine video clips. Three clips elicited moral elevation, three elicited admiration, and three were emotionally neutral. We then performed pair-wise voxel-by-voxel correlations of the BOLD signal between individuals for each video clip and a separate resting-state run. We observed a high degree of inter-subject synchronization, regardless of stimulus type, across several brain regions during free-viewing of videos. Videos in the elevation condition evoked significant inter-subject synchronization in brain regions previously implicated in self-referential and interoceptive processes, including the medial prefrontal cortex, precuneus, and insula. The degree of synchronization was highly variable over the course of the videos, with the strongest synchrony occurring during portions of the videos that were independently rated as most emotionally arousing. Synchrony in these same brain regions was not consistently observed during the admiration videos, and was absent for the neutral videos.Results suggest that the neural systems supporting moral elevation are remarkably consistent across subjects viewing the same emotional content. We demonstrate that model-free techniques such as inter-subject synchronization may be a useful tool for studying complex, context dependent emotions such as self-transcendent emotion.
Tang, Yang; Fang, Jian-An; Miao, Qing-Ying
In this paper, the problem of adaptive synchronization for a class of stochastic neural networks (SNNs) which involve both mixed delays and Markovian jumping parameters is investigated. The mixed delays comprise the time-varying delays and distributed delays, both of which are mode-dependent. The stochastic perturbations are described in terms of Browian motion. By the adaptive feedback technique, several sufficient criteria have been proposed to ensure the synchronization of SNNs in mean square. Moreover, the proposed adaptive feedback scheme is applied to the secure communication. Finally, the corresponding simulation results are given to demonstrate the usefulness of the main results obtained.
Haras, Consuela; Sauquet, Dominique; Ameline, Philippe; Jaulent, Marie-Christine; Degoulet, Patrice
In a distributed patient record environment, we analyze the processes needed to ensure exchange and access to EHR data. We propose an adapted method and the tools for data synchronization. Our study takes into account the issues of user rights management for data access and of decreasing the amount of data exchanged over the network. We describe a XML-based synchronization model that is portable and independent of specific medical data models. The implemented platform consists of several servers, of local network clients, of workstations running user’s interfaces and of data exchange and synchronization tools. PMID:16779049
Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
Li Yanlong; Ma Jun; Chen Yuhong; Xu Wenke; Wang Yinghai
In this paper the influence of the networks scale on the coupling parameter in the synchronization of neural networks with diluted synapses is investigated. Using numerical simulations, an exponential decay form is observed in the extreme case of global coupling among networks and full connection in each network; the larger linked degree becomes, the larger critical coupling intensity becomes; and the oscillation phenomena in the relationship of critical coupling intensity and the number of neural networks layers in the case of small-scale networks are found
Michael J. Dinneen
Full Text Available We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP, for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems.
Full Text Available Abstract In this paper, an integral sliding mode control approach is presented to investigate synchronization of nonidentical chaotic neural networks with discrete and distributed time-varying delays as well as leakage delay. By considering a proper sliding surface and constructing Lyapunov-Krasovskii functional, as well as employing a combination of the free-weighting matrix method, Newton-Leibniz formulation and inequality technique, a sliding mode controller is designed to achieve the asymptotical synchronization of the addressed nonidentical neural networks. Moreover, a sliding mode control law is also synthesized to guarantee the reachability of the specified sliding surface. The provided conditions are expressed in terms of linear matrix inequalities, and are dependent on the discrete and distributed time delays as well as leakage delay. A simulation example is given to verify the theoretical results.
Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.
Balasubramaniam, P.; Kalpana, M.; Rakkiyappan, R.
Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov—Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method. (interdisciplinary physics and related areas of science and technology)
Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao
Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.
Li, Xiaofan; Fang, Jian-An; Li, Huiyuan
This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E
This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B; Ivanitskii, Genrikh R
Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)
Maggini, Marco; Jain, Lakhmi
This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to ...
Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong
This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.
Könemann, Patrick; Kindler, Ekkart; Unland, Ludger
Models play a central role in model-driven software engineering. There are different kinds of models during the development process, which are related to each other and change over time. Therefore, it is difficult to keep the different models consistent with each other. Consistency of different m...... model versions, and for synchronizing other types of models. The main concern is to apply our concepts to an industrial process, in particular keeping usability and performance in mind. Keyword: Model Differencing, Model Merging, Model Synchronization...
A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.
Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming
In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.
Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L
Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.
Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513
Ursino, Mauro; Magosso, Elisa; Cuppini, Cristiano
Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.
Liu, Qian-qian; Wang, Chun-yan; Shi, Xiao-feng; Li, Wen-dong; Luan, Xiao-ning; Hou, Shi-lin; Zhang, Jin-liang; Zheng, Rong-er
In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.
Full Text Available This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685
Meeri Eeva-Liisa Mäkinen
Full Text Available The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging and temporal resolution microelectrode array (MEA. We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling.
Mäkinen, Meeri Eeva-Liisa; Ylä-Outinen, Laura; Narkilahti, Susanna
The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC)-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA) and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging) and temporal resolution microelectrode array (MEA). We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling. PMID:29559893
Chandrasekar, A; Rakkiyappan, R; Cao, Jinde
This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Liu, Dan; Zhu, Song; Ye, Er
This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin
This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.
Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung
The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.
Li, Dong; Zhou, Changsong
Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576
Budzinski, R. C.; Boaretto, B. R. R.; Prado, T. L.; Lopes, S. R.
We study the stability of asymptotic states displayed by a complex neural network. We focus on the loss of stability of a stationary state of networks using recurrence quantifiers as tools to diagnose local and global stabilities as well as the multistability of a coupled neural network. Numerical simulations of a neural network composed of 1024 neurons in a small-world connection scheme are performed using the model of Braun et al. [Int. J. Bifurcation Chaos 08, 881 (1998), 10.1142/S0218127498000681], which is a modified model from the Hodgkin-Huxley model [J. Phys. 117, 500 (1952)]. To validate the analyses, the results are compared with those produced by Kuramoto's order parameter [Chemical Oscillations, Waves, and Turbulence (Springer-Verlag, Berlin Heidelberg, 1984)]. We show that recurrence tools making use of just integrated signals provided by the networks, such as local field potential (LFP) (LFP signals) or mean field values bring new results on the understanding of neural behavior occurring before the synchronization states. In particular we show the occurrence of different stationary and nonstationarity asymptotic states.
Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.
The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.
Full Text Available The paper addresses the issue of synchronization of memristive bidirectional associative memory neural networks (MBAMNNs with mixed time-varying delays and stochastic perturbation via a sampled-data controller. First, we propose a new model of MBAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying distributed delays and discrete delays. Second, we design a new method of sampled-data control for the stochastic MBAMNNs. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the methods are carefully designed to confirm the synchronization processes are suitable for the feather of the memristor. Third, sufficient criteria guaranteeing the synchronization of the systems are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.
Kindler, Ekkart; Könemann, Patrick; Unland, Ludger
of different models is maintained manually in many cases today. This paper presents an approach for automated model differencing, so that the differences between two model versions (called delta) can be extracted and stored. It can then be re-used independently of the models it was created from...... to interactively merge different model versions, and for synchronizing other types of models. The main concern was to apply our concepts to an industrial process, so usability and performance were important issues....
Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing
In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.
Hu, Cheng; Yu, Juan; Chen, Zhanheng; Jiang, Haijun; Huang, Tingwen
In this paper, the fixed-time stability of dynamical systems and the fixed-time synchronization of coupled discontinuous neural networks are investigated under the framework of Filippov solution. Firstly, by means of reduction to absurdity, a theorem of fixed-time stability is established and a high-precision estimation of the settling-time is given. It is shown by theoretic proof that the estimation bound of the settling time given in this paper is less conservative and more accurate compared with the classical results. Besides, as an important application, the fixed-time synchronization of coupled neural networks with discontinuous activation functions is proposed. By designing a discontinuous control law and using the theory of differential inclusions, some new criteria are derived to ensure the fixed-time synchronization of the addressed coupled networks. Finally, two numerical examples are provided to show the effectiveness and validity of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available The sliding mode control has the merits with respect to the variation of the disturbance and robustness. In this paper, the sensorless sliding-mode observer with least mean squared error approach for permanent magnet synchronous motor (PMSM to detect the rotor position by counter electromotive force and then compute motor speed is designed and implemented. In addition, the neural network control is also used to compensate the PI gain tuning to increase the speed accuracy without regarding the errors of the current measurement and motor noise. In this paper, a digital signal processor TMS320F2812 utilizes its high-speed ADC module to get current feedback information and thus to estimate the rotor position and takes advantage of the built-in modules to achieve SVPWM current control so that the senseless speed control will be accomplished. The correctness and effectiveness of the proposed control system will be verified from the experimental results.
Full Text Available A novel approach is presented to realize the optimal exponential synchronization of nonidentical multiple time-delay chaotic (MTDC systems via fuzzy control scheme. A neural-network (NN model is first constructed for the MTDC system. Then, a linear differential inclusion (LDI state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, a delay-dependent exponential stability criterion of the error system derived in terms of Lyapunov's direct method is proposed to guarantee that the trajectories of the slave system can approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI. According to the LMI, a fuzzy controller is synthesized not only to realize the exponential synchronization but also to achieve the optimal performance by minimizing the disturbance attenuation level at the same time. Finally, a numerical example with simulations is given to demonstrate the effectiveness of our approach.
Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian
This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Pasikova, N V; Marchenko, V G; Kositsyn, N S
Studies in long-term isolated areas of the rat neocortex were performed to investigate the dynamics of the numbers and areas of nerve cell bodies in layer V and to compare these data with the degree of synchronization of epileptic discharges evoked by application of penicillin. Decreases in the number of pyramidal neurons with body areas of 200-350 microm2 in isolated strips after maintenance for 30 and 90 days led to decreases in the degree of synchronization of epileptiform potentials. Large pyramidal neurons are known to have long horizontal axon collaterals, spreading into layers V and VI of the neocortex. It is suggested that the neural networks formed by large pyramidal neurons by means of their long horizontal collaterals mediate the process of intracortical synchronization.
Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B [Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation); Ivanitskii, Genrikh R [Institute for Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation)
Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)
Stamova, Ivanka; Stamov, Gani
In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives. Copyright © 2017 Elsevier Ltd. All rights reserved.
Peng, Xiao; Wu, Huaiqin; Song, Ka; Shi, Jiaxin
This paper is concerned with the global Mittag-Leffler synchronization and the synchronization in finite time for fractional-order neural networks (FNNs) with discontinuous activations and time delays. Firstly, the properties with respect to Mittag-Leffler convergence and convergence in finite time, which play a critical role in the investigation of the global synchronization of FNNs, are developed, respectively. Secondly, the novel state-feedback controller, which includes time delays and discontinuous factors, is designed to realize the synchronization goal. By applying the fractional differential inclusion theory, inequality analysis technique and the proposed convergence properties, the sufficient conditions to achieve the global Mittag-Leffler synchronization and the synchronization in finite time are addressed in terms of linear matrix inequalities (LMIs). In addition, the upper bound of the setting time of the global synchronization in finite time is explicitly evaluated. Finally, two examples are given to demonstrate the validity of the proposed design method and theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Zhao, Zi-Fang; Li, Xue-Zhu; Wan, You
The local field potential (LFP) is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood (SL) is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit (GPU)-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes, delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals (like EEG and fMRI) using similar recording techniques.
Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.
Zou, Yisheng; Ding, Guofu; Zhang, Weihua; Zhang, Jian; Qin, Shengfeng; Tan, John Kian
When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system's dynamic behaviors because it involves multi-disciplinary subsystems. Currently,a most practical approach for multi-disciplinary simulation is interface based coupling simulation method, but it faces a twofold challenge: spatial and time unsynchronizations among multi-directional coupling simulation of subsystems. A new collaborative simulation method with spatiotemporal synchronization process control is proposed for coupling simulating a given complex mechatronics system across multiple subsystems on different platforms. The method consists of 1) a coupler-based coupling mechanisms to define the interfacing and interaction mechanisms among subsystems, and 2) a simulation process control algorithm to realize the coupling simulation in a spatiotemporal synchronized manner. The test results from a case study show that the proposed method 1) can certainly be used to simulate the sub-systems interactions under different simulation conditions in an engineering system, and 2) effectively supports multi-directional coupling simulation among multi-disciplinary subsystems. This method has been successfully applied in China high speed train design and development processes, demonstrating that it can be applied in a wide range of engineering systems design and simulation with improved efficiency and effectiveness.
Alexandru Bârlea; Olivian Chiver
Starting synchronous motors can be achieved by several ethods: starting with an auxiliary motor launch, starting in asynchronous regim, by feeding from a variable frequency source, auto-synchronization with the network.. In our case we study the transient processes in a asynchronous regim . In this case the synchronous motor is started like a squirrel cage induction motor . To start, the synchronous motor is equipped with a starting winding cage placed in the pole pieces of polar inducers; la...
Ma, Shuo; Kang, Yanmei
In this paper, the exponential synchronization of stochastic neutral-type neural networks with time-varying delay and Lévy noise under non-Lipschitz condition is investigated for the first time. Using the general Itô's formula and the nonnegative semi-martingale convergence theorem, we derive general sufficient conditions of two kinds of exponential synchronization for the drive system and the response system with adaptive control. Numerical examples are presented to verify the effectiveness of the proposed criteria.
Li, Xuanying; Li, Xiaotong; Hu, Cheng
In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.
De Vos, Astrid; Vanvooren, Sophie; Vanderauwera, Jolijn; Ghesquière, Pol; Wouters, Jan
Recent evidence suggests that a fundamental deficit in the synchronization of neural oscillations to temporal information in speech may underlie phonological processing problems in dyslexia. Since previous studies were performed cross-sectionally in school-aged children or adults, developmental aspects of neural auditory processing in relation to reading acquisition and dyslexia remain to be investigated. The present longitudinal study followed 68 children during development from pre-reader (5 years old) to beginning reader (7 years old) and more advanced reader (9 years old). Thirty-six children had a family risk for dyslexia and 14 children eventually developed dyslexia. EEG recordings of auditory steady-state responses to 4 and 20 Hz modulations, corresponding to syllable and phoneme rates, were collected at each point in time. Our results demonstrate an increase in neural synchronization to phoneme-rate modulations around the onset of reading acquisition. This effect was negatively correlated with later reading and phonological skills, indicating that children who exhibit the largest increase in neural synchronization to phoneme rates, develop the poorest reading and phonological skills. Accordingly, neural synchronization to phoneme-rate modulations was found to be significantly higher in beginning and more advanced readers with dyslexia. We found no developmental effects regarding neural synchronization to syllable rates, nor any effects of a family risk for dyslexia. Altogether, our findings suggest that the onset of reading instruction coincides with an increase in neural responsiveness to phoneme-rate modulations, and that the extent of this increase is related to (the outcome of) reading development. Hereby, dyslexic children persistently demonstrate atypically high neural synchronization to phoneme rates from the beginning of reading acquisition onwards. Copyright © 2017 Elsevier Ltd. All rights reserved.
Jiancheng, Shi; Min, Luo; Chusheng, Huang
The cooperative effect of random coupling strength and time-periodic coupling strengh on synchronization transitions in one-way coupled neural system has been investigated by mean field approach. Results show that cooperative coupling strength (CCS) plays an active role for the enhancement of synchronization transitions. There exist an optimal frequency of CCS which makes the system display the best CCS-induced synchronization transitions, a critical frequency of CCS which can not further affect the CCS-induced synchronization transitions, and a critical amplitude of CCS which can not occur the CCS-induced synchronization transitions. Meanwhile, noise intensity plays a negative role for the CCS-induced synchronization transitions. Furthermore, it is found that the novel CCS amplitude-induced synchronization transitions and CCS frequency-induced synchronization transitions are found.
Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui
This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.
Lu, Wenlian; Zheng, Ren; Chen, Tianping
In this paper, we discuss outer-synchronization of the asymmetrically connected recurrent time-varying neural networks. By using both centralized and decentralized discretization data sampling principles, we derive several sufficient conditions based on three vector norms to guarantee that the difference of any two trajectories starting from different initial values of the neural network converges to zero. The lower bounds of the common time intervals between data samples in centralized and decentralized principles are proved to be positive, which guarantees exclusion of Zeno behavior. A numerical example is provided to illustrate the efficiency of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Liu Mingzhe; Wang Ruili; Jiang Rui; Hu Maobin; Gao Yang
The effects of a single local defect in synchronous asymmetric exclusion processes are investigated via theoretical analysis and Monte Carlo simulations. Our theoretical analysis shows that there are four possible stationary phases, i.e., the (low density, low density), (low density, high density), (high density, low density) and (high density, high density) in the system. In the (high density, low density) phase, the system can reach a maximal current which is determined by the local defect, but independent of boundary conditions. A phenomenological domain wall approach is developed to predict dynamic behavior at phase boundaries. The effects of defective hopping probability p on density profiles and currents are investigated. Our investigation shows that the value of p determines phase transitions when entrance rate α and exit rate β are fixed. Density profiles and currents obtained from theoretical calculations are in agreement with Monte Carlo simulations
Ruiz, María Herrojo; Koelsch, Stefan; Bhattacharya, Joydeep
The present study investigated the neural correlates associated with the processing of music-syntactical irregularities as compared with regular syntactic structures in music. Previous studies reported an early ( approximately 200 ms) right anterior negative component (ERAN) by traditional event-related-potential analysis during music-syntactical irregularities, yet little is known about the underlying oscillatory and synchronization properties of brain responses which are supposed to play a crucial role in general cognition including music perception. First we showed that the ERAN was primarily represented by low frequency (music-syntactical irregularities as compared with music-syntactical regularities, were associated with (i) an early decrease in the alpha band (9-10 Hz) phase synchronization between right fronto-central and left temporal brain regions, and (ii) a late ( approximately 500 ms) decrease in gamma band (38-50 Hz) oscillations over fronto-central brain regions. These results indicate a weaker degree of long-range integration when the musical expectancy is violated. In summary, our results reveal neural mechanisms of music-syntactic processing that operate at different levels of cortical integration, ranging from early decrease in long-range alpha phase synchronization to late local gamma oscillations. 2008 Wiley-Liss, Inc.
Bächinger, Marc; Zerbi, Valerio; Moisa, Marius; Polania, Rafael; Liu, Quanying; Mantini, Dante; Ruff, Christian; Wenderoth, Nicole
Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individual's α rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain's intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity. SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to
Ding, Xiaoshuai; Cao, Jinde; Zhao, Xuan; Alsaadi, Fuad E
This paper is concerned with the drive-response synchronization for a class of fractional-order bidirectional associative memory neural networks with time delays, as well as in the presence of discontinuous activation functions. The global existence of solution under the framework of Filippov for such networks is firstly obtained based on the fixed-point theorem for condensing map. Then the state feedback and impulsive controllers are, respectively, designed to ensure the Mittag-Leffler synchronization of these neural networks and two new synchronization criteria are obtained, which are expressed in terms of a fractional comparison principle and Razumikhin techniques. Numerical simulations are presented to validate the proposed methodologies.
Mudrik, Liad; Shalgi, Shani; Lamy, Dominique; Deouell, Leon Y
Whether contextual regularities facilitate perceptual stages of scene processing is widely debated, and empirical evidence is still inconclusive. Specifically, it was recently suggested that contextual violations affect early processing of a scene only when the incongruent object and the scene are presented a-synchronously, creating expectations. We compared event-related potentials (ERPs) evoked by scenes that depicted a person performing an action using either a congruent or an incongruent object (e.g., a man shaving with a razor or with a fork) when scene and object were presented simultaneously. We also explored the role of attention in contextual processing by using a pre-cue to direct subjects׳ attention towards or away from the congruent/incongruent object. Subjects׳ task was to determine how many hands the person in the picture used in order to perform the action. We replicated our previous findings of frontocentral negativity for incongruent scenes that started ~ 210 ms post stimulus presentation, even earlier than previously found. Surprisingly, this incongruency ERP effect was negatively correlated with the reaction times cost on incongruent scenes. The results did not allow us to draw conclusions about the role of attention in detecting the regularity, due to a weak attention manipulation. By replicating the 200-300 ms incongruity effect with a new group of subjects at even earlier latencies than previously reported, the results strengthen the evidence for contextual processing during this time window even when simultaneous presentation of the scene and object prevent the formation of prior expectations. We discuss possible methodological limitations that may account for previous failures to find this an effect, and conclude that contextual information affects object model selection processes prior to full object identification, with semantic knowledge activation stages unfolding only later on. Copyright © 2014 Elsevier Ltd. All rights reserved.
Full Text Available Abstract Background Attention-Deficit/Hyperactivity Disorder (ADHD is a prevalent, complex disorder which is characterized by symptoms of inattention, hyperactivity, and impulsivity. Convergent evidence from neurobiological studies of ADHD identifies dysfunction in fronto-striatal-cerebellar circuitry as the source of behavioural deficits. Recent studies have shown that regions governing basic sensory processing, such as the somatosensory cortex, show abnormalities in those with ADHD suggesting that these processes may also be compromised. Methods We used event-related magnetoencephalography (MEG to examine patterns of cortical rhythms in the primary (SI and secondary (SII somatosensory cortices in response to median nerve stimulation, in 9 adults with ADHD and 10 healthy controls. Stimuli were brief (0.2 ms non-painful electrical pulses presented to the median nerve in two counterbalanced conditions: unpredictable and predictable stimulus presentation. We measured changes in strength, synchronicity, and frequency of cortical rhythms. Results Healthy comparison group showed strong event-related desynchrony and synchrony in SI and SII. By contrast, those with ADHD showed significantly weaker event-related desynchrony and event-related synchrony in the alpha (8–12 Hz and beta (15–30 Hz bands, respectively. This was most striking during random presentation of median nerve stimulation. Adults with ADHD showed significantly shorter duration of beta rebound in both SI and SII except for when the onset of the stimulus event could be predicted. In this case, the rhythmicity of SI (but not SII in the ADHD group did not differ from that of controls. Conclusion Our findings suggest that somatosensory processing is altered in individuals with ADHD. MEG constitutes a promising approach to profiling patterns of neural activity during the processing of sensory input (e.g., detection of a tactile stimulus, stimulus predictability and facilitating our
Kim, Sang-Yoon; Lim, Woochang
We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D
Kim, Sang-Yoon; Lim, Woochang
We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D
Gregory S. Duane
Full Text Available The synchronization of loosely-coupled chaotic oscillators, a phenomenon investigated intensively for the last two decades, may realize the philosophical concept of “synchronicity”—the commonplace notion that related eventsmysteriously occur at the same time. When extended to continuous media and/or large discrete arrays, and when general (non-identical correspondences are considered between states, intermittent synchronous relationships indeed become ubiquitous. Meaningful synchronicity follows naturally if meaningful events are identified with coherent structures, defined by internal synchronization between remote degrees of freedom; a condition that has been posited as necessary for synchronizability with an external system. The important case of synchronization between mind and matter is realized if mind is analogized to a computer model, synchronizing with a sporadically observed system, as in meteorological data assimilation. Evidence for the ubiquity of synchronization is reviewed along with recent proposals that: (1 synchronization of different models of the same objective process may be an expeditious route to improved computational modeling and may also describe the functioning of conscious brains; and (2 the nonlocality in quantum phenomena implied by Bell’s theorem may be explained in a variety of deterministic (hidden variable interpretations if the quantum world resides on a generalized synchronization “manifold”.
Effects of coupling distance on synchronization and coherence of chaotic neurons in complex networks are numerically investigated. We find that it is not beneficial to neurons synchronization if confining the coupling distance of random edges to a limit d max , but help to improve their coherence. Moreover, there is an optimal value of d max at which the coherence is maximum.
Yang, Wengui; Yu, Wenwu; Cao, Jinde; Alsaadi, Fuad E; Hayat, Tasawar
This paper investigates the stability and lag synchronization for memristor-based fuzzy Cohen-Grossberg bidirectional associative memory (BAM) neural networks with mixed delays (asynchronous time delays and continuously distributed delays) and impulses. By applying the inequality analysis technique, homeomorphism theory and some suitable Lyapunov-Krasovskii functionals, some new sufficient conditions for the uniqueness and global exponential stability of equilibrium point are established. Furthermore, we obtain several sufficient criteria concerning globally exponential lag synchronization for the proposed system based on the framework of Filippov solution, differential inclusion theory and control theory. In addition, some examples with numerical simulations are given to illustrate the feasibility and validity of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available The exponential synchronization issue for stochastic neural networks (SNNs with mixed time delays and Markovian jump parameters using sampled-data controller is investigated. Based on a novel Lyapunov-Krasovskii functional, stochastic analysis theory, and linear matrix inequality (LMI approach, we derived some novel sufficient conditions that guarantee that the master systems exponentially synchronize with the slave systems. The design method of the desired sampled-data controller is also proposed. To reflect the most dynamical behaviors of the system, both Markovian jump parameters and stochastic disturbance are considered, where stochastic disturbances are given in the form of a Brownian motion. The results obtained in this paper are a little conservative comparing the previous results in the literature. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.
Russo, Giovanni; Shorten, Robert
This paper is concerned with the study of common noise-induced synchronization phenomena in complex networks of diffusively coupled nonlinear systems. We consider the case where common noise propagation depends on the network state and, as a result, the noise diffusion process at the nodes depends on the state of the network. For such networks, we present an algebraic sufficient condition for the onset of synchronization, which depends on the network topology, the dynamics at the nodes, the coupling strength and the noise diffusion. Our result explicitly shows that certain noise diffusion processes can drive an unsynchronized network towards synchronization. In order to illustrate the effectiveness of our result, we consider two applications: collective decision processes and synchronization of chaotic systems. We explicitly show that, in the former application, a sufficiently large noise can drive a population towards a common decision, while, in the latter, we show how common noise can synchronize a network of Lorentz chaotic systems.
Benedek, Mathias; Bergner, Sabine; Konen, Tanja; Fink, Andreas; Neubauer, Aljoscha C.
Synchronization of EEG alpha activity has been referred to as being indicative of cortical idling, but according to more recent evidence it has also been associated with active internal processing and creative thinking. The main objective of this study was to investigate to what extent EEG alpha synchronization is related to internal processing…
DeMarco, Andrew T; Wilson, Stephen M; Rising, Kindle; Rapcsak, Steven Z; Beeson, Pélagie M
We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.
Full Text Available This paper is concerned with the problem of the nonlinear dynamic surface control (DSC of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.
Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang
This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.
Hao Tam Ho
Full Text Available Integrating emotional information from multiple sensory modalities is generally assumed to be a pre-attentive process (de Gelder et al., 1999. This assumption, however, presupposes that the integrative process occurs independent of attention. Using event-potentials (ERP the present study investigated whether the neural processes underlying the integration of dynamic facial expression and emotional prosody is indeed unaffected by attentional manipulations. To this end, participants were presented with congruent and incongruent face-voice combinations (eg, an angry face combined with a neutral voice and performed different two-choice tasks in four consecutive blocks. Three of the tasks directed the participants' attention to emotion expressions in the face, the voice or both. The fourth task required participants to attend to the synchronicity between voice and lip movements. The results show divergent modulations of early ERP components by the different attentional manipulations. For example, when attention was directed to the face (or the voice, incongruent stimuli elicited a reduced N1 as compared to congruent stimuli. This effect was absent, when attention was diverted away from the emotionality in both face and voice suggesting that the detection of emotional incongruence already requires attention. Based on these findings, we question whether multisensory integration of emotion occurs indeed pre-attentively.
Fuchs, Einat; Ayali, Amir; Ben-Jacob, Eshel; Boccaletti, Stefano
Modular organization is a special feature shared by many biological and social networks alike. It is a hallmark for systems exhibiting multitasking, in which individual tasks are performed by separated and yet coordinated functional groups. Understanding how networks of segregated modules develop to support coordinated multitasking functionalities is the main topic of the current study. Using simulations of biologically inspired neuronal networks during development, we study the formation of functional groups (cliques) and inter-neuronal synchronization. The results indicate that synchronization cliques first develop locally according to the explicit network topological organization. Later on, at intermediate connectivity levels, when networks have both local segregation and long-range integration, new synchronization cliques with distinctive properties are formed. In particular, by defining a new measure of synchronization centrality, we identify at these developmental stages dominant neurons whose functional centrality largely exceeds the topological one. These are generated mainly in a few dominant clusters that become the centers of the newly formed synchronization cliques. We show that by the local synchronization properties at the very early developmental stages, it is possible to predict with high accuracy which clusters will become dominant in later stages of network development
Daisuke N Saito
Full Text Available Eye contact provides a communicative link between humans, prompting joint attention. As spontaneous brain activity may have an important role in coordination of neuronal processing within the brain, their inter-subject synchronization may occur during eye contact. To test this, we conducted simultaneous functional MRI in pairs of adults. Eye contact was maintained at baseline while the subjects engaged in real-time gaze exchange in a joint attention task. Averted gaze activated the bilateral occipital pole extending to the right posterior superior temporal sulcus, the dorso-medial prefrontal cortex, and bilateral inferior frontal gyrus. Following a partner’s gaze towards an object activated the left intraparietal sulcus. After all task-related effects were modeled out, inter-individual correlation analysis of residual time-courses was performed. Paired subjects showed more prominent correlations than non-paired subjects in the right inferior frontal gyrus, suggesting that this region is involved in sharing intention during eye contact that provides the context for joint attention.
Full Text Available A permanent magnet (PM synchronous generator system driven by wind turbine (WT, connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN and amended particle swarm optimization (PSO to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid.
Chen, Wu-Hua; Luo, Shixian; Zheng, Wei Xing
This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.
Vogt, M. C.
Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can
Vogt, Michael C.; Skubal, Laura R.
Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical 'signatures' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration; the calibration, sensing, and processing methods of these active voltammetric microsensors can detect, recognize, and
Sivakumar, Siddharth S; Namath, Amalia G; Galán, Roberto F
Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10-20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10-16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in
Kang, Dongwoo; Lee, Eonseok; Kim, Hyunchang; Choi, Young-Man; Lee, Seunghyun; Kim, Inyoung; Yoon, Dukkyun; Jo, Jeongdai; Kim, Bongmin; Lee, Taik-Min
Offset printing processes are promising candidates for producing printed electronics due to their capacity for fine patterning and suitability for mass production. To print high-resolution patterns with good overlay using offset printing, the velocities of two contact surfaces, which ink is transferred between, should be synchronized perfectly. However, an exact velocity of the contact surfaces is unknown due to several imperfections, including tolerances, blanket swelling, and velocity ripple, which prevents the system from being operated in the synchronized condition. In this paper, a novel method of measurement based on the sticking model of friction force was proposed to determine the best synchronized condition, i.e., the condition in which the rate of synchronization error is minimized. It was verified by experiment that the friction force can accurately represent the rate of synchronization error. Based on the measurement results of the synchronization error, the allowable margin of synchronization error when printing high-resolution patterns was investigated experimentally using reverse offset printing. There is a region where the patterning performance is unchanged even though the synchronization error is varied, and this may be viewed as indirect evidence that printability performance is secured when there is no slip at the contact interface. To understand what happens at the contact surfaces during ink transfer, the deformation model of the blanket's surface was developed. The model estimates how much deformation on the blanket's surface can be borne by the synchronization error when there is no slip at the contact interface. In addition, the model shows that the synchronization error results in scale variation in the machine direction (MD), which means that the printing registration in the MD can be adjusted actively by controlling the synchronization if there is a sufficient margin of synchronization error to guarantee printability. The effect of
Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Portes, Jacob P.; Timerman, Dmitriy
Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying patterns of excitatory neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI. PMID:27974609
Vries, de B.
In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns that are successively presented to a neural net. In the approach presented here segmentation is avoided.
Understanding neural mechanisms of social interaction is important for understanding human social nature and for developing treatments for social deficits related to disorders such as autism. However, conventional cognitive and behavioral neuroscience has concentrated on developing novel experimental paradigms and investigating human–computer interactions, rather than studying interpersonal interaction per se. To fully understand neural mechanisms of human interpersonal interaction, we will l...
Barbara F. M. Marino
Full Text Available We investigated the conceptual processing of nouns referring to objects characterized by a highly typical color and orientation. We used a go/no-go task in which we asked participants to categorize each noun as referring or not to natural entities (e.g., animals after a selective adaptation of color-edge neurons in the posterior LV4 region of the visual cortex was induced by means of a McCollough effect procedure. This manipulation affected categorization: the green-vertical adaptation led to slower responses than the green-horizontal adaptation, regardless of the specific color and orientation of the to-be-categorized noun. This result suggests that the conceptual processing of natural entities may entail the activation of modality-specific neural channels with weights proportional to the reliability of the signals produced by these channels during actual perception. This finding is discussed with reference to the debate about the grounded cognition view.
Nicholas W. Simon
Full Text Available Immaturities in adolescent reward processing are thought to contribute to poor decision making and increased susceptibility to develop addictive and psychiatric disorders. Very little is known; however, about how the adolescent brain processes reward. The current mechanistic theories of reward processing are derived from adult models. Here we review recent research focused on understanding of how the adolescent brain responds to rewards and reward-associated events. A critical aspect of this work is that age-related differences are evident in neuronal processing of reward-related events across multiple brain regions even when adolescent rats demonstrate behavior similar to adults. These include differences in reward processing between adolescent and adult rats in orbitofrontal cortex and dorsal striatum. Surprisingly, minimal age related differences are observed in ventral striatum, which has been a focal point of developmental studies. We go on to discuss the implications of these differences for behavioral traits affected in adolescence, such as impulsivity, risk-taking, and behavioral flexibility. Collectively, this work suggests that reward-evoked neural activity differs as a function of age and that regions such as the dorsal striatum that are not traditionally associated with affective processing in adults may be critical for reward processing and psychiatric vulnerability in adolescents.
Park, Choongseok; Worth, Robert M.; Rubchinsky, Leonid L.
Synchronous oscillatory dynamics is frequently observed in the human brain. We analyze the fine temporal structure of phase-locking in a realistic network model and match it with the experimental data from Parkinsonian patients. We show that the experimentally observed intermittent synchrony can be generated just by moderately increased coupling strength in the basal ganglia circuits due to the lack of dopamine. Comparison of the experimental and modeling data suggest that brain activity in Parkinson's disease resides in the large boundary region between synchronized and nonsynchronized dynamics. Being on the edge of synchrony may allow for easy formation of transient neuronal assemblies.
Abakarova, N.S.; Aliev, K.M.; Ibragimov, Kh.O.; Kamilov, I.K. [Institute of Physics, Dagestan Science Centre, RAS, Makhachkala (Russian Federation)
The effect of an external harmonic signal on the screw instability of the current in the electron-hole plasma has been studied experimentally in Ge at 77 K and 300 K. The influence exerted by external signals with various amplitudes and frequencies, applied to a sample both additively and multiplicatively, on the synchronization, amplification and stability of the system in absolute and convective modes of instability excitation has been investigated at points of bifurcation in a wide region of the parametric space. (author)
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 10 11 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, Kuehn 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
Ono, Kentaro; Nakamura, Akinori; Maess, Burkhard
For orchestra musicians, synchronized playing under a conductor’s direction is necessary to achieve optimal performance. Previous studies using simple auditory/visual stimuli have reported cortico-subcortical networks underlying synchronization and that training improves the accuracy of synchronization. However, it is unclear whether people who played regularly under a conductor and non-musicians activate the same networks when synchronizing with a conductor’s gestures. We conducted a functional magnetic resonance imaging (fMRI) experiment testing nonmusicians and musicians who regularly play music under a conductor. Participants were required to tap the rhythm they perceived from silent movies displaying either conductor’s gestures or a swinging metronome. Musicians performed tapping under a conductor with more precision than nonmusicians. Results from fMRI measurement showed greater activity in the anterior part of the left superior frontal gyrus (SFG) in musicians with more frequent practice under a conductor. Conversely, tapping with the metronome did not show any difference between musicians and nonmusicians, indicating that the expertize effect in tapping under the conductor does not result in a general increase in tapping performance for musicians. These results suggest that orchestra musicians have developed an advanced ability to predict conductor’s next action from the gestures. PMID:25883561
Full Text Available For orchestra musicians, synchronized playing under a conductor’s direction is necessary to achieve optimal performance. Previous studies using simple auditory/visual stimuli have reported cortico-subcortical networks underlying synchronization and that training improves the accuracy of synchronization. However, it is unclear whether people who played regularly under a conductor and non-musicians activate the same networks when synchronizing with a conductor’s gestures. We conducted a functional magnetic resonance imaging (fMRI experiment testing nonmusicians and musicians who regularly play music under a conductor. Participants were required to tap the rhythm they perceived from silent movies displaying either conductor’s gestures or a swinging metronome. Musicians performed tapping under a conductor with more precision than nonmusicians. Results from fMRI measurement showed greater activity in the anterior part of the left superior frontal gyrus (SFG in musicians with more frequent practice under a conductor. Conversely, tapping with the metronome did not show any difference between musicians and nonmusicians, indicating that the expertise effect in tapping under the conductor does not result in a general increase in tapping performance for musicians. These results suggest that orchestra musicians have developed an advanced ability to predict conductor’s next action from the gestures.
Rakshit, Sarbendu; Bera, Bidesh K.; Ghosh, Dibakar; Sinha, Sudeshna
We study synchronization of dynamical systems coupled in time-varying network architectures, composed of two or more network topologies, corresponding to different interaction schemes. As a representative example of this class of time-varying hypernetworks, we consider coupled Hindmarsh-Rose neurons, involving two distinct types of networks, mimicking interactions that occur through the electrical gap junctions and the chemical synapses. Specifically, we consider the connections corresponding to the electrical gap junctions to form a small-world network, while the chemical synaptic interactions form a unidirectional random network. Further, all the connections in the hypernetwork are allowed to change in time, modeling a more realistic neurobiological scenario. We model this time variation by rewiring the links stochastically with a characteristic rewiring frequency f . We find that the coupling strength necessary to achieve complete neuronal synchrony is lower when the links are switched rapidly. Further, the average time required to reach the synchronized state decreases as synaptic coupling strength and/or rewiring frequency increases. To quantify the local stability of complete synchronous state we use the Master Stability Function approach, and for global stability we employ the concept of basin stability. The analytically derived necessary condition for synchrony is in excellent agreement with numerical results. Further we investigate the resilience of the synchronous states with respect to increasing network size, and we find that synchrony can be maintained up to larger network sizes by increasing either synaptic strength or rewiring frequency. Last, we find that time-varying links not only promote complete synchronization, but also have the capacity to change the local dynamics of each single neuron. Specifically, in a window of rewiring frequency and synaptic coupling strength, we observe that the spiking behavior becomes more regular.
Johansson, Björn; Holst, Philip; Henningsson, Stefan
A major question for contemporary organizations is how to support business processes with ICT. One way to do so is by the adoption of a standardized software package such as enterprise resource planning (ERP) systems. However, this demands that either the system is adjusted to existing business...... processes or that the business processes are adjusted to the system’s inherited processes, often described as an alignment problem. This paper suggests that instead of alignment, organization-technology synchronization would be better, since an organization’s business processes as well as the technology...... used is continuously evolving. The question is then how synchronization could be achieved and if any tools exists that could support this. Our suggestion is that enterprise architecture (EA) could be seen as a tool to increase organizationtechnology synchronization. We investigated a role based...
Mohammadzadeh, Ardashir; Ghaemi, Sehraneh
This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Zhu, Wei; Wang, Dandan; Liu, Lu; Feng, Gang
This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.
Xu, Lei; Jeavons, Peter
Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.
Tam, Wing-Kin; Yang, Zhi
Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.
Wang, Lei; Wombacher, Andreas; Ferreira Pires, Luis; van Sinderen, Marten J.; Chi, Chihung
Processes can synchronize their states by exchanging messages. System crashes and network failures may cause message loss, so that state changes of a process may remain unnoticed by its partner processes, resulting in state inconsistency or deadlocks. In this paper we define a method to transform a
Cao, Jinde; Alofi, Abdulaziz; Al-Mazrooei, Abdullah; Elaiw, Ahmed
This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchroniza...
Ponulak, Filip; Kasinski, Andrzej
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.
We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual
Lerner, Yulia; Bleich-Cohen, Maya; Solnik-Knirsh, Shimrit; Yogev-Seligmann, Galit; Eisenstein, Tamir; Madah, Waheed; Shamir, Alon; Hendler, Talma; Kremer, Ilana
Previous research indicates abnormal comprehension of verbal information in patients with schizophrenia. Yet the neural mechanism underlying the breakdown of verbal information processing in schizophrenia is poorly understood. Imaging studies in healthy populations have shown a network of brain areas involved in hierarchical processing of verbal information over time. Here, we identified critical aspects of this hierarchy, examining patients with schizophrenia. Using functional magnetic resonance imaging, we examined various levels of information comprehension elicited by naturally presented verbal stimuli; from a set of randomly shuffled words to an intact story. Specifically, patients with first episode schizophrenia ( N = 15), their non-manifesting siblings ( N = 14) and healthy controls ( N = 15) listened to a narrated story and randomly scrambled versions of it. To quantify the degree of dissimilarity between the groups, we adopted an inter-subject correlation (inter-SC) approach, which estimates differences in synchronization of neural responses within and between groups. The temporal topography found in healthy and siblings groups were consistent with our previous findings - high synchronization in responses from early sensory toward high order perceptual and cognitive areas. In patients with schizophrenia, stimuli with short and intermediate temporal scales evoked a typical pattern of reliable responses, whereas story condition (long temporal scale) revealed robust and widespread disruption of the inter-SCs. In addition, the more similar the neural activity of patients with schizophrenia was to the average response in the healthy group, the less severe the positive symptoms of the patients. Our findings suggest that system-level neural indication of abnormal verbal information processing in schizophrenia reflects disease manifestations.
Full Text Available Previous research indicates abnormal comprehension of verbal information in patients with schizophrenia. Yet the neural mechanism underlying the breakdown of verbal information processing in schizophrenia is poorly understood. Imaging studies in healthy populations have shown a network of brain areas involved in hierarchical processing of verbal information over time. Here, we identified critical aspects of this hierarchy, examining patients with schizophrenia. Using functional magnetic resonance imaging, we examined various levels of information comprehension elicited by naturally presented verbal stimuli; from a set of randomly shuffled words to an intact story. Specifically, patients with first episode schizophrenia (N = 15, their non-manifesting siblings (N = 14 and healthy controls (N = 15 listened to a narrated story and randomly scrambled versions of it. To quantify the degree of dissimilarity between the groups, we adopted an inter-subject correlation (inter-SC approach, which estimates differences in synchronization of neural responses within and between groups. The temporal topography found in healthy and siblings groups were consistent with our previous findings – high synchronization in responses from early sensory toward high order perceptual and cognitive areas. In patients with schizophrenia, stimuli with short and intermediate temporal scales evoked a typical pattern of reliable responses, whereas story condition (long temporal scale revealed robust and widespread disruption of the inter-SCs. In addition, the more similar the neural activity of patients with schizophrenia was to the average response in the healthy group, the less severe the positive symptoms of the patients. Our findings suggest that system-level neural indication of abnormal verbal information processing in schizophrenia reflects disease manifestations.
Veldhoen, S.; Hupkes, D.; Zuidema, W.
We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that
Full Text Available Three-motor synchronous coordination system is a MI-MO nonlinear and complex control system. And it often works in poor working condition. Advanced control strategies are required to improve the control performance of the system and to achieve the decoupling between main motor speed and tension. Cerebellar Model Articulation Controller coupled with Active Disturbance Rejection Control (CMAC-ADRC control strategy is proposed. The speed of the main motor and tensions between two motors is decoupled by extended state observer (ESO in ADRC. ESO in ADRC is used to compensate internal and external disturbances of the system online. And the anti interference of the system is improved by ESO. And the same time the control model is optimized. Feedforward control is implemented by the adoption of CMAC neural network controller. And control precision of the system is improved in reason of CMAC. The overshoot of the system can be reduced without affecting the dynamic response of the system by the use of CMAC-ADRC. The simulation results show that: the CMAC- ADRC control strategy is better than the traditional PID control strategy. And CMAC-ADRC control strategy can achieve the decoupling between speed and tension. The control system using CMAC-ADRC have strong anti-interference ability and small regulate time and small overshoot. The magnitude of the system response incited by the interference using CMAC-ADRC is smaller than the system using conventional PID control 6.43 %. And the recovery time of the system with CMAC-ADRC is shorter than the system with traditional PID control 0.18 seconds. And the triangular wave tracking error of the system with CMAC-ADRC is smaller than the system with conventional PID control 0.24 rad/min. Thus the CMAC-ADRC control strategy is a good control strategy and is able to fit three-motor synchronous coordinated control.
The report studies the question: what synchronization primitive should be used to handle inter-process communication. A formal model is presented...between these synchronization primitives. Although only four synchronization primitives are compared, the general methods can be used to compare other... synchronization primitives. Moreover, in the definitions of these synchronization primitives, conditional branches are explicitly allowed. In addition
Mahoney, John R.; Aghamohammadi, Cina; Crutchfield, James P.
A stochastic process’ statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process’ cryptic order-a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.
Song, Shuai; Song, Xiaona; Balsera, Ines Tejado
This paper investigates the mixed H∞ and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks with time delays. Our aim is to design a controller such that, though the unavoidable phenomena of time delay and external disturbances is fully considered, the resulting closed-loop system is stable with a mixed H∞ and passive performance level. By combining sliding mode control and adaptive control methods, a novel adaptive sliding mode control strategy is designed for the synchronization of time-delayed FO dynamic networks. Via the application of FO system stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequalities. Based on the conditions, a desired controller which can guarantee the stability of the closed-loop system and also ensure a mixed H∞ and passive performance level is designed. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.
Li, Chun-Hsien; Yang, Suh-Yuh
Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.
Card, H C
This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.
Liu, Ning; Mok, Charis; Witt, Emily E; Pradhan, Anjali H; Chen, Jingyuan E; Reiss, Allan L
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying social cognition. In particular, fNIRS permits simultaneous measurement of hemodynamic activity in two or more individuals interacting in a naturalistic setting. Here, we used fNIRS hyperscanning to study social cognition and communication in human dyads engaged in cooperative and obstructive interaction while they played the game of Jenga™. Novel methods were developed to identify synchronized channels for each dyad and a structural node-based spatial registration approach was utilized for inter-dyad analyses. Strong inter-brain neural synchrony (INS) was observed in the posterior region of the right middle and superior frontal gyrus, in particular Brodmann area 8 (BA8), during cooperative and obstructive interaction. This synchrony was not observed during the parallel game play condition and the dialog section, suggesting that BA8 was involved in goal-oriented social interaction such as complex interactive movements and social decision-making. INS was also observed in the dorsomedial prefrontal cortex (dmPFC), in particular Brodmann 9, during cooperative interaction only. These additional findings suggest that BA9 may be particularly engaged when theory-of-mind (ToM) is required for cooperative social interaction. The new methods described here have the potential to significantly extend fNIRS applications to social cognitive research.
Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.
Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications
Ko, Jong Hwan; Fromm, Josh; Philipose, Matthai; Tashev, Ivan; Zarar, Shuayb
While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In this paper, we study the impact of scaling the precision of neural networks on the performance of two common audio processing tasks, namely, voice-activity detection and single-channel speech enhancement. We determine the optimal pair of weight/neuron bit precision by exploring its impact on both the performance and ...
Barber, Michael J.; Clark, John W.
We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence
Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)
We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.
Y. D. Filipchik
Full Text Available The paper considers an impact of various methods for control of an exciting current pertaining to a synchronous generator on the nature of transient processes. A control algorithm for the exciting current in relation to changes in sliding and acceleration of a generator rotor has been proposed in the paper. The algorithm makes it possible to improve quality of the transient processes due to reduction of oscillation range concerning as an active power so a δ-angle as well.
Bailey, Kira; West, Robert; Mullaney, Kellie M.
Recent work has questioned whether the negativity bias is a distinct component of affective picture processing. The current study was designed to determine whether there are different neural correlates of processing positive and negative pictures using event-related brain potentials. The early posterior negativity and late positive potential were greatest in amplitude for erotic pictures. Partial Least Squares analysis revealed one latent variable that distinguished erotic pictures from neutral and positive pictures and another that differentiated negative pictures from neutral and positive pictures. The effects of orienting task on the neural correlates of processing negative and erotic pictures indicate that affective picture processing is sensitive to both stimulus-driven, and attentional or decision processes. The current data, together with other recent findings from our laboratory, lead to the suggestion that there are distinct neural correlates of processing negative and positive stimuli during affective picture processing. PMID:23029071
The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)
Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)
Bogert, Brigitte; Numminen-Kontti, Taru; Gold, Benjamin; Sams, Mikko; Numminen, Jussi; Burunat, Iballa; Lampinen, Jouko; Brattico, Elvira
Music is often used to regulate emotions and mood. Typically, music conveys and induces emotions even when one does not attend to them. Studies on the neural substrates of musical emotions have, however, only examined brain activity when subjects have focused on the emotional content of the music. Here we address with functional magnetic resonance imaging (fMRI) the neural processing of happy, sad, and fearful music with a paradigm in which 56 subjects were instructed to either classify the e...
Georgopoulos, A. P.; Tan, H.-R. M.; Lewis, S. M.; Leuthold, A. C.; Winskowski, A. M.; Lynch, J. K.; Engdahl, B.
Traumatic experiences can produce post-traumatic stress disorder (PTSD) which is a debilitating condition and for which no biomarker currently exists (Institute of Medicine (US) 2006 Posttraumatic Stress Disorder: Diagnosis and Assessment (Washington, DC: National Academies)). Here we show that the synchronous neural interactions (SNI) test which assesses the functional interactions among neural populations derived from magnetoencephalographic (MEG) recordings (Georgopoulos A P et al 2007 J. Neural Eng. 4 349-55) can successfully differentiate PTSD patients from healthy control subjects. Externally cross-validated, bootstrap-based analyses yielded >90% overall accuracy of classification. In addition, all but one of 18 patients who were not receiving medications for their disease were correctly classified. Altogether, these findings document robust differences in brain function between the PTSD and control groups that can be used for differential diagnosis and which possess the potential for assessing and monitoring disease progression and effects of therapy.
Full Text Available Recently, wireless sensor networks (WSNs have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.
Lapedes, A.; Farber, R.
The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.
Frank van der Velde
Full Text Available We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like structures. First is the Neural Blackboard Architecture (NBA, which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking, which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.
Barnden, John A.; Fields, Christopher A.
Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.
Mirsafianf, Atefeh S.; Isfahani, Shirin N.; Kasaei, Shohreh; Mobasheri, Hamid
Here we present an approach for processing neural cells images to analyze their growth process in culture environment. We have applied several image processing techniques for: 1- Environmental noise reduction, 2- Neural cells segmentation, 3- Neural cells classification based on their dendrites' growth conditions, and 4- neurons' features Extraction and measurement (e.g., like cell body area, number of dendrites, axon's length, and so on). Due to the large amount of noise in the images, we have used feed forward artificial neural networks to detect edges more precisely.
Adapa, Ram M.; Davis, Matthew H.; Stamatakis, Emmanuel A.; Absalom, Anthony R.; Menon, David K.
Sedation has a graded effect on brain responses to auditory stimuli: perceptual processing persists at sedation levels that attenuate more complex processing. We used fMRI in healthy volunteers sedated with propofol to assess changes in neural responses to spoken stimuli. Volunteers were scanned
To improve the learning process from the input data, a new learning rule was suggested. In this paper, a new method is proposed to deal with the RGB color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of ...
Full Text Available Li-Yuan Tang,1,* Hai-Jun Li,2,* Xin Huang,1 Jing Bao,1 Zubin Sethi,3 Lei Ye,1 Qing Yuan,1 Pei-Wen Zhu,1 Nan Jiang,1 Gui-Ping Gao,1 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China; 2Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China; 3The Department of Medicine, University of Miami, Coral Gables, FL, USA *These authors contributed equally to this work Objective: Previous neuroimaging studies have demonstrated that pain-related diseases are associated with brain function and anatomical abnormalities, whereas altered synchronous neural activity in acute eye pain (EP patients has not been investigated. The purpose of this study was to explore whether or not synchronous neural activity changes were measured with the regional homogeneity (ReHo method in acute EP patients.Methods: A total of 20 patients (15 males and 5 females with EP and 20 healthy controls (HCs consisting of 15 and 5 age-, sex-, and education-matched males and females, respectively, underwent resting-state functional magnetic resonance imaging. The ReHo method was applied to assess synchronous neural activity changes.Results: Compared with HCs, acute EP patients had significantly lower ReHo values in the left precentral/postcentral gyrus (Brodmann area [BA]3/4, right precentral/postcentral gyrus (BA3/4, and left middle frontal gyrus (BA6. In contrast, higher ReHo values in acute EP patients were observed in the left superior frontal gyrus (BA11, right inferior parietal lobule (BA39/40, and left precuneus (BA7. However, no relationship was found between the mean ReHo signal values of the different areas and clinical manifestations, which included both the duration and degree of pain in EP patients.Conclusion: Our study highlighted that acute EP patients showed altered synchronous neural activities in many brain regions, including somatosensory regions. These
Xia, Yonghui; Yang, Zijiang; Han, Maoan
This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.
Song Xiao-Na; Song Shuai; Liu Lei-Po; Tejado Balsera, Inés
This paper investigates the mixed H ∞ and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks. Our aim is to design a controller such that, though the unavoidable phenomena of time-delay and parameter uncertainty are fully considered, the resulting closed-loop system is asymptotically stable with a mixed H ∞ and passive performance level. By combining active and adaptive control methods, a novel hybrid control strategy is designed, which can guarantee the robust stability of the closed-loop system and also ensure a mixed H ∞ and passive performance level. Via the application of FO Lyapunov stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequality techniques. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method. (paper)
Ameling, Michael; Wolf, Bernhard; Springer, Thomas; Schill, Alexander
Business objects (BOs) are data containers for complex data structures used in business applications such as Supply Chain Management and Customer Relationship Management. Due to the replication of application logic, multiple copies of BOs are created which have to be synchronized and updated. This is a complex and time consuming task because BOs rigorously vary in their structure according to the distribution, number and size of elements. Since BOs are internally represented as XML documents, the parsing of XML is one major cost factor which has to be considered for minimizing the processing time during synchronization. The prediction of the parsing time for BOs is an significant property for the selection of an efficient synchronization mechanism. In this paper, we present a method to evaluate the influence of the structure of BOs on their parsing time. The results of our experimental evaluation incorporating four different XML parsers examine the dependencies between the distribution of elements and the parsing time. Finally, a general cost model will be validated and simplified according to the results of the experimental setup.
Lenhardt, L; Zeković, I; Dramićanin, T; Dramićanin, M D
Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%. (paper)
van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; d'Avila Garcez, Artur; Marcus, Gary F.; Miikkulainen, Risto
We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported.
It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…
Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.
We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.
Kravitz, Dwight J; Saleem, Kadharbatcha S; Baker, Chris I; Mishkin, Mortimer
The division of cortical visual processing into distinct dorsal and ventral streams is a key framework that has guided visual neuroscience. The characterization of the ventral stream as a 'What' pathway is relatively uncontroversial, but the nature of dorsal stream processing is less clear. Originally proposed as mediating spatial perception ('Where'), more recent accounts suggest it primarily serves non-conscious visually guided action ('How'). Here, we identify three pathways emerging from the dorsal stream that consist of projections to the prefrontal and premotor cortices, and a major projection to the medial temporal lobe that courses both directly and indirectly through the posterior cingulate and retrosplenial cortices. These three pathways support both conscious and non-conscious visuospatial processing, including spatial working memory, visually guided action and navigation, respectively.
Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang
A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.
Gilead, Michael; Katzir, Maayan; Eyal, Tal; Liberman, Nira
Self-conscious emotions are prevalent in our daily lives and play an important role in both normal and pathological behavior. Despite their immense significance, the neural substrates that are involved in the processing of such emotions are surprisingly under-studied. In light of this, we conducted an fMRI study in which participants thought of various personal events which elicited feelings of negative and positive self-conscious (i.e., guilt, pride) or basic (i.e., anger, joy) emotions. We performed a conjunction analysis to investigate the neural correlates associated with processing events that are related to self-conscious vs. basic emotions, irrespective of valence. The results show that processing self-conscious emotions resulted in activation within frontal areas associated with self-processing and self-control, namely, the mPFC extending to the dACC, and within the lateral-dorsal prefrontal cortex. Processing basic emotions resulted in activation throughout relatively phylogenetically-ancient regions of the cortex, namely in visual and tactile processing areas and in the insular cortex. Furthermore, self-conscious emotions differentially activated the mPFC such that the negative self-conscious emotion (guilt) was associated with a more dorsal activation, and the positive self-conscious emotion (pride) was associated with a more ventral activation. We discuss how these results shed light on the nature of mental representations and neural systems involved in self-reflective and affective processing. Copyright © 2015 Elsevier Ltd. All rights reserved.
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...
Lee, James S. J.; Nguyen, Dziem D.; Lin, C.
A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.
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 implementati...
Morey, Rajendra A.; Mitchell, Teresa V.; Inan, Seniha; Lieberman, Jeffrey A.; Belger, Aysenil
Individuals with schizophrenia demonstrate impairments in selective attention and sensory processing. The authors assessed differences in brain function between 26 participants with schizophrenia and 17 comparison subjects engaged in automatic (unattended) and controlled (attended) auditory information processing using event-related functional MRI. Lower regional neural activation during automatic auditory processing in the schizophrenia group was not confined to just the temporal lobe, but also extended to prefrontal regions. Controlled auditory processing was associated with a distributed frontotemporal and subcortical dysfunction. Differences in activation between these two modes of auditory information processing were more pronounced in the comparison group than in the patient group. PMID:19196926
Full Text Available Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”. Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot” suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of
Full Text Available The purpose of this study was to investigate approaches (techniques and technologies for the coordination of collaborative tasks using synchronous gesture manipulation. Business Process Modelling (BPM tasks are often performed in teams of modellers who need to collaborate with each other in order to coordinate and integrate their individual contributions into the various process models in a co-located environment. These collaborative BPM tasks were used as a case study in order to develop the artifact (the BPM-Touch approach as a proof of concept. The BPM-Touch approach allows for the coordination and collaboration of BPM tasks in co-located modelling teams using synchronous gesture manipulation approaches. The Design Science Research (DSR methodology was used and several cycles of developing and evaluating the artifact took place. This paper reports on the last cycle and set of evaluations. The proposed approach was implemented in a BPM software package in order to provide empirical validation. Usability evaluations of the software were undertaken with both students and BPM professionals as participants. The empirical results of the evaluations revealed that the participants found the approach to be effective and rated the usability and satisfaction of the collaboration and gesture manipulation aspects of the software positively.
Full Text Available A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN, precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.
Full Text Available The ability to encode and maintain the exact order of short sequences of stimuli or events is often crucial to our ability for effective high-order planning. However, it is not yet clear which neural mechanisms underpin this process. Several studies suggest that in comparison with item recognition temporal order coding activates prefrontal and parietal brain regions. Results of various studies tend to favour the hypothesis that the order of the stimuli is represented and encoded on several stages, from primacy and recency estimates to the exact position of the item in a sequence. Different brain regions play a different role in this process. Dorsolateral prefrontal cortex has a more general role in attention, while the premotor cortex is more involved in the process of information grouping. Parietal lobe and hippocampus also play a significant role in order processing as they enable the representation of distance. Moreover, order maintenance is associated with the existence of neural oscillators that operate at different frequencies. Electrophysiological studies revealed that theta and alpha oscillations play an important role in the maintenance of temporal order information. Those EEG oscillations are differentially associated with processes that support the maintenance of order information and item recognition. Various studies suggest a link between prefrontal areas and memory for temporal order, implying that EEG neural oscillations in the prefrontal cortex may play a role in the maintenance of information on temporal order.
Telzer, Eva H; Qu, Yang; Lin, Lynda C
Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights
Xu, Tingting; Stephane, Massoud; Parhi, Keshab K
The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the
Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.
Fu, Chi Y.
Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.
Farms Batch System (FBS) was developed as a batch process management system for off-line Run II data processing at Fermilab. FBS will manage PC farms composed of up to 250 nodes and scalable to 1000 nodes with disk capacity of up to several TB. FBS allows users to start arrays of parallel processes on multiple computers. It uses a simplified resource counting method load balancing. FBS has been successfully used for more than a year at Fermilab by fixed target experiments and will be used for collider experiment off-line data processing. Fermi Inter-Process Communication toolkit (FIPC) was designed as a supplement product for FBS that helps establish synchronization and communication between processes running in a distributed batch environment. However, FIPC is an independent package, and can be used with other batch systems, as well as in a non-batch environment. FIPC provides users with a variety of global distributed objects such as semaphores, queues and string variables. Other types of objects can be easily added to FIPC. FIPC has been running on several PC farms at Fermilab for half a year and is going to be used by CDF for off-line data processing
Baptista, M S; Moukam Kakmeni, F M; Grebogi, C
In this work we studied the combined action of chemical and electrical synapses in small networks of Hindmarsh-Rose (HR) neurons on the synchronous behavior and on the rate of information produced (per time unit) by the networks. We show that if the chemical synapse is excitatory, the larger the chemical synapse strength used the smaller the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find desynchronous behavior. Otherwise, if the chemical synapse is inhibitory, the larger the chemical synapse strength used the larger the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find synchronous behaviors. Finally, we show how to calculate semianalytically an upper bound for the rate of information produced per time unit (Kolmogorov-Sinai entropy) in larger networks. As an application, we show that this upper bound is linearly proportional to the number of neurons in a network whose neurons are highly connected.
Schmälzle, Ralf; Häcker, Frank E K; Honey, Christopher J; Hasson, Uri
Powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners. What is happening in the brains of a captivated audience? Here, we assess audience-wide functional brain dynamics during listening to speeches of varying rhetorical quality. The speeches were given by German politicians and evaluated as rhetorically powerful or weak. Listening to each of the speeches induced similar neural response time courses, as measured by inter-subject correlation analysis, in widespread brain regions involved in spoken language processing. Crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex. Thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses. Weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses. These patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances. Overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages. © The Author (2015). Published by Oxford University Press. For Permissions, please email: firstname.lastname@example.org.
Parlos, A.G.; Atiya, A.F.; Chong, K.T.
In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios
Buescher, K.L.; Baum, C.C.; Jones, R.D.
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Full Text Available Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC, the anterior insula and the posterior superior temporal sulcus (pSTS.
Monfardini, Elisabetta; Gazzola, Valeria; Boussaoud, Driss; Brovelli, Andrea; Keysers, Christian; Wicker, Bruno
Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC), the anterior insula and the posterior superior temporal sulcus (pSTS).
Anthony John Porcelli
Full Text Available People often make decisions under aversive conditions such as acute stress. Yet, less is known about the process in which acute stress can influence decision-making. A growing body of research has established that reward-related information associated with the outcomes of decisions exerts a powerful influence over the choices people make and that an extensive network of brain regions, prominently featuring the striatum, is involved in the processing of this reward-related information. Thus, an important step in research on the nature of acute stress’ influence over decision-making is to examine how it may modulate responses to rewards and punishments within reward-processing neural circuitry. In the current experiment, we employed a simple reward processing paradigm – where participants received monetary rewards and punishments – known to evoke robust striatal responses. Immediately prior to performing each of two task runs, participants were exposed to acute stress (i.e., cold pressor or a no stress control procedure in a between-subjects fashion. No stress group participants exhibited a pattern of activity within the dorsal striatum and orbitofrontal cortex consistent with past research on outcome processing – specifically, differential responses for monetary rewards over punishments. In contrast, acute stress group participants’ dorsal striatum and orbitofrontal cortex demonstrated decreased sensitivity to monetary outcomes and a lack of differential activity. These findings provide insight into how neural circuits may process rewards and punishments associated with simple decisions under acutely stressful conditions.
The multivariate construct of impulsivity is examined through neural systems and connections that comprise the executive functioning system. It is proposed that cognitive and behavioral components of impulsivity can be divided into two distinct groups, mediated by (1) the cognitive control system: deficits in top-down cognitive control processes referred to as action/cognitive impulsivity and (2) the socioemotional system: related to bottom-up affective/motivational processes referred to as affective impulsivity. Examination of impulsivity from a developmental viewpoint can guide future research, potentially enabling the selection of more effective interventions for impulsive individuals, based on the cognitive components requiring improvement.
Full Text Available Wide application of the Internet of Things (IoT system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based three-tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.
V. S. Malyar
Full Text Available Purpose. Development of a mathematical model that enables to detect resonance modes during asynchronous startup of salient-pole synchronous motors, in which capacitors are switched on to increase the electromagnetic moment in the circuit of the excitation winding. Methodology. The asynchronous mode is described by a system of differential equations of the electric equilibrium of motor circuits written in orthogonal coordinate axes. The basis of the developed algorithm is the mathematical model of the high-level adequacy motor and the projection method for solving the boundary value problem for the equations of the electric equilibrium of the circuits written in orthogonal coordinate axes, taking into account the presence of capacitors in the excitation winding. The coefficients of differential equations are the differential inductances of the motor circuits, which are determined on the basis of the calculation of its magnetic circuit. As a result of the asymmetry of the rotor windings in the asynchronous mode, the current coupling and currents change according to the periodic law. The problem of its definition is solved as a boundary one. Results. A mathematical model for studying the asynchronous characteristics of synchronous motors with capacitors in an excitation winding is developed, by means of which it is possible to investigate the influence of the size of the capacity on the motor's starting properties and the resonance processes which may arise in this case. Scientific novelty. The developed method of mathematical modeling is based on a fundamentally new mathematical basis for the calculation of stationary dynamic modes of nonlinear electromagnetic circuits, which enables to obtain periodic coordinate dependencies, without resorting to the calculation of the transients. The basis of the developed algorithm is based on the approximation of state variables by cubic splines, the projection method of decomposition for the boundary value
El Laithy, Karim; Bogdan, Martin
Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.
Flachot, Alban; Gegenfurtner, Karl R
Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.
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.
Song, Xiao-Na; Song, Shuai; Tejado Balsera, Inés; Liu, Lei-Po
This paper investigates the mixed H ∞ and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks. Our aim is to design a controller such that, though the unavoidable phenomena of time-delay and parameter uncertainty are fully considered, the resulting closed-loop system is asymptotically stable with a mixed H ∞ and passive performance level. By combining active and adaptive control methods, a novel hybrid control strategy is designed, which can guarantee the robust stability of the closed-loop system and also ensure a mixed H ∞ and passive performance level. Via the application of FO Lyapunov stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequality techniques. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method. Supported by National Natural Science Foundation of China under Grant Nos. U1604146, U1404610, 61473115, 61203047, Science and Technology Research Project in Henan Province under Grant Nos. 152102210273, 162102410024, and Foundation for the University Technological Innovative Talents of Henan Province under Grant No. 18HASTIT019
Howell, J.A.; Whiteson, R.
Process control consists of two basic elements: a model of the process and knowledge of the desired control algorithm. In some cases the level of the control algorithm is merely supervisory, as in an alarm-reporting or anomaly-detection system. If the model of the process is known, then a set of equations may often be solved explicitly to provide the control algorithm. Otherwise, the model has to be discovered through empirical studies. Neural networks have properties that make them useful in this application. They can learn (make internal models from experience or observations). The problem of anomaly detection in materials control systems fits well into this general control framework. To successfully model a process with a neutral network, a good set of observables must be chosen. These observables must in some sense adequately span the space of representable events, so that a signature metric can be built for normal operation. In this way, a non-normal event, one that does not fit within the signature, can be detected. In this paper, we discuss the issues involved in applying a neural network model to anomaly detection in materials control systems. These issues include data selection and representation, network architecture, prediction of events, the use of simulated data, and software tools. 10 refs., 4 figs., 1 tab
Shafir, Roni; Thiruchselvam, Ravi; Suri, Gaurav; Gross, James J; Sheppes, Gal
Emotional-intensity is a core characteristic of affective events that strongly determines how individuals choose to regulate their emotions. Our conceptual framework suggests that in high emotional-intensity situations, individuals prefer to disengage attention using distraction, which can more effectively block highly potent emotional information, as compared with engagement reappraisal, which is preferred in low emotional-intensity. However, existing supporting evidence remains indirect because prior intensity categorization of emotional stimuli was based on subjective measures that are potentially biased and only represent the endpoint of emotional-intensity processing. Accordingly, this study provides the first direct evidence for the role of online emotional-intensity processing in predicting behavioral regulatory-choices. Utilizing the high temporal resolution of event-related potentials, we evaluated online neural processing of stimuli's emotional-intensity (late positive potential, LPP) prior to regulatory-choices between distraction and reappraisal. Results showed that enhanced neural processing of intensity (enhanced LPP amplitudes) uniquely predicted (above subjective measures of intensity) increased tendency to subsequently choose distraction over reappraisal. Additionally, regulatory-choices led to adaptive consequences, demonstrated in finding that actual implementation of distraction relative to reappraisal-choice resulted in stronger attenuation of LPPs and self-reported arousal. © The Author (2016). Published by Oxford University Press. For Permissions, please email: email@example.com.
Giebel, S; Rainer, M
Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.
Alice Mado eProverbio
Full Text Available Some behavioral and neuroimaging studies suggest that adults prefer to view attractive faces of the opposite sex more than attractive faces of the same sex. However, unlike the other-race face effect (ORE; Caldara et al., 2004, little is known regarding the existence of an opposite-/same-sex bias in face processing. In this study, the faces of 130 attractive male and female adults were foveally presented to 40 heterosexual university students (20 men and 20 women who were engaged in a secondary perceptual task (landscape detection. The automatic processing of face gender was investigated by recording ERPs from 128 scalp sites. Neural markers of opposite- vs. same-sex bias in face processing included larger and earlier centro-parietal N400s in response to faces of the opposite sex and a larger late positivity (LP to same-sex faces. Analysis of intra-cortical neural generators (swLORETA showed that facial processing-related (FG, BA37, BA20/21 and emotion-related brain areas (the right parahippocampal gyrus, BA35; uncus, BA36/38; and the cingulate gyrus, BA24 had higher activations in response to opposite- than same-sex faces. The results of this analysis, along with data obtained from ERP recordings, support the hypothesis that both genders process opposite-sex faces differently than same-sex faces. The data also suggest a hemispheric asymmetry in the processing of opposite-/same-sex faces, with the right hemisphere involved in processing same-sex faces and the left hemisphere involved in processing faces of the opposite sex. The data support previous literature suggesting a right lateralization for the representation of self-image and body awareness.
Proverbio, Alice Mado; Riva, Federica; Martin, Eleonora; Zani, Alberto
Some behavioral and neuroimaging studies suggest that adults prefer to view attractive faces of the opposite sex more than attractive faces of the same sex. However, unlike the other-race face effect (Caldara et al., 2004), little is known regarding the existence of an opposite-/same-sex bias in face processing. In this study, the faces of 130 attractive male and female adults were foveally presented to 40 heterosexual university students (20 men and 20 women) who were engaged in a secondary perceptual task (landscape detection). The automatic processing of face gender was investigated by recording ERPs from 128 scalp sites. Neural markers of opposite- vs. same-sex bias in face processing included larger and earlier centro-parietal N400s in response to faces of the opposite sex and a larger late positivity (LP) to same-sex faces. Analysis of intra-cortical neural generators (swLORETA) showed that facial processing-related (FG, BA37, BA20/21) and emotion-related brain areas (the right parahippocampal gyrus, BA35; uncus, BA36/38; and the cingulate gyrus, BA24) had higher activations in response to opposite- than same-sex faces. The results of this analysis, along with data obtained from ERP recordings, support the hypothesis that both genders process opposite-sex faces differently than same-sex faces. The data also suggest a hemispheric asymmetry in the processing of opposite-/same-sex faces, with the right hemisphere involved in processing same-sex faces and the left hemisphere involved in processing faces of the opposite sex. The data support previous literature suggesting a right lateralization for the representation of self-image and body awareness.
Hutchinson, C. E.; Chon, Y.-T.
The effect of NPFM (Neural Pulse Frequency Modulation) on a stationary Gaussian input, namely an exponentially correlated Gaussian input, is investigated with special emphasis on the determination of the average number of pulses in unit time, known also as the average frequency of pulse occurrence. For some classes of stationary input processes where the formulation of the appropriate multidimensional Markov diffusion model of the input-plus-NPFM system is possible, the average impulse frequency may be obtained by a generalization of the approach adopted. The results are approximate and numerical, but are in close agreement with Monte Carlo computer simulation results.
Wang, Yuping; Chen, Nian-Shing; Levy, Mike
This article discusses the learning process undertaken by language teachers in a cyber face-to-face teacher training program. Eight tertiary Chinese language teachers attended a 12-week training program conducted in an online synchronous learning environment characterized by multimedia-based, oral and visual interaction. The term "cyber…
All natural processes are accompanied by fluctuations, characterized as noise or chaos. Biological systems, which have evolved during billions of years, are likely to have adapted, not only to cope with such fluctuations, but also to make use of them. We investigate how the complex dynamics of the brain, including oscillations, chaos and noise, can affect the efficiency of neural information processing. In particular, we consider the amplification and functional role of internal fluctuations. Using computer simulations of a neural network model of the olfactory cortex and hippocampus, we demonstrate how microscopic fluctuations can result in global effects at the network level. We show that the rate of information processing in associative memory tasks can be maximized for optimal noise levels, analogous to stochastic resonance phenomena. Noise can also induce transitions between different dynamical states, which could be of significance for learning and memory. A chaotic-like behavior, induced by noise or by an increase in neuronal excitability, can enhance system performance if it is transient and converges to a limit cycle memory state. We speculate whether this dynamical behavior perhaps could be related to (creative) thinking.
Maksimenko, Vladimir A.; Runnova, Anastasia E.; Frolov, Nikita S.; Makarov, Vladimir V.; Nedaivozov, Vladimir; Koronovskii, Alexey A.; Pisarchik, Alexander; Hramov, Alexander E.
Stimulus-related brain activity is considered using wavelet-based analysis of neural interactions between occipital and parietal brain areas in alpha (8-12 Hz) and beta (15-30 Hz) frequency bands. We show that human sensory processing related to the visual stimuli perception induces brain response resulted in different ways of parieto-occipital interactions in these bands. In the alpha frequency band the parieto-occipital neuronal network is characterized by homogeneous increase of the interaction between all interconnected areas both within occipital and parietal lobes and between them. In the beta frequency band the occipital lobe starts to play a leading role in the dynamics of the occipital-parietal network: The perception of visual stimuli excites the visual center in the occipital area and then, due to the increase of parieto-occipital interactions, such excitation is transferred to the parietal area, where the attentional center takes place. In the case when stimuli are characterized by a high degree of ambiguity, we find greater increase of the interaction between interconnected areas in the parietal lobe due to the increase of human attention. Based on revealed mechanisms, we describe the complex response of the parieto-occipital brain neuronal network during the perception and primary processing of the visual stimuli. The results can serve as an essential complement to the existing theory of neural aspects of visual stimuli processing.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Zhang, Bitao; Pi, YouGuo
The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.
Oliver, Jason A; Evans, David E; Addicott, Merideth A; Potts, Geoffrey F; Brandon, Thomas H; Drobes, David J
Nicotine withdrawal reduces neurobiological responses to nonsmoking rewards. Insight into these reward deficits could inform the development of targeted interventions. This study examined the effect of withdrawal on neural and behavioral responses during a reward prediction task. Smokers (N = 48) attended two laboratory sessions following overnight abstinence. Withdrawal was manipulated by having participants smoke three regular nicotine (0.6 mg yield; satiation) or very low nicotine (0.05 mg yield; withdrawal) cigarettes. Electrophysiological recordings of neural activity were obtained while participants completed a reward prediction task that involved viewing four combinations of predictive and reward-determining stimuli: (1) Unexpected Reward; (2) Predicted Reward; (3) Predicted Punishment; (4) Unexpected Punishment. The task evokes a medial frontal negativity that mimics the phasic pattern of dopaminergic firing in ventral tegmental regions associated with reward prediction errors. Nicotine withdrawal decreased the amplitude of the medial frontal negativity equally across all trial types (p nicotine dependence (p Nicotine withdrawal had equivocal impact across trial types, suggesting reward processing deficits are unlikely to stem from changes in phasic dopaminergic activity during prediction errors. Effects on tonic activity may be more pronounced. Pharmacological interventions directly targeting the dopamine system and behavioral interventions designed to increase reward motivation and responsiveness (eg, behavioral activation) may aid in mitigating withdrawal symptoms and potentially improving smoking cessation outcomes. Findings from this study indicate nicotine withdrawal impacts reward processing signals that are observable in smokers' neural activity. This may play a role in the subjective aversive experience of nicotine withdrawal and potentially contribute to smoking relapse. Interventions that address abnormal responding to both pleasant and
Kriener, Birgit; Anand, Lishma; Timme, Marc
Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits. (paper)
Processing of meaning is critical for language perception, and therefore the majority of research on meaning processing has focused on the semantic, lexical, conceptual, and propositional processing of language. However, music is another a means of communication, and meaning also emerges from the interpretation of musical information. This article provides a framework for the investigation of the processing of musical meaning, and reviews neuroscience studies investigating this issue. These studies reveal two neural correlates of meaning processing, the N400 and the N5 (which are both components of the event-related electric brain potential). Here I argue that the N400 can be elicited by musical stimuli due to the processing of extra-musical meaning, whereas the N5 can be elicited due to the processing of intra-musical meaning. Notably, whereas the N400 can be elicited by both linguistic and musical stimuli, the N5 has so far only been observed for the processing of meaning in music. Thus, knowledge about both the N400 and the N5 can advance our understanding of how the human brain processes meaning information.
Lister, J.B.; Schnurrenberger, H.; Staeheli, N.; Stockhammer, N.; Duperrex, P.A.; Moret, J.M.
Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper the authors illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. The authors also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. The authors outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. The authors also present some of the difficulties encountered in applying these networks
Lister, J.B.; Schnurrenberger, H.; Staeheli, N.; Stockhammer, N.; Duperrex, P.A.; Moret, J.M.
Research into neural networks has gained a large following in recent years. In spite of the long term timescale of this Artificial Intelligence research, the tools which the community is developing can already find useful applications to real practical problems in experimental research. One of the main advantages of the parallel algorithms being developed in AI is the structural simplicity of the required hardware implementation, and the simple nature of the calculations involved. This makes these techniques ideal for problems in which both speed and data volume reduction are important, the case for most front-end processing tasks. In this paper we illustrate the use of a particular neural network known as the Multi-Layer Perceptron as a method for solving several different tasks, all drawn from the field of Tokamak research. We also briefly discuss the use of the Multi-Layer Perceptron as a non-linear controller in a feedback loop. We outline the type of problem which can be usefully addressed by these techniques, even before the large-scale parallel processing hardware currently under development becomes cheaply available. We also present some of the difficulties encountered in applying these networks. (author) 13 figs., 9 refs
Hwang, Kai; Panda, Dhabaleswar K.; Haddadi, Navid
This paper presents the architectural features and imaging applications of the Orthogonal MultiProcessor (OMP) system, which is under construction at the University of Southern California with research funding from NSF and assistance from several industrial partners. The prototype OMP is being built with 16 Intel i860 RISC microprocessors and 256 parallel memory modules using custom-designed spanning buses, which are 2-D interleaved and orthogonally accessed without conflicts. The 16-processor OMP prototype is targeted to achieve 430 MIPS and 600 Mflops, which have been verified by simulation experiments based on the design parameters used. The prototype OMP machine will be initially applied for image processing, computer vision, and neural network simulation applications. We summarize important vision and imaging algorithms that can be restructured with neural network models. These algorithms can efficiently run on the OMP hardware with linear speedup. The ultimate goal is to develop a high-performance Visual Computer (Viscom) for integrated low- and high-level image processing and vision tasks.
Chey, J; Grossberg, S; Mingolla, E
A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-turned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1-->MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.
Liu, Tongran; Xiao, Tong; Li, Xiaoyan
The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolescent...... males were enrolled: a high IQ group and an average IQ group. Age and parental socioeconomic status were matched between the two groups. Participants counted the numbers of the central cross changes while paired facial expressions were presented bilaterally in an oddball paradigm. There were two.......2). Participants were required to concentrate on the primary task of counting the central cross changes and to ignore the expressions to ensure that facial expression processing was automatic. Event-related potentials (ERPs) were obtained during the tasks. The visual mismatch negativity (vMMN) components were...
Full Text Available This paper aims to describe the implementa-tion of a neural network and a genetic algorithm system in order to forecast certain economic indicators of a free market economy. In a free market economy forecasting process precedes the economic planning (a management function, providing important information for the result of the last process. Forecasting represents a starting point in setting of target for a firm, an organization or even a branch of the economy. Thus, the forecasting method used can influence in a significant mode the evolution of an entity. In the following we will describe the forecasting of an economic indicator using two intelligent systems. The difference between the results obtained by this two systems are described in chapter IV.
Sivakumar, Siddharth S.; Namath, Amalia G.; Galán, Roberto F.
Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale ...
In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)
In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)
Antje B M Gerdes
Full Text Available In our natural environment, emotional information is conveyed by converging visual and auditory information; multimodal integration is of utmost importance. In the laboratory, however, emotion researchers have mostly focused on the examination of unimodal stimuli. Few existing studies on multimodal emotion processing have focused on human communication such as the integration of facial and vocal expressions. Extending the concept of multimodality, the current study examines how the neural processing of emotional pictures is influenced by simultaneously presented sounds. Twenty pleasant, unpleasant, and neutral pictures of complex scenes were presented to 22 healthy participants. On the critical trials these pictures were paired with pleasant, unpleasant and neutral sounds. Sound presentation started 500 ms before picture onset and each stimulus presentation lasted for 2s. EEG was recorded from 64 channels and ERP analyses focused on the picture onset. In addition, valence, and arousal ratings were obtained. Previous findings for the neural processing of emotional pictures were replicated. Specifically, unpleasant compared to neutral pictures were associated with an increased parietal P200 and a more pronounced centroparietal late positive potential (LPP, independent of the accompanying sound valence. For audiovisual stimulation, increased parietal P100 and P200 were found in response to all pictures which were accompanied by unpleasant or pleasant sounds compared to pictures with neutral sounds. Most importantly, incongruent audiovisual pairs of unpleasant pictures and pleasant sounds enhanced parietal P100 and P200 compared to pairings with congruent sounds. Taken together, the present findings indicate that emotional sounds modulate early stages of visual processing and, therefore, provide an avenue by which multimodal experience may enhance perception.
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
Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.
The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.
Ma, Yina; Qin, Jungang; Han, Shihui
Behavioral studies suggest that humans evolve the capacity to cope with anxiety induced by the awareness of death’s inevitability. However, the neurocognitive processes that underlie online death-related thoughts remain unclear. Our recent functional MRI study found that the processing of linguistic cues related to death was characterized by decreased neural activity in human insular cortex. The current study further investigated the time course of neural processing of death-related linguistic cues. We recorded event-related potentials (ERP) to death-related, life-related, negative-valence, and neutral-valence words in a modified Stroop task that required color naming of words. We found that the amplitude of an early frontal/central negativity at 84–120 ms (N1) decreased to death-related words but increased to life-related words relative to neutral-valence words. The N1 effect associated with death-related and life-related words was correlated respectively with individuals’ pessimistic and optimistic attitudes toward life. Death-related words also increased the amplitude of a frontal/central positivity at 124–300 ms (P2) and of a frontal/central positivity at 300–500 ms (P3). However, the P2 and P3 modulations were observed for both death-related and negative-valence words but not for life-related words. The ERP results suggest an early inverse coding of linguistic cues related to life and death, which is followed by negative emotional responses to death-related information. PMID:23840787
Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B; Siegrist, Michael
The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences. We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy. Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.
Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern
and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....
Netto, Antonio V.; Ferreira de Oliveira, Maria C.
In this work we describe the framework of a functional system for processing and analyzing images of the human eye acquired by the Hartmann-Shack technique (HS), in order to extract information to formulate a diagnosis of eye refractive errors (astigmatism, hypermetropia and myopia). The analysis is to be carried out using an Artificial Intelligence system based on Neural Nets, Fuzzy Logic and Classifier Combination. The major goal is to establish the basis of a new technology to effectively measure ocular refractive errors that is based on methods alternative those adopted in current patented systems. Moreover, analysis of images acquired with the Hartmann-Shack technique may enable the extraction of additional information on the health of an eye under exam from the same image used to detect refraction errors.
Jacob, P J; Gu, F; Ball, A D [School of Engineering, University of Manchester, Manchester (United Kingdom)
The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.
Jacob, P.J.; Gu, F.; Ball, A.D. [School of Engineering, University of Manchester, Manchester (United Kingdom)
The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.
Orbán, Levente L; Chartier, Sylvain
Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.
McGettigan, Carolyn; Warren, Jane E; Eisner, Frank; Marshall, Chloe R; Shanmugalingam, Pradheep; Scott, Sophie K
This study investigated links between working memory and speech processing systems. We used delayed pseudoword repetition in fMRI to investigate the neural correlates of sublexical structure in phonological working memory (pWM). We orthogonally varied the number of syllables and consonant clusters in auditory pseudowords and measured the neural responses to these manipulations under conditions of covert rehearsal (Experiment 1). A left-dominant network of temporal and motor cortex showed increased activity for longer items, with motor cortex only showing greater activity concomitant with adding consonant clusters. An individual-differences analysis revealed a significant positive relationship between activity in the angular gyrus and the hippocampus, and accuracy on pseudoword repetition. As models of pWM stipulate that its neural correlates should be activated during both perception and production/rehearsal [Buchsbaum, B. R., & D'Esposito, M. The search for the phonological store: From loop to convolution. Journal of Cognitive Neuroscience, 20, 762-778, 2008; Jacquemot, C., & Scott, S. K. What is the relationship between phonological short-term memory and speech processing? Trends in Cognitive Sciences, 10, 480-486, 2006; Baddeley, A. D., & Hitch, G. Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47-89). New York: Academic Press, 1974], we further assessed the effects of the two factors in a separate passive listening experiment (Experiment 2). In this experiment, the effect of the number of syllables was concentrated in posterior-medial regions of the supratemporal plane bilaterally, although there was no evidence of a significant response to added clusters. Taken together, the results identify the planum temporale as a key region in pWM; within this region, representations are likely to take the form of auditory or audiomotor "templates" or "chunks" at the level of the syllable
Pecora, Louis M.; Carroll, Thomas L.
We review some of the history and early work in the area of synchronization in chaotic systems. We start with our own discovery of the phenomenon, but go on to establish the historical timeline of this topic back to the earliest known paper. The topic of synchronization of chaotic systems has always been intriguing, since chaotic systems are known to resist synchronization because of their positive Lyapunov exponents. The convergence of the two systems to identical trajectories is a surprise. We show how people originally thought about this process and how the concept of synchronization changed over the years to a more geometric view using synchronization manifolds. We also show that building synchronizing systems leads naturally to engineering more complex systems whose constituents are chaotic, but which can be tuned to output various chaotic signals. We finally end up at a topic that is still in very active exploration today and that is synchronization of dynamical systems in networks of oscillators
Rebecca J Lepping
Full Text Available Anterior cingulate cortex (ACC and striatum are part of the emotional neural circuitry implicated in major depressive disorder (MDD. Music is often used for emotion regulation, and pleasurable music listening activates the dopaminergic system in the brain, including the ACC. The present study uses functional MRI (fMRI and an emotional nonmusical and musical stimuli paradigm to examine how neural processing of emotionally provocative auditory stimuli is altered within the ACC and striatum in depression.Nineteen MDD and 20 never-depressed (ND control participants listened to standardized positive and negative emotional musical and nonmusical stimuli during fMRI scanning and gave subjective ratings of valence and arousal following scanning.ND participants exhibited greater activation to positive versus negative stimuli in ventral ACC. When compared with ND participants, MDD participants showed a different pattern of activation in ACC. In the rostral part of the ACC, ND participants showed greater activation for positive information, while MDD participants showed greater activation to negative information. In dorsal ACC, the pattern of activation distinguished between the types of stimuli, with ND participants showing greater activation to music compared to nonmusical stimuli, while MDD participants showed greater activation to nonmusical stimuli, with the greatest response to negative nonmusical stimuli. No group differences were found in striatum.These results suggest that people with depression may process emotional auditory stimuli differently based on both the type of stimulation and the emotional content of that stimulation. This raises the possibility that music may be useful in retraining ACC function, potentially leading to more effective and targeted treatments.
Lee, Yune-Sang; Min, Nam Eun; Wingfield, Arthur; Grossman, Murray; Peelle, Jonathan E
The information contained in a sensory signal plays a critical role in determining what neural processes are engaged. Here we used interleaved silent steady-state (ISSS) functional magnetic resonance imaging (fMRI) to explore how human listeners cope with different degrees of acoustic richness during auditory sentence comprehension. Twenty-six healthy young adults underwent scanning while hearing sentences that varied in acoustic richness (high vs. low spectral detail) and syntactic complexity (subject-relative vs. object-relative center-embedded clause structures). We manipulated acoustic richness by presenting the stimuli as unprocessed full-spectrum speech, or noise-vocoded with 24 channels. Importantly, although the vocoded sentences were spectrally impoverished, all sentences were highly intelligible. These manipulations allowed us to test how intelligible speech processing was affected by orthogonal linguistic and acoustic demands. Acoustically rich speech showed stronger activation than acoustically less-detailed speech in a bilateral temporoparietal network with more pronounced activity in the right hemisphere. By contrast, listening to sentences with greater syntactic complexity resulted in increased activation of a left-lateralized network including left posterior lateral temporal cortex, left inferior frontal gyrus, and left dorsolateral prefrontal cortex. Significant interactions between acoustic richness and syntactic complexity occurred in left supramarginal gyrus, right superior temporal gyrus, and right inferior frontal gyrus, indicating that the regions recruited for syntactic challenge differed as a function of acoustic properties of the speech. Our findings suggest that the neural systems involved in speech perception are finely tuned to the type of information available, and that reducing the richness of the acoustic signal dramatically alters the brain's response to spoken language, even when intelligibility is high. Copyright © 2015 Elsevier
Wakefield, Elizabeth M; James, Thomas W; James, Karin H
Co-speech gesture facilitates learning to a greater degree in children than in adults, suggesting that the mechanisms underlying the processing of co-speech gesture differ as a function of development. We suggest that this may be partially due to children's lack of experience producing gesture, leading to differences in the recruitment of sensorimotor networks when comparing adults to children. Here, we investigated the neural substrates of gesture processing in a cross-sectional sample of 5-, 7.5-, and 10-year-old children and adults and focused on relative recruitment of a sensorimotor system that included the precentral gyrus (PCG) and the posterior middle temporal gyrus (pMTG). Children and adults were presented with videos in which communication occurred through different combinations of speech and gesture during a functional magnetic resonance imaging (fMRI) session. Results demonstrated that the PCG and pMTG were recruited to different extents in the two populations. We interpret these novel findings as supporting the idea that gesture perception (pMTG) is affected by a history of gesture production (PCG), revealing the importance of considering gesture processing as a sensorimotor process.
Saini, K. K.; Saini, Sanju
Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.
Moore, R.; Goraj, M.J.; McGhee, J. [RuggedCom Inc., Concord, ON (Canada)
This paper discussed a time synchronization and dynamic multicast filtering procedure conducted on an IEC 61850 process bus. The Institute of Electrical and Electronic (IEEE) 1588 time synchronization and dynamic multicast filtering procedure was conducted at a substation equipped with non-conventional instrument transformers (NCIT) and intelligent circuit breakers. The process bus interconnected IEDs within a bay that included a real time sampled value (SV) measurement system. The system was designed to reduce the use of copper wiring and to eliminate high energy signal processes. Digitized sampled measured values were sent from the electronic instrument transformers to protect and control relays. A merging unit was used to enable the transmission of the digitized current and voltage measurements across an ethernet network. Two sampling rates were supplied for power system monitoring and protection applications. The merging units continuously sent sampling values of current and voltages acquired from primary equipment. Precision time protocol systems were discussed, and issues related to time synchronization were reviewed. A network topology was provided. 4 refs., 4 figs.
This article presents the current status of the use of Artificial Neural Networks (ANNs) in process engineering applications where common mathematical methods do not completely represent the behavior shown by experimental observations, results, and plant operating data. Three examples of the use of ANNs in typical process engineering applications such as prediction of activity in solvent-polymer binary systems, prediction of a surfactant self-diffusion coefficient of micellar systems, and process control and simulation are shown. These examples are important for polymerization applications, enhanced-oil recovery, and automatic process control
Santovena-Casal, Sonia Ma
When integrating a new online tool in university educational system, it is necessary to know its features, applications and functions in depth, advantages and disadvantages, and the results obtained when it has been used by other educational institutions. Synchronous communication tool, "Elluminate Live" can be integrated into a virtual platform…
González Martínez, Jose María; Ferrer Riquelme, Alberto José; Westerhuis, Johan A.
This paper addresses the real-time monitoring of batch processes with multiple different local time trajectories of variables measured during the process run. For Unfold Principal Component Analysis (U-PCA)—or Unfold Partial Least Squares (U-PLS)-based on-line monitoring of batch processes, batch runs need to be synchronized, not only to have the same time length, but also such that key events happen at the same time. An adaptation from Kassidas et al.'s approach  will be introduced to ach...
Sadeh, Naomi; Spielberg, Jeffrey M; Warren, Stacie L; Miller, Gregory A; Heller, Wendy
Given the complexity of the brain, characterizing relations among distributed brain regions is likely essential to describing the neural instantiation of posttraumatic stress symptoms. This study examined patterns of functional connectivity among key brain regions implicated in the pathophysiology of posttraumatic stress disorder (PTSD) in 35 trauma-exposed adults using an emotion-word Stroop task. PTSD symptom severity (particularly hyperarousal symptoms) moderated amygdala-mPFC coupling during the processing of unpleasant words, and this moderation correlated positively with reported real-world impairment and amygdala reactivity. Reexperiencing severity moderated hippocampus-insula coupling during pleasant and unpleasant words. Results provide evidence that PTSD symptoms differentially moderate functional coupling during emotional interference and underscore the importance of examining network connectivity in research on PTSD. They suggest that hyperarousal is associated with negative mPFC-amygdala coupling and that reexperiencing is associated with altered insula-hippocampus function, patterns of connectivity that may represent separable indicators of dysfunctional inhibitory control during affective processing.
Manjunath Patel Gowdru Chandrashekarappa
Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.
Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina
Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that 2 years of group music classes in high school enhance the neural encoding of speech. To tease apart the relationships between music and neural function, we tested high school students participating in either music or fitness-based training. These groups were matched at the onset of training on neural timing, reading ability, and IQ. Auditory brainstem responses were collected to a synthesized speech sound presented in background noise. After 2 years of training, the neural responses of the music training group were earlier than at pre-training, while the neural timing of students in the fitness training group was unchanged. These results represent the strongest evidence to date that in-school music education can cause enhanced speech encoding. The neural benefits of musical training are, therefore, not limited to expensive private instruction early in childhood but can be elicited by cost-effective group instruction during adolescence.
Full Text Available Previous neuroimaging studies on decision making have mainly focused on decisions on behalf of oneself. Considering that people often make decisions on behalf of others, it is intriguing that there is little neurobiological evidence on how decisions for others differ from those for self. Thus, the present study focused on the direct comparison between risky decisions for self and those for other using functional magnetic resonance imaging (fMRI. Participants (N = 23 were asked to perform a gambling task for themselves (decision-for-self condition or for another person (decision-for-other condition while in the scanner. Their task was to choose between a low-risk option (i.e., win or lose 10 points and a high-risk option (i.e., win or lose 90 points. The winning probabilities of each option varied from 17% to 83%. Compared to choices for others, choices for self were more risk-averse at lower winning probability and more risk-seeking at higher winning probability, perhaps due to stronger affective process during risky decision for self compared to other. The brain activation pattern changed according to the target of the decision, such that reward-related regions were more active in the decision-for-self condition than in the decision-for-other condition, whereas brain regions related to the theory of mind (ToM showed greater activation in the decision-for-other condition than in the decision-for-self condition. A parametric modulation analysis reflecting each individual’s decision model revealed that activation of the amygdala and the dorsomedial prefrontal cortex (DMPFC were associated with value computation for self and for other, respectively, during a risky financial decision. The present study suggests that decisions for self and other may recruit fundamentally distinctive neural processes, which can be mainly characterized by dominant affective/impulsive and cognitive/regulatory processes, respectively.
Jung, Daehyun; Sul, Sunhae; Kim, Hackjin
Previous neuroimaging studies on decision making have mainly focused on decisions on behalf of oneself. Considering that people often make decisions on behalf of others, it is intriguing that there is little neurobiological evidence on how decisions for others differ from those for oneself. The present study directly compared risky decisions for self with those for another person using functional magnetic resonance imaging (fMRI). Participants were asked to perform a gambling task on behalf of themselves (decision-for-self condition) or another person (decision-for-other condition) while in the scanner. Their task was to choose between a low-risk option (i.e., win or lose 10 points) and a high-risk option (i.e., win or lose 90 points) with variable levels of winning probability. Compared with choices regarding others, those regarding oneself were more risk-averse at lower winning probabilities and more risk-seeking at higher winning probabilities, perhaps due to stronger affective process during risky decisions for oneself compared with those for other. The brain-activation pattern changed according to the target, such that reward-related regions were more active in the decision-for-self condition than in the decision-for-other condition, whereas brain regions related to the theory of mind (ToM) showed greater activation in the decision-for-other condition than in the decision-for-self condition. Parametric modulation analysis using individual decision models revealed that activation of the amygdala and the dorsomedial prefrontal cortex (DMPFC) were associated with value computations for oneself and for another, respectively, during risky financial decisions. The results of the present study suggest that decisions for oneself and for other may recruit fundamentally distinct neural processes, which can be mainly characterized as dominant affective/impulsive and cognitive/regulatory processes, respectively. PMID:23519016
Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R
Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under
Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.
ALADDIN is a prototype system which combines fuzzy clustering techniques and artificial neural network (ANN) models in a novel approach to the problem of classifying events in dynamic processes. The main motivation for the development of such a system derived originally from the problem of finding new principled methods to perform alarm structuring/suppression in a nuclear power plant (NPP) alarm system. One such method consists in basing the alarm structuring/suppression on a fast recognition of the event generating the alarms, so that a subset of alarms sufficient to efficiently handle the current fault can be selected to be presented to the operator, minimizing in this way the operator's workload in a potentially stressful situation. The scope of application of a system like ALADDIN goes however beyond alarm handling, to include diagnostic tasks in general. The eventual application of the system to domains other than NPPs was also taken into special consideration during the design phase. In this document we report on the first phase of the ALADDIN project which consisted mainly in a comparative study of a series of ANN-based approaches to event classification, and on the proposal of a first system prototype which is to undergo further tests and, eventually, be integrated in existing alarm, diagnosis, and accident management systems such as CASH, IDS, and CAMS. (author)
Levente L Orbán
Full Text Available Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.
Full Text Available In this article a new neural network based method for automatic classification of ground penetrating radar (GPR traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.
Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.
In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.
Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina
Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that two years of group music classes in high school enhance the subcortical encoding of speech. To tease apart the relationships between music and neural...
Conductive nanogel-interfaced neural microelectrode arrays with electrically controlled in-situ delivery of manganese ions enabling high-resolution MEMRI for synchronous neural tracing with deep brain stimulation.
Huang, Wei-Chen; Lo, Yu-Chih; Chu, Chao-Yi; Lai, Hsin-Yi; Chen, You-Yin; Chen, San-Yuan
Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl 2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn 2+ . In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn 2+ ions, so that the system can release Mn 2+ ions rather than MnCl 2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high-resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based microelectrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional
Upadhyay, Ranjit Kumar; Mondal, Argha
Highlights: • To examine synchronization, noisy chemical and electrical coupling have been considered for a coupled bursting M-L neurons. • Bursting presents the precursor to spike synchronization and coupling strength increases the locking between neurons (anti phase and in phase). • The stability of synchronization is established via similarity function. • The necessary condition to occur CS state is observed using master stability function. • A network of four M-L neurons is considered to observe the synchronization. - Abstract: Bursting of neuronal firing is an interesting dynamical consequences depending on fast/slow dynamics. Certain cells in different brain regions produce spike-burst activity. We study such firing activity and its transitions to synchronization using identical as well as non-identical coupled bursting Morris-Lecar (M-L) neurons. Synchronization of different firing activity is a multi-time-scale phenomenon and burst synchronization presents the precursor to spike synchronization. Chemical synapses are one of the dynamical means of information processing between neurons. Electrical synapses play a major role for synchronous activity in a certain network of neurons. Synaptically coupled neural cells exhibit different types of synchronization such as in phase or anti-phase depending on the nature and strength of coupling functions and the synchronization regimes are analyzed by similarity functions. The sequential transitions to synchronization regime are examined by the maximum transverse Lyapunov exponents. Synchronization of voltage traces of two types of planar bursting mechanisms is explored for both kind of synapses under realistic conditions. The noisy influence effects on the transmission of signals and strongly acts to the firing activity (such as periodic firing and bursting) and integration of signals for a network. It has been examined using the mean interspike interval analysis. The transition to synchronization states of
van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.; Blockeel, Hendrik; Denecker, Marc
Monolithic neural networks may be trained from measured data to establish knowledge about the process. Unfortunately, this knowledge is not guaranteed to be found and – if at all – hard to extract. Modular neural networks are better suited for this purpose. Domain-ordered by topology, rule
Zhou Tao; Zhao Ming; Zhou Changsong
The study of network synchronization has attracted increasing attentionrecently. In this paper, we strictly define a class of networks, namely effective networks, which are synchronizable and orientable networks. We can prove that all the effective networks with the same size have the same spectra, and are of the best synchronizability according to the master stability analysis. However, it is found that the synchronization time for different effective networks can be quite different. Further analysis shows that the key ingredient affecting the synchronization time is the maximal depth of an effective network: the larger depth results in a longer synchronization time. The secondary factor is the number of links. The increasing number of links connecting nodes in the same layer (horizontal links) will lead to longer synchronization time, whereas the increasing number of links connecting nodes in neighboring layers (vertical links) will accelerate the synchronization. Our analysis of the relationship between the structure and synchronization properties of the original and effective networks shows that the purely directed effective network can provide an approximation of the original weighted network with normalized input strength. Our findings provide insights into the roles of depth, horizontal and vertical links in the synchronizing process, and suggest that the spectral analysis is helpful yet insufficient for the comprehensive understanding of network synchronization.
Zhou Tao [Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054 (China); Zhao Ming [Department of Modern Physics, University of Science and Technology of China, Hefei 230026 (China); Zhou Changsong, E-mail: firstname.lastname@example.org [Department of Physics, Hong Kong Baptist University, Kowloon Tong (Hong Kong)
The study of network synchronization has attracted increasing attentionrecently. In this paper, we strictly define a class of networks, namely effective networks, which are synchronizable and orientable networks. We can prove that all the effective networks with the same size have the same spectra, and are of the best synchronizability according to the master stability analysis. However, it is found that the synchronization time for different effective networks can be quite different. Further analysis shows that the key ingredient affecting the synchronization time is the maximal depth of an effective network: the larger depth results in a longer synchronization time. The secondary factor is the number of links. The increasing number of links connecting nodes in the same layer (horizontal links) will lead to longer synchronization time, whereas the increasing number of links connecting nodes in neighboring layers (vertical links) will accelerate the synchronization. Our analysis of the relationship between the structure and synchronization properties of the original and effective networks shows that the purely directed effective network can provide an approximation of the original weighted network with normalized input strength. Our findings provide insights into the roles of depth, horizontal and vertical links in the synchronizing process, and suggest that the spectral analysis is helpful yet insufficient for the comprehensive understanding of network synchronization.
Partanen, Eino; Kujala, Teija; Näätänen, Risto; Liitola, Auli; Sambeth, Anke; Huotilainen, Minna
Learning, the foundation of adaptive and intelligent behavior, is based on plastic changes in neural assemblies, reflected by the modulation of electric brain responses. In infancy, auditory learning implicates the formation and strengthening of neural long-term memory traces, improving discrimination skills, in particular those forming the prerequisites for speech perception and understanding. Although previous behavioral observations show that newborns react differentially to unfamiliar sounds vs. familiar sound material that they were exposed to as fetuses, the neural basis of fetal learning has not thus far been investigated. Here we demonstrate direct neural correlates of human fetal learning of speech-like auditory stimuli. We presented variants of words to fetuses; unlike infants with no exposure to these stimuli, the exposed fetuses showed enhanced brain activity (mismatch responses) in response to pitch changes for the trained variants after birth. Furthermore, a significant correlation existed between the amount of prenatal exposure and brain activity, with greater activity being associated with a higher amount of prenatal speech exposure. Moreover, the learning effect was generalized to other types of similar speech sounds not included in the training material. Consequently, our results indicate neural commitment specifically tuned to the speech features heard before birth and their memory representations.
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...
Full Text Available Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that two years of group music classes in high school enhance the subcortical encoding of speech. To tease apart the relationships between music and neural function, we tested high school students participating in either music or fitness-based training. These groups were matched at the onset of training on neural timing, reading ability, and IQ. Auditory brainstem responses were collected to a synthesized speech sound presented in background noise. After 2 years of training, the subcortical responses of the music training group were earlier than at pretraining, while the neural timing of students in the fitness training group was unchanged. These results represent the strongest evidence to date that in-school music education can cause enhanced speech encoding. The neural benefits of musical training are, therefore, not limited to expensive private instruction early in childhood but can be elicited by cost-effective group instruction during adolescence.
Jandt, Uwe; Barradas, Oscar Platas; Pörtner, Ralf; Zeng, An-Ping
The consideration of inherent population inhomogeneities of mammalian cell cultures becomes increasingly important for systems biology study and for developing more stable and efficient processes. However, variations of cellular properties belonging to different sub-populations and their potential effects on cellular physiology and kinetics of culture productivity under bioproduction conditions have not yet been much in the focus of research. Culture heterogeneity is strongly determined by the advance of the cell cycle. The assignment of cell-cycle specific cellular variations to large-scale process conditions can be optimally determined based on the combination of (partially) synchronized cultivation under otherwise physiological conditions and subsequent population-resolved model adaptation. The first step has been achieved using the physical selection method of countercurrent flow centrifugal elutriation, recently established in our group for different mammalian cell lines which is presented in Part I of this paper series. In this second part, we demonstrate the successful adaptation and application of a cell-cycle dependent population balance ensemble model to describe and understand synchronized bioreactor cultivations performed with two model mammalian cell lines, AGE1.HNAAT and CHO-K1. Numerical adaptation of the model to experimental data allows for detection of phase-specific parameters and for determination of significant variations between different phases and different cell lines. It shows that special care must be taken with regard to the sampling frequency in such oscillation cultures to minimize phase shift (jitter) artifacts. Based on predictions of long-term oscillation behavior of a culture depending on its start conditions, optimal elutriation setup trade-offs between high cell yields and high synchronization efficiency are proposed. © 2014 American Institute of Chemical Engineers.
Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N
A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed
Moujahid, A.; D'Anjou, A.; Torrealdea, F.J.; Torrealdea, F.
Highlights: → Neural activity might be constrained by a requirement of energy efficiency. → Signaling in synchrony is a normal way to propagate information between neurons. → Quality of synchrony affects the capacity to exchange information and the energy cost. → Adaptive mechanism leads to high degree of synchronization between nonidentical neurons. - Abstract: The use of spikes to carry information between brain areas implies complete or partial synchronization of the neurons involved. The degree of synchronization reached by two coupled systems and the energy cost of maintaining their synchronized behavior is highly dependent on the nature of the systems. For non-identical systems the maintenance of a synchronized regime is energetically a costly process. In this work, we study conditions under which two non-identical electrically coupled neurons can reach an efficient regime of synchronization at low energy cost. We show that the energy consumption required to keep the synchronized regime can be spontaneously reduced if the receiving neuron has adaptive mechanisms able to bring its biological parameters closer in value to the corresponding ones in the sending neuron.
Fu, Chi Y.; Petrich, Loren; Law, Benjamin
Semiconductor fabrication lines have become extremely costly, and achieving a good return from such a high capital investment requires efficient utilization of these expensive facilities. It is highly desirable to shorten processing development time, increase fabrication yield, enhance flexibility, improve quality, and minimize downtime. We propose that these ends can be achieved by applying recent advances in the areas of artificial neural networks, fuzzy logic, machine learning, and genetic algorithms. We use the term neural manufacturing to describe such applications. This paper describes our use of artificial neural networks to improve the monitoring and control of semiconductor process.
Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong
This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms
Mikulandrić, Robert; Lončar, Dražen; Böhning, Dorith; Böhme, Rene; Beckmann, Michael
Highlights: • 2 Different equilibrium models are developed and their performance is analysed. • Neural network prediction models for 2 different fixed bed gasifier types are developed. • The influence of different input parameters on neural network model performance is analysed. • Methodology for neural network model development for different gasifier types is described. • Neural network models are verified for various operating conditions based on measured data. - Abstract: The number of the small and middle-scale biomass gasification combined heat and power plants as well as syngas production plants has been significantly increased in the last decade mostly due to extensive incentives. However, existing issues regarding syngas quality, process efficiency, emissions and environmental standards are preventing biomass gasification technology to become more economically viable. To encounter these issues, special attention is given to the development of mathematical models which can be used for a process analysis or plant control purposes. The presented paper analyses possibilities of neural networks to predict process parameters with high speed and accuracy. After a related literature review and measurement data analysis, different modelling approaches for the process parameter prediction that can be used for an on-line process control were developed and their performance were analysed. Neural network models showed good capability to predict biomass gasification process parameters with reasonable accuracy and speed. Measurement data for the model development, verification and performance analysis were derived from biomass gasification plant operated by Technical University Dresden
Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula
Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.
Casado, Jose Manuel
It is well known that a strong coupling can synchronize a population of nonlinear oscillators. This fact has deep implications for the current understanding of information processing by the brain. The focus of this Letter is on the role of conductance noise on a system of two coupled Hodgkin-Huxley neurons in the so-called excitable region, where both neurons are at rest in the absence of noise. It is shown that, in this region, conductance noise allows the neurons to achieve both frequency and phase synchronization. This suggests that internal noise could play a role in the emergence of synchronous neural activity in populations of weakly coupled neurons
Zhang, Wenhao; Li, Hanyu; Yang, Minda; Mesgarani, Nima
A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.
Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan
Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial
Lee, Sang Eun; Han, Yeji; Park, HyunWook
The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: email@example.com.
Juan Andres Laura
Full Text Available In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.
Waldinger, Robert J; Kensinger, Elizabeth A; Schulz, Marc S
This study examines whether differences in late-life well-being are linked to how older adults encode emotionally valenced information. Using fMRI with 39 older adults varying in life satisfaction, we examined how viewing positive and negative images would affect activation and connectivity of an emotion-processing network. Participants engaged most regions within this network more robustly for positive than for negative images, but within the PFC this effect was moderated by life satisfaction, with individuals higher in satisfaction showing lower levels of activity during the processing of positive images. Participants high in satisfaction showed stronger correlations among network regions-particularly between the amygdala and other emotion processing regions-when viewing positive, as compared with negative, images. Participants low in satisfaction showed no valence effect. Findings suggest that late-life satisfaction is linked with how emotion-processing regions are engaged and connected during processing of valenced information. This first demonstration of a link between neural recruitment and late-life well-being suggests that differences in neural network activation and connectivity may account for the preferential encoding of positive information seen in some older adults.
Romano, M Carmen; Thiel, Marco; Kurths, Jürgen; Mergenthaler, Konstantin; Engbert, Ralf
The method of twin surrogates has been introduced to test for phase synchronization of complex systems in the case of passive experiments. In this paper we derive new analytical expressions for the number of twins depending on the size of the neighborhood, as well as on the length of the trajectory. This allows us to determine the optimal parameters for the generation of twin surrogates. Furthermore, we determine the quality of the twin surrogates with respect to several linear and nonlinear statistics depending on the parameters of the method. In the second part of the paper we perform a hypothesis test for phase synchronization in the case of experimental data from fixational eye movements. These miniature eye movements have been shown to play a central role in neural information processing underlying the perception of static visual scenes. The high number of data sets (21 subjects and 30 trials per person) allows us to compare the generated twin surrogates with the "natural" surrogates that correspond to the different trials. We show that the generated twin surrogates reproduce very well all linear and nonlinear characteristics of the underlying experimental system. The synchronization analysis of fixational eye movements by means of twin surrogates reveals that the synchronization between the left and right eye is significant, indicating that either the centers in the brain stem generating fixational eye movements are closely linked, or, alternatively that there is only one center controlling both eyes.
Moreira-Tamayo, O.; Pineda de Gyvez, J.
This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This
Morimoto, Chie; Hida, Eisuke; Shima, Keisuke; Okamura, Hitoshi
To identify a specific sensorimotor impairment feature of autism spectrum disorder (ASD), we focused on temporal processing with millisecond accuracy. A synchronized finger-tapping task was used to characterize temporal processing in individuals with ASD as compared to typically developing (TD) individuals. We found that individuals with ASD…
Full Text Available An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.
Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying
A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.
Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.
Oleschuk, Valentin; Grandi, Gabriele; Sanjeevikumar, Padmanaban
Novel method of space-vector-based pulse-width modulation (PWM) has been disseminated for synchronous control of four inverters feeding six-phase drive on the base of asymmetrical induction motor which has two sets of windings spatially shifted by 30 electrical degrees. Basic schemes of synchronized PWM, applied for control of four separate voltage-source inverters, allow both continuous phase voltages synchronization in the system and required power sharing between DC sources. Detailed MATLA...
Robust adaptive synchronization; dynamical network; multiple delays; multiple uncertainties. ... Networks such as neural networks, communication transmission networks, social rela- tionship networks etc. ..... a very good effect. Pramana – J.
Liu, Yu-Zhang; Wang, Yao; Shen, Weida; Wang, Zhiru
Learning and memory storage requires neuronal plasticity induced in the hippocampus and other related brain areas, and this process is thought to rely on synchronized activity in neural networks. We used paired whole-cell recording in vivo to examine the synchronized activity that was induced in hippocampal CA1 neurons by associative fear learning. We found that both membrane potential synchronization and spike synchronization of CA1 neurons could be transiently enhanced after task learning, as observed on day 1 but not day 5. On day 1 after learning, CA1 neurons showed a decrease in firing threshold and rise times of suprathreshold membrane potential changes as well as an increase in spontaneous firing rates, possibly contributing to the enhancement of spike synchronization. The transient enhancement of CA1 neuronal synchronization may play important roles in the induction of neuronal plasticity for initial storage and consolidation of associative memory. The hippocampus is critical for memory acquisition and consolidation. This function requires activity- and experience-induced neuronal plasticity. It is known that neuronal plasticity is largely dependent on synchronized activity. As has been well characterized, repetitive correlated activity of presynaptic and postsynaptic neurons can lead to long-term modifications at their synapses. Studies on network activity have also suggested that memory processing in the hippocampus may involve learning-induced changes of neuronal synchronization, as observed in vivo between hippocampal CA3 and CA1 networks as well as between the rhinal cortex and the hippocampus. However, further investigation of learning-induced synchronized activity in the hippocampus is needed for a full understanding of hippocampal memory processing. In this study, by performing paired whole-cell recording in vivo on CA1 pyramidal cells (PCs) in anaesthetized adult rats, we examined CA1 neuronal synchronization before and after associative fear
Peter, Varghese; Kalashnikova, Marina; Burnham, Denis
This study aimed to investigate how children with dyslexia weight amplitude rise time (ART) and formant rise time (FRT) cues in phonetic discrimination. Passive mismatch responses (MMR) were recorded for a/ba/-/wa/contrast in a multiple deviant odd-ball paradigm to identify the neural response to cue weighting in 17 children with dyslexia and 17 age-matched control children. The deviant stimuli had either partial or full ART or FRT cues. The results showed that ART did not generate an MMR in either group, whereas both partial and full FRT cues generated MMR in control children while only full FRT cues generated MMR in children with dyslexia. These findings suggest that children, both controls and those with dyslexia, discriminate speech based on FRT cues and not ART cues. However, control children have greater sensitivity to FRT cues in speech compared to children with dyslexia. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D
We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.
Ferjan Ramirez, Naja; Leonard, Matthew K; Davenport, Tristan S; Torres, Christina; Halgren, Eric; Mayberry, Rachel I
One key question in neurolinguistics is the extent to which the neural processing system for language requires linguistic experience during early life to develop fully. We conducted a longitudinal anatomically constrained magnetoencephalography (aMEG) analysis of lexico-semantic processing in 2 deaf adolescents who had no sustained language input until 14 years of age, when they became fully immersed in American Sign Language. After 2 to 3 years of language, the adolescents' neural responses to signed words were highly atypical, localizing mainly to right dorsal frontoparietal regions and often responding more strongly to semantically primed words (Ferjan Ramirez N, Leonard MK, Torres C, Hatrak M, Halgren E, Mayberry RI. 2014. Neural language processing in adolescent first-language learners. Cereb Cortex. 24 (10): 2772-2783). Here, we show that after an additional 15 months of language experience, the adolescents' neural responses remained atypical in terms of polarity. While their responses to less familiar signed words still showed atypical localization patterns, the localization of responses to highly familiar signed words became more concentrated in the left perisylvian language network. Our findings suggest that the timing of language experience affects the organization of neural language processing; however, even in adolescence, language representation in the human brain continues to evolve with experience. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org.
The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activity in artificial neural networks. Here we propose and examine the dHAN concept for autonomous associative thought processes in dense and homogeneous associative networks. An associative thought-process is characterized, within this approach, by a time-series of transient attractors. Each transient state corresponds to a stored information, a memory. The subsequent transient states are characterized by large associative overlaps, which are identical to acquired patterns. Memory states, the acquired patterns, have such a dual functionality. In this approach the self-sustained neural activity has a central functional role. The network acquires a discrimination capability, as external stimuli need to compete with the autonomous activity. Noise in the input is readily filtered-out. Hebbian learning of external patterns occurs coinstantaneous with the ongoing associative thought process. The autonomous dynamics needs a long-term working-point optimization which acquires within the dHAN concept a dual functionality: It stabilizes the time development of the associative thought process and limits runaway synaptic growth, which generically occurs otherwise in neural networks with self-induced activities and Hebbian-type learning rules.
Kreidler, Kathryn; Wray, Amanda Hampton; Usler, Evan; Weber, Christine
Purpose: Maturation of neural processes for language may lag in some children who stutter (CWS), and event-related potentials (ERPs) distinguish CWS who have recovered from those who have persisted. The current study explores whether ERPs indexing semantic processing may distinguish children who will eventually persist in stuttering…
Arenas, A.; Diaz-Guilera, A.; Moreno, Y.; Zhou, C.; Kurths, J.
Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.
Mizuno, Kei; Tanaka, Masaaki; Tanabe, Hiroki C; Sadato, Norihiro; Watanabe, Yasuyoshi
The kana pick-out test has been widely used in Japan to evaluate the ability to divide attention in both adult and pediatric patients. However, the neural substrates underlying the ability to divide attention using the kana pick-out test, which requires participants to pick out individual letters (vowels) in a story while also reading for comprehension, thus requiring simultaneous allocation of attention to both activities, are still unclear. Moreover, outside of the clinical area, neuroimaging studies focused on the mechanisms of divided attention during complex story comprehension are rare. Thus, the purpose of the present study, to clarify the neural substrates of kana pick-out test, improves our current understanding of the basic neural mechanisms of dual task performance in verbal memory function. We compared patterns of activation in the brain obtained during performance of the individual tasks of vowel identification and story comprehension, to levels of activation when participants performed the two tasks simultaneously during the kana pick-out test. We found that activations of the left dorsal inferior frontal gyrus and superior parietal lobule increase in functional connectivity to a greater extent during the dual task condition compared to the two single task conditions. In contrast, activations of the left fusiform gyrus and middle temporal gyrus, which are significantly involved in picking out letters and complex sentences during story comprehension, respectively, were reduced in the dual task condition compared to during the two single task conditions. These results suggest that increased activations of the dorsal inferior frontal gyrus and superior parietal lobule during dual task performance may be associated with the capacity for attentional resources, and reduced activations of the left fusiform gyrus and middle temporal gyrus may reflect the difficulty of concurrent processing of the two tasks. In addition, the increase in synchronization between
Allam, Ahmed M; Abbas, Hazem M
Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.
Full Text Available Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article.
Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article. PMID:26858632
Full Text Available This work investigates the use of artificial neural networks in modeling an industrial fermentation process of Pleuromutilin produced by Pleurotus mutilus in a fed-batch mode. Three feed-forward neural network models characterized by a similar structure (five neurons in the input layer, one hidden layer and one neuron in the output layer are constructed and optimized with the aim to predict the evolution of three main bioprocess variables: biomass, substrate and product. Results show a good fit between the predicted and experimental values for each model (the root mean squared errors were 0.4624% - 0.1234 g/L and 0.0016 mg/g respectively. Furthermore, the comparison between the optimized models and the unstructured kinetic models in terms of simulation results shows that neural network models gave more significant results. These results encourage further studies to integrate the mathematical formulae extracted from these models into an industrial control loop of the process.
Miskowiak, Kamilla; O'Sullivan, Ursula; Harmer, Catherine J
with reduced attention to fear. Erythropoietin additionally reduced recognition of fearful facial expressions without affecting recognition of other emotional expressions. These actions occurred in the absence of changes in hematological parameters. CONCLUSIONS: The present study demonstrates that Epo directly......) versus saline on the neural processing of happy and fearful faces in 23 healthy volunteers. Facial expression recognition was assessed outside the scanner. RESULTS: One week after administration, Epo reduced neural response to fearful versus neutral faces in the occipito-parietal cortex consistent...... study aimed to explore the effects of Epo on neural and behavioral measures of emotional processing relevant for depression and the effects of conventional antidepressant medication. METHODS: In the present study, we used functional magnetic resonance imaging to explore the effects of Epo (40,000 IU...
Kupers, R; Beaulieu-Lefebvre, M; Schneider, F C
Adaptive neuroplastic changes have been well documented in congenitally blind individuals for the processing of tactile and auditory information. By contrast, very few studies have investigated olfactory processing in the absence of vision. There is ample evidence that the olfactory system...... magnetic resonance imaging to measure changes in the blood-oxygenation level-dependent signal in congenitally blind and blindfolded sighted control subjects during a simple odor detection task. We found several group differences in task-related activations. Compared to sighted controls, congenitally blind......, linking it also to olfactory processing in addition to tactile and auditory processing....
Fellner, Marie-Christin; Bäuml, Karl-Heinz T; Hanslmayr, Simon
Memory crucially depends on the way information is processed during encoding. Differences in processes during encoding not only lead to differences in memory performance but also rely on different brain networks. Although these assumptions are corroborated by several previous fMRI and ERP studies, little is known about how brain oscillations dissociate between different memory encoding tasks. The present study therefore compared encoding related brain oscillatory activity elicited by two very efficient encoding tasks: a typical deep semantic item feature judgment task and a more elaborative survival encoding task. Subjects were asked to judge words either for survival relevance or for animacy, as indicated by a cue presented prior to the item. This allowed dissociating pre-item activity from item-related activity for both tasks. Replicating prior studies, survival processing led to higher recognition performance than semantic processing. Successful encoding in the semantic condition was reflected by a strong decrease in alpha and beta power, whereas successful encoding in the survival condition was related to increased alpha and beta long-range phase synchrony. Moreover, a pre-item subsequent memory effect in theta power was found which did not vary with encoding condition. These results show that measures of local synchrony (power) and global long range-synchrony (phase synchronization) dissociate between memory encoding processes. Whereas semantic encoding was reflected in decreases in local synchrony, increases in global long range synchrony were related to elaborative survival encoding, presumably reflecting the involvement of a more widespread cortical network in this task. Copyright © 2013 Elsevier Inc. All rights reserved.
Full Text Available A quantitative structure-property relationship (QSPR study based on partial least squares (PLS and artificial neural network (ANN was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP, ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a 5-5-1 neural network was generated for prediction of ferric iron precipitation. The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set are 32.860, 40.739 and 35.890, respectively, which are smaller than those obtained by PLS model (180.972, 165.047 and 149.950, respectively. Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ferric iron precipitation in bioleaching process.
Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M.; Pohl, Klaus; Dubois, Eric
Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at
Oliver, Jason A; Jentink, Kade G; Drobes, David J; Evans, David E
There has been growing interest in the role that implicit processing of drug cues can play in motivating drug use behavior. However, the extent to which drug cue processing biases relate to the processing biases exhibited to other types of evocative stimuli is largely unknown. The goal of the present study was to determine how the implicit cognitive processing of smoking cues relates to the processing of affective cues using a novel paradigm. Smokers (n = 50) and nonsmokers (n = 38) completed a picture-viewing task, in which participants were presented with a series of smoking, pleasant, unpleasant, and neutral images while engaging in a distractor task designed to direct controlled resources away from conscious processing of image content. Electroencephalogram recordings were obtained throughout the task for extraction of event-related potentials (ERPs). Smokers exhibited differential processing of smoking cues across 3 different ERP indices compared with nonsmokers. Comparable effects were found for pleasant cues on 2 of these indices. Late cognitive processing of smoking and pleasant cues was associated with nicotine dependence and cigarette use. Results suggest that cognitive biases may extend across classes of stimuli among smokers. This raises important questions about the fundamental meaning of cognitive biases, and suggests the need to consider generalized cognitive biases in theories of drug use behavior and interventions based on cognitive bias modification. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Goerlich, Katharina Sophia; Votinov, Mikhail; Lammertz, Sarah E; Winkler, Lina; Spreckelmeyer, Katja N; Habel, Ute; Gründer, Gerhard; Gossen, Anna
Empathy has been found to affect the neural processing of social and monetary rewards. Alexithymia, a subclinical condition showing a close inverse relationship with empathy is linked to dysfunctions of socio-emotional processing in the brain. Whether alexithymia alters the neural processing of rewards, which is currently unknown. Here, we investigated the influence of both alexithymia and empathy on reward processing using a social incentive delay (SID) task and a monetary incentive delay (MID) task in 45 healthy men undergoing functional magnetic resonance imaging. Controlling for temperament-character dimensions and rejection sensitivity, the relationship of alexithymia and empathy with neural activity in several a priori regions of interest (ROIs) was examined by means of partial correlations, while participants anticipated and received social and monetary rewards. Results were considered significant if they survived Holm-Bonferroni correction for multiple comparisons. Alexithymia modulated neural activity in several ROIs of the emotion and reward network, both during the anticipation of social and monetary rewards and in response to the receipt of monetary rewards. In contrast, empathy did not affect reward anticipation and modulated ROI activity only in response to the receipt of social rewards. These results indicate a significant influence of alexithymia on the processing of social and monetary rewards in the healthy brain.
Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup
For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.
Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui
To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.
Zhang, Meng; Ma, Chao; Luo, Yanyan; Li, Ji; Li, Qingwei; Liu, Yijun; Ding, Cody; Qiu, Jiang
Individuals with high trait anxiety form a non-clinical group with a predisposition for an anxiety-related bias in emotional and cognitive processing that is considered by some to be a prerequisite for psychiatric disorders. Anxious individuals tend to experience more worry under uncertainty, and processing uncertain information is an important, but often overlooked factor in anxiety. So, we decided to explore the brain correlates of processing uncertain information in individuals with high trait anxiety using the learn-test paradigm. Behaviorally, the percentages on memory test and the likelihood ratios of identifying novel stimuli under uncertainty were similar to the certain fear condition, but different from the certain neutral condition. The brain results showed that the visual cortex, bilateral fusiform gyrus, and right parahippocampal gyrus were active during the processing of uncertain cues. Moreover, we found that trait anxiety was positively correlated with the BOLD signal of the right parahippocampal gyrus during the processing of uncertain cues. No significant results were found in the amygdala during uncertain cue processing. These results suggest that memory retrieval is associated with uncertain cue processing, which is underpinned by over-activation of the right parahippocampal gyrus, in individuals with high trait anxiety.
Talebnia, Farid; Mighani, Moein; Rahimnejad, Mostafa
and 67% of maximum theoretical value. Next, data of the experimental runs were exploited for modeling the processes by artificial neural networks (ANNs) and performance of the developed models was evaluated. The ANN-based models showed a great potential for time-course prediction of the studied processes....... Efficiency of the joint network for simulating the whole process was also determined and promising results were obtained....
This presentation has 2 parts:1. An introduction to the vision processing - neuroscience, and machine vision.2. Discussion of one of the first papers relating Deep Networks to the visual ventral stream. (Khaligh-Razavi, 2014)
Vagliasindi, G.; Arena, P.; Fortuna, L.; Mazzitelli, G.; Murari, A.
Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)
Kauffmann, Louise; Ramanoël, Stephen; Peyrin, Carole
Theories on visual perception agree that scenes are processed in terms of spatial frequencies. Low spatial frequencies (LSF) carry coarse information whereas high spatial frequencies (HSF) carry fine details of the scene. However, how and where spatial frequencies are processed within the brain remain unresolved questions. The present review addresses these issues and aims to identify the cerebral regions differentially involved in low and high spatial frequency processing, and to clarify their attributes during scene perception. Results from a number of behavioral and neuroimaging studies suggest that spatial frequency processing is lateralized in both hemispheres, with the right and left hemispheres predominantly involved in the categorization of LSF and HSF scenes, respectively. There is also evidence that spatial frequency processing is retinotopically mapped in the visual cortex. HSF scenes (as opposed to LSF) activate occipital areas in relation to foveal representations, while categorization of LSF scenes (as opposed to HSF) activates occipital areas in relation to more peripheral representations. Concomitantly, a number of studies have demonstrated that LSF information may reach high-order areas rapidly, allowing an initial coarse parsing of the visual scene, which could then be sent back through feedback into the occipito-temporal cortex to guide finer HSF-based analysis. Finally, the review addresses spatial frequency processing within scene-selective regions areas of the occipito-temporal cortex. PMID:24847226
Nguyen, Truong Thinh; Yang, Young Soo; Bae, Kang Yul; Choi, Sung Nam
To control a heat source easily in the forming process of steel plate with heating, the electro-magnetic induction process has been used as a substitute of the flame heating process. However, only few studies have analyzed the deformation of a workpiece in the induction heating process by using a mathematical model. This is mainly due to the difficulty of modeling the heat flux from the inductor traveling on the conductive plate during the induction process. In this study, the heat flux distribution over a steel plate during the induction process is first analyzed by a numerical method with the assumption that the process is in a quasi-stationary state around the inductor and also that the heat flux itself greatly depends on the temperature of the workpiece. With the heat flux, heat flow and thermo-mechanical analyses on the plate to obtain deformations during the heating process are then performed with a commercial FEM program for 34 combinations of heating parameters. An artificial neural network is proposed to build a simplified relationship between deformations and heating parameters that can be easily utilized to predict deformations of steel plate with a wide range of heating parameters in the heating process. After its architecture is optimized, the artificial neural network is trained with the deformations obtained from the FEM analyses as outputs and the related heating parameters as inputs. The predicted outputs from the neural network are compared with those of the experiments and the numerical results. They are in good agreement
Full Text Available The paper deals with the correlation of the business cycles between the Eurozone member states in the period 1957–2003. The analysed period is divided into the four parts (1959–1972, 1973–1985, 1986–1994, 1995–2003, which correspond to integration waves and relate approximately to the European integration process. The empirical analysis is based on the time series correlation. The authors discuss the impact of the EC enlargements on the business cycles correlation as well as on qualitative changes in the interaction between the states.
Ritchey, Maureen; LaBar, Kevin S.; Cabeza, Roberto
Emotion is known to influence multiple aspects of memory formation, including the initial encoding of the memory trace and its consolidation over time. However, the neural mechanisms whereby emotion impacts memory encoding remain largely unexplored. The present study used a levels-of-processing manipulation to characterize the impact of emotion on…
Oh, Hwamee; Leung, Hoi-Chung
In this fMRI study, we investigated prefrontal cortex (PFC) and visual association regions during selective information processing. We recorded behavioral responses and neural activity during a delayed recognition task with a cue presented during the delay period. A specific cue ("Face" or "Scene") was used to indicate which one of the two…
van Vugt, Marieke K; Beulen, Marijke A; Taatgen, Niels A
Models of evidence accumulation have been very successful at describing human decision making behavior. Recent years have also seen the first reports of neural correlates of this accumulation process. However, these studies have mostly focused on perceptual decision making tasks, ignoring the role
Simon, J.J.; Walther, S.; Fiebach, C.J.; Friederich, H.C.; Stippich, C.; Weisbrod, M.; Kaiser, S.
The neural processing of reward can be differentiated into two sub-components with different functions, "wanting" (i.e., the expectation of a reward which includes appetitive and motivational components) and "liking" (i.e., the hedonic impact experienced during the receipt of a reward), involving
Wang, Lihong; Huettel, Scott; De Bellis, Michael D.
Neural systems related to cognitive and emotional processing were examined in adolescents using event-related functional magnetic resonance imaging (fMRI). Ten healthy adolescents performed an emotional oddball task. Subjects detected infrequent circles (targets) within a continual stream of phase-scrambled images (standards). Sad and neutral…
Yu, Hang; Jiang, Junqiu; Zhao, Qingliang; Wang, Kun; Zhang, Yunshu; Zheng, Zhen; Hao, Xiaodi
Bioelectrochemically-assisted anaerobic composting process (AnCBE) with dewatered sludge as the anode fuel was constructed to accelerate composting of dewatered sludge, which could increase the quality of the compost and harvest electric energy in comparison with the traditional anaerobic composting (AnC). Results revealed that the AnCBE yielded a voltage of 0.60 ± 0.02 V, and total COD (TCOD) removal reached 19.8 ± 0.2% at the end of 35 d. The maximum power density was 5.6 W/m(3). At the end of composting, organic matter content (OM) reduction rate increased to 19.5 ± 0.2% in AnCBE and to 12.9 ± 0.1% in AnC. The fuzzy comprehensive assessment (FCA) result indicated that the membership degree of class I of AnCBE compost (0.64) was higher than that of AnC compost (0.44). It was demonstrated that electrogenesis in the AnCBE could improve the sludge stabilization degree, accelerate anaerobic composting process and enhance composting maturity with bioelectricity generation. Copyright © 2015 Elsevier Ltd. All rights reserved.
Full Text Available Musical training has been shown to have positive effects on several aspects of speech processing, however, the effects of musical training on the neural processing of speech prosody conveying distinct emotions are yet to be better understood. We used functional magnetic resonance imaging (fMRI to investigate whether the neural responses to speech prosody conveying happiness, sadness, and fear differ between musicians and non-musicians. Differences in processing of emotional speech prosody between the two groups were only observed when sadness was expressed. Musicians showed increased activation in the middle frontal gyrus, the anterior medial prefrontal cortex, the posterior cingulate cortex and the retrosplenial cortex. Our results suggest an increased sensitivity of emotional processing in musicians with respect to sadness expressed in speech, possibly reflecting empathic processes.
Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.
The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.
Cheng, Ranran; Peng, Mingshu; Yu, Weibin; Sun, Bo; Yu, Jinchen
A new network of coupled maps is proposed in which the connections between units involve no delays but the intra-neural communication does, whereas in the work of Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], the focus is on information processing delayed by the inter-neural communication. We show that the synchronization of the network depends on not only the intrinsic dynamical features and inter-connection topology (characterized by the spectrum of the graph Laplacian) but also the delays and the coupling strength. There are two main findings: (i) the more neighbours, the easier to be synchronized; (ii) odd delays are easier to be synchronized than even ones. In addition, compared with those discussed by Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], our model has a better synchronizability for regular networks and small-world variants.
Seixas, Jose M. de; Soares Filho, William; Pereira, Wagner C.A.; Teles, Claudio C.B.
Ultrasonic pulses were used for automatic detection of failed nuclear fuel rods. For experimental tests of the proposed method, an assembly prototype of 16 x 16 rods was built by using genuine rods but without fuel inside (just air). Some rods were partially filled with water to simulate cracked rods. Using neural signal processing on the received echoes of the emitted ultrasonic pulses, a detection efficiency of 97% was obtained. Neural detection is shown to outperform other classical discriminating methods and can also reveal important features of the signal structure of the received echoes. (author)
Full Text Available Growth regulating factors (GRFs are a conserved class of transcription factor in seed plants. GRFs are involved in various aspects of tissue differentiation and organ development. The implication of GRFs in biotic stress response has also been recently reported, suggesting a role of these transcription factors in coordinating the interaction between developmental processes and defense dynamics. However, the molecular mechanisms by which GRFs mediate the overlaps between defense signaling and developmental pathways are elusive. Here, we report large scale identification of putative target candidates of Arabidopsis GRF1 and GRF3 by comparing mRNA profiles of the grf1/grf2/grf3 triple mutant and those of the transgenic plants overexpressing miR396-resistant version of GRF1 or GRF3. We identified 1,098 and 600 genes as putative targets of GRF1 and GRF3, respectively. Functional classification of the potential target candidates revealed that GRF1 and GRF3 contribute to the regulation of various biological processes associated with defense response and disease resistance. GRF1 and GRF3 participate specifically in the regulation of defense-related transcription factors, cell-wall modifications, cytokinin biosynthesis and signaling, and secondary metabolites accumulation. GRF1 and GRF3 seem to fine-tune the crosstalk between miRNA signaling networks by regulating the expression of several miRNA target genes. In addition, our data suggest that GRF1 and GRF3 may function as negative regulators of gene expression through their association with other transcription factors. Collectively, our data provide new insights into how GRF1 and GRF3 might coordinate the interactions between defense signaling and plant growth and developmental pathways.
Górna, K.; Jaśkowski, B. M.; Okoń, P.; Czechlowski, M.; Koszela, K.; Zaborowicz, M.; Idziaszek, P.
The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.
Upadhyaya, B.R.; Yan, W.
The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods
Pringle, A; Ashworth, F; Harmer, C J; Norbury, R; Cooper, M J
There is increasing interest in understanding the roles of distorted beliefs about the self, ostensibly unrelated to eating, weight and shape, in eating disorders (EDs), but little is known about their neural correlates. We therefore used functional magnetic resonance imaging to investigate the neural correlates of self-referent emotional processing in EDs. During the scan, unmedicated patients with bulimia nervosa (n=11) and healthy controls (n=16) responded to personality words previously found to be related to negative self beliefs in EDs and depression. Rating of the negative personality descriptors resulted in reduced activation in patients compared to controls in parietal, occipital and limbic areas including the amygdala. There was no evidence that reduced activity in patients was secondary to increased cognitive control. Different patterns of neural activation between patients and controls may be the result of either habituation to personally relevant negative self beliefs or of emotional blunting in patients. Copyright © 2011 Elsevier Ltd. All rights reserved.
Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in
Foster, J. Nathan; Greenwald, Michael B.; Kirkegaard, Christian
Increased reliance on optimistic data replication has led to burgeoning interest in tools and frameworks for disconnected updates to replicated data.We have implemented a generic synchronization framework, called HARMONY, that can be used to build state-based synchronizers for a wide variety...... of tree-structureddata formats. A novel feature of this framework is that the synchronization process - in particular, the recognition of conflicts - is driven by the schema of the structures being synchronized.We formalize HARMONY's synchronization algorithm, state a simple and intuitive specification......, and illustrate, using simple address books as a case study, how it can be used to synchronize trees representing a variety of specific forms of applicationdata, including sets, records, tuples, and relations....
Sebastian, Catherine L; Fontaine, Nathalie M G; Bird, Geoffrey; Blakemore, Sarah-Jayne; Brito, Stephane A De; McCrory, Eamon J P; Viding, Essi
Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11-16 years) and 15 adult (aged 24-40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing.
Bridges, Robert S.
The reproductive experience of pregnancy, lactation and motherhood can significantly remodel the female’s biological state, affecting endocrine, neuroendocrine, neural, and immunological processes. The brain, pituitary gland, liver, thymus, and mammary tissue are among the structures that are modified by reproductive experience. The present review that focuses on rodent research, but also includes pertinent studies in sheep and other species, identifies specific changes in these processes brought about by the biological states of pregnancy, parturition, and lactation and how the components of reproductive experience contribute to the remodeling of the maternal brain and organ systems. Findings indicate that prior parity alters key circulating hormone levels and neural receptor gene expression. Moreover, reproductive experience results in modifications in neural processes and glial support. The possible role of pregnancy-induced neurogenesis is considered in the context of neuroplasticity and behavior, and the effects of reproductive experience on maternal memory, i.e. the retention of maternal behavior, together with anxiety and learning are presented. Together, these sets of findings support the concept that the neural and biological state of the adult female is significantly and dramatically altered on a long-term basis by the experiences of parity and motherhood. Remodeling of the maternal brain and other biological systems is posited to help facilitate adaptations to environmental/ecological challenges as the female raises young and ages. PMID:26388065
Maron, Eduard; Wall, Matt; Norbury, Ray; Godlewska, Beata; Terbeck, Sylvia; Cowen, Philip; Matthews, Paul; Nutt, David J
Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs. © The Author(s) 2015.
Sylvia A. Morelli
Full Text Available Although many studies have examined the neural basis of experiencing empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, while instructed to empathize, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; septal area, SA. Two key regions – the ventral AI and SA – were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching versus empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others’ emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy (DMPFC, MPFC, TPJ, amygdala and social cognition. The current results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions.
Full Text Available Introduction. Event-related brain potentials (ERP provide important information about the sensitivity of the brain to process varying risks. The aim of the present study was to determine how different risk levels are reflected in decision-related ERPs, namely the feedback-related negativity (FRN and the P300. Material and Methods. 20 participants conducted a probabilistic two-choice gambling task while an electroencephalogram was recorded. Choices were provided between a low-risk option yielding low rewards and low losses and a high-risk option yielding high rewards and high losses. While options differed in expected risks, they were equal in expected values and in feedback probabilities. Results. At the behavioral level, participants were generally risk-averse but modulated their risk-taking behavior according to reward history. An early positivity (P200 was enhanced on negative feedbacks in high-risk compared to low-risk options. With regard to the FRN, there were significant amplitude differences between positive and negative feedbacks in high-risk options, but not in low-risk options. While the FRN on negative feedbacks did not vary with decision riskiness, reduced amplitudes were found for positive feedbacks in high-risk relative to low-risk choices. P300 amplitudes were larger in high-risk decisions, and in an additive way, after negative compared to positive feedback. Discussion. The present study revealed significant influences of risk and valence processing on ERPs. FRN findings suggest that the reward prediction error signal is increased after high-risk decisions. The increased P200 on negative feedback in risky decisions suggests that large negative prediction errors are processed as early as in the P200 time range. The later P300 amplitude is sensitive to feedback valence as well as to the risk of a decision. Thus, the P300 carries additional information for reward processing, mainly the enhanced motivational significance of risky
Barrós-Loscertales, Alfonso; Ventura-Campos, Noelia; Visser, Maya; Alsius, Agnès; Pallier, Christophe; Avila Rivera, César; Soto-Faraco, Salvador
Neuroimaging studies of audiovisual speech processing have exclusively addressed listeners' native language (L1). Yet, several behavioural studies now show that AV processing plays an important role in non-native (L2) speech perception. The current fMRI study measured brain activity during auditory, visual, audiovisual congruent and audiovisual incongruent utterances in L1 and L2. BOLD responses to congruent AV speech in the pSTS were stronger than in either unimodal condition in both L1 and L2. Yet no differences in AV processing were expressed according to the language background in this area. Instead, the regions in the bilateral occipital lobe had a stronger congruency effect on the BOLD response (congruent higher than incongruent) in L2 as compared to L1. According to these results, language background differences are predominantly expressed in these unimodal regions, whereas the pSTS is similarly involved in AV integration regardless of language dominance. Copyright © 2013 Elsevier Inc. All rights reserved.
Bao, Yan; Pöppel, Ernst; Wang, Lingyan; Lin, Xiaoxiong; Yang, Taoxi; Avram, Mihai; Blautzik, Janusch; Paolini, Marco; Silveira, Sarita; Vedder, Aline; Zaytseva, Yuliya; Zhou, Bin
Synchronizing neural processes, mental activities, and social interactions is considered to be fundamental for the creation of temporal order on the personal and interpersonal level. Several different types of synchronization are distinguished, and for each of them examples are given: self-organized synchronizations on the neural level giving rise to pre-semantically defined time windows of some tens of milliseconds and of approximately 3 s; time windows that are created by synchronizing different neural representations, as for instance in aesthetic appreciations or moral judgments; and synchronization of biological rhythms with geophysical cycles, like the circadian clock with the 24-hr rhythm of day and night. For the latter type of synchronization, an experiment is described that shows the importance of social interactions for sharing or avoiding common time. In a group study with four subjects being completely isolated together for 3 weeks from the external world, social interactions resulted both in intra- and interindividual circadian synchronization and desynchronization. A unique phenomenon in circadian regulation is described, the "beat phenomenon," which has been made visible by the interaction of two circadian rhythms with different frequencies in one body. The separation of the two physiological rhythms was the consequence of social interactions, that is, by the desire of a subject to share and to escape common time during different phases of the long-term experiment. The theoretical arguments on synchronization are summarized with the general statement: "Nothing in cognitive science makes sense except in the light of time windows." The hypothesis is forwarded that time windows that express discrete timing mechanisms in behavioral control and on the level of conscious experiences are the necessary bases to create cognitive order, and it is suggested that time windows are implemented by neural oscillations in different frequency domains. © 2015 The
Esteban, L. G.; Garcia Fernandez, F.; Palacios, P. de; Conde, M.
Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. (Author) 47 refs.
Zhao, T Christina; Lam, H T Gloria; Sohi, Harkirat; Kuhl, Patricia K
Musical sounds, along with speech, are the most prominent sounds in our daily lives. They are highly dynamic, yet well structured in the temporal domain in a hierarchical manner. The temporal structures enhance the predictability of musical sounds. Western music provides an excellent example: while time intervals between musical notes are highly variable, underlying beats can be realized. The beat-level temporal structure provides a sense of regular pulses. Beats can be further organized into units, giving the percept of alternating strong and weak beats (i.e. metrical structure or meter). Examining neural processing at the meter level offers a unique opportunity to understand how the human brain extracts temporal patterns, predicts future stimuli and optimizes neural resources for processing. The present study addresses two important questions regarding meter processing, using the mismatch negativity (MMN) obtained with electroencephalography (EEG): 1) how tempo (fast vs. slow) and type of metrical structure (duple: two beats per unit vs. triple: three beats per unit) affect the neural processing of metrical structure in non-musically trained individuals, and 2) how early music training modulates the neural processing of metrical structure. Metrical structures were established by patterns of consecutive strong and weak tones (Standard) with occasional violations that disrupted and reset the structure (Deviant). Twenty non-musicians listened passively to these tones while their neural activities were recorded. MMN indexed the neural sensitivity to the meter violations. Results suggested that MMNs were larger for fast tempo and for triple meter conditions. Further, 20 musically trained individuals were tested using the same methods and the results were compared to the non-musicians. While tempo and meter type similarly influenced MMNs in both groups, musicians overall exhibited significantly reduced MMNs, compared to their non-musician counterparts. Further analyses
Full Text Available During social interaction, both participants are continuously active, each modifying their own actions in response to the continuously changing actions of the partner. This continuous mutual adaptation results in interactional synchrony to which both members contribute. Freely exchanging the role of imitator and model is a well-framed example of interactional synchrony resulting from a mutual behavioral negotiation. How the participants' brain activity underlies this process is currently a question that hyperscanning recordings allow us to explore. In particular, it remains largely unknown to what extent oscillatory synchronization could emerge between two brains during social interaction. To explore this issue, 18 participants paired as 9 dyads were recorded with dual-video and dual-EEG setups while they were engaged in spontaneous imitation of hand movements. We measured interactional synchrony and the turn-taking between model and imitator. We discovered by the use of nonlinear techniques that states of interactional synchrony correlate with the emergence of an interbrain synchronizing network in the alpha-mu band between the right centroparietal regions. These regions have been suggested to play a pivotal role in social interaction. Here, they acted symmetrically as key functional hubs in the interindividual brainweb. Additionally, neural synchronization became asymmetrical in the higher frequency bands possibly reflecting a top-down modulation of the roles of model and imitator in the ongoing interaction.
Liu, Yan; Pereira, João L.; Constandinou, Timothy G.
Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.
Nina V. Komleva
Full Text Available The article is devoted to the issues of mathematical modeling of the decision-making process of information content processing based on the fuzzy neural network TSK. Integral rating assessment of the content, which is necessary for taking a decision about its further usage, is made depended on varying characteristics. Mechanism for building individual trajectory and forming individual competence is provided to make the intellectual content search.
Stockdale, Laura A.; Morrison, Robert G.; Kmiecik, Matthew J.; Garbarino, James; Silton, Rebecca L.
Media violence exposure causes increased aggression and decreased prosocial behavior, suggesting that media violence desensitizes people to the emotional experience of others. Alterations in emotional face processing following exposure to media violence may result in desensitization to others’ emotional states. This study used scalp electroencephalography methods to examine the link between exposure to violence and neural changes associated with emotional face processing. Twenty-five particip...
Ritchey, Maureen; LaBar, Kevin S.; Cabeza, Roberto
Emotion is known to influence multiple aspects of memory formation, including the initial encoding of the memory trace and its consolidation over time. However, the neural mechanisms whereby emotion impacts memory encoding remain largely unexplored. The present study employed a levels-of-processing manipulation to characterize the impact of emotion on encoding with and without the influence of elaborative processes. Participants viewed emotionally negative, neutral, and positive scenes under ...
Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.
Gong, Yubing; Xu, Bo; Wu, Ya'nan
In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.
Contreras-Rodríguez, Oren; Pujol, Jesus; Batalla, Iolanda; Harrison, Ben J; Bosque, Javier; Ibern-Regàs, Immaculada; Hernández-Ribas, Rosa; Soriano-Mas, Carles; Deus, Joan; López-Solà, Marina; Pifarré, Josep; Menchón, José M; Cardoner, Narcís
Psychopaths show a reduced ability to recognize emotion facial expressions, which may disturb the interpersonal relationship development and successful social adaptation. Behavioral hypotheses point toward an association between emotion recognition deficits in psychopathy and amygdala dysfunction. Our prediction was that amygdala dysfunction would combine deficient activation with disturbances in functional connectivity with cortical regions of the face-processing network. Twenty-two psychopaths and 22 control subjects were assessed and functional magnetic resonance maps were generated to identify both brain activation and task-induced functional connectivity using psychophysiological interaction analysis during an emotional face-matching task. Results showed significant amygdala activation in control subjects only, but differences between study groups did not reach statistical significance. In contrast, psychopaths showed significantly increased activation in visual and prefrontal areas, with this latest activation being associated with psychopaths' affective-interpersonal disturbances. Psychophysiological interaction analyses revealed a reciprocal reduction in functional connectivity between the left amygdala and visual and prefrontal cortices. Our results suggest that emotional stimulation may evoke a relevant cortical response in psychopaths, but a disruption in the processing of emotional faces exists involving the reciprocal functional interaction between the amygdala and neocortex, consistent with the notion of a failure to integrate emotion into cognition in psychopathic individuals.
Liu, Yuelu; Huang, Haiqing; McGinnis, Menton; Keil, Andreas; Ding, Mingzhou
The late positive potential (LPP) is a reliable electrophysiological index of emotional perception in humans. Despite years of research the brain structures that contribute to the generation and modulation of LPP are not well understood. Recording EEG and fMRI simultaneously, and applying a recently proposed single-trial ERP analysis method, we addressed the problem by correlating the single-trial LPP amplitude evoked by affective pictures with the blood-oxygen-level-dependent (BOLD) activity. Three results were found. First, relative to neutral pictures, pleasant and unpleasant pictures elicited enhanced LPP, as well as heightened BOLD activity in both visual cortices and emotion-processing structures such as amygdala and prefrontal cortex, consistent with previous findings. Second, the LPP amplitude across three picture categories was significantly correlated with BOLD activity in visual cortices, temporal cortices, amygdala, orbitofrontal cortex, and insula. Third, within each picture category, LPP-BOLD coupling revealed category-specific differences. For pleasant pictures, the LPP amplitude was coupled with BOLD in occipitotemporal junction, medial prefrontal cortex, amygdala, and precuneus, whereas for unpleasant pictures, significant LPP-BOLD correlation was observed in ventrolateral prefrontal cortex, insula, and posterior cingulate cortex. These results suggest that LPP is generated and modulated by an extensive brain network comprised of both cortical and subcortical structures associated with visual and emotional processing and the degree of contribution by each of these structures to the LPP modulation is valence-specific. PMID:23077042
Rachel C. Leung
Full Text Available Social cognition is impaired in autism spectrum disorder (ASD. The ability to perceive and interpret affect is integral to successful social functioning and has an extended developmental course. However, the neural mechanisms underlying emotional face processing in ASD are unclear. Using magnetoencephalography (MEG, the present study explored neural activation during implicit emotional face processing in young adults with and without ASD. Twenty-six young adults with ASD and 26 healthy controls were recruited. Participants indicated the location of a scrambled pattern (target that was presented alongside a happy or angry face. Emotion-related activation sources for each emotion were estimated using the Empirical Bayes Beamformer (pcorr ≤ 0.001 in Statistical Parametric Mapping 12 (SPM12. Emotional faces elicited elevated fusiform, amygdala and anterior insula and reduced anterior cingulate cortex (ACC activity in adults with ASD relative to controls. Within group comparisons revealed that angry vs. happy faces elicited distinct neural activity in typically developing adults; there was no distinction in young adults with ASD. Our data suggest difficulties in affect processing in ASD reflect atypical recruitment of traditional emotional processing areas. These early differences may contribute to difficulties in deriving social reward from faces, ascribing salience to faces, and an immature threat processing system, which collectively could result in deficits in emotional face processing.
Brattico, Elvira; Tupala, Tiina; Glerean, Enrico; Tervaniemi, Mari
A chord deviating from the conventions of Western tonal music elicits an early right anterior negativity (ERAN) in inferofrontal brain regions. Here, we tested whether the ERAN is modulated by expertise in more than one music culture, as typical of folk musicians. Finnish folk musicians and nonmusicians participated in electroencephalography recordings. The cadences consisted of seven chords. In incongruous cadences, the third, fifth, or seventh chord was a Neapolitan. The ERAN to the Neapolitans was enhanced in folk musicians compared to nonmusicians. Folk musicians showed an enhanced P3a for the ending Neapolitan. The Neapolitan at the fifth position was perceived differently and elicited a late enhanced ERAN in folk musicians. Hence, expertise in more than one music culture seems to modify chord processing by enhancing the ERAN to ambivalent chords and the P3a to incongruous chords, and by altering their perceptual attributes. Copyright © 2013 Society for Psychophysiological Research.
Payer, Doris E; Lieberman, Matthew D; London, Edythe D
Methamphetamine abuse is associated with high rates of aggression but few studies have addressed the contributing neurobiological factors. To quantify aggression, investigate function in the amygdala and prefrontal cortex, and assess relationships between brain function and behavior in methamphetamine-dependent individuals. In a case-control study, aggression and brain activation were compared between methamphetamine-dependent and control participants. Participants were recruited from the general community to an academic research center. Thirty-nine methamphetamine-dependent volunteers (16 women) who were abstinent for 7 to 10 days and 37 drug-free control volunteers (18 women) participated in the study; subsets completed self-report and behavioral measures. Functional magnetic resonance imaging (fMRI) was performed on 25 methamphetamine-dependent and 23 control participants. We measured self-reported and perpetrated aggression and self-reported alexithymia. Brain activation was assessed using fMRI during visual processing of facial affect (affect matching) and symbolic processing (affect labeling), the latter representing an incidental form of emotion regulation. Methamphetamine-dependent participants self-reported more aggression and alexithymia than control participants and escalated perpetrated aggression more following provocation. Alexithymia scores correlated with measures of aggression. During affect matching, fMRI showed no differences between groups in amygdala activation but found lower activation in methamphetamine-dependent than control participants in the bilateral ventral inferior frontal gyrus. During affect labeling, participants recruited the dorsal inferior frontal gyrus and exhibited decreased amygdala activity, consistent with successful emotion regulation; there was no group difference in this effect. The magnitude of decrease in amygdala activity during affect labeling correlated inversely with self-reported aggression in control participants
Zhao, Weihua; Luo, Lizhu; Li, Qin; Kendrick, Keith M
Many psychiatric disorders are associated with abnormal self-processing. While these disorders also have a wide-range of complex, and often heterogeneous sets of symptoms involving different cognitive, emotional, and motor domains, an impaired sense of self can contribute to many of these. Research investigating self-processing in healthy subjects has facilitated identification of changes in specific neural circuits which may cause altered self-processing in psychiatric disorders. While there is evidence for altered self-processing in many psychiatric disorders, here we will focus on four of the most studied ones, schizophrenia, autism spectrum disorder (ASD), major depression, and borderline personality disorder (BPD). We review evidence for dysfunction in two different neural systems implicated in self-processing, namely the cortical midline system (CMS) and the mirror neuron system (MNS), as well as contributions from altered inter-hemispheric connectivity (IHC). We conclude that while abnormalities in frontal-parietal activity and/or connectivity in the CMS are common to all four disorders there is more disruption of integration between frontal and parietal regions resulting in a shift toward parietal control in schizophrenia and ASD which may contribute to the greater severity and delusional aspects of their symptoms. Abnormalities in the MNS and in IHC are also particularly evident in schizophrenia and ASD and may lead to disturbances in sense of agency and the physical self in these two disorders. A better future understanding of how changes in the neural systems sub-serving self-processing contribute to different aspects of symptom abnormality in psychiatric disorders will require that more studies carry out detailed individual assessments of altered self-processing in conjunction with measurements of neural functioning.
Krizman, Jennifer; Slater, Jessica; Skoe, Erika; Marian, Viorica; Kraus, Nina
Language experience fine-tunes how the auditory system processes sound. For example, bilinguals, relative to monolinguals, have more robust evoked responses to speech that manifest as stronger neural encoding of the fundamental frequency (F0) and greater across-trial consistency. However, it is unknown whether such enhancements increase with increasing second language experience. We predict that F0 amplitude and neural consistency scale with dual-language experience during childhood, such that more years of bilingual experience leads to more robust F0 encoding and greater neural consistency. To test this hypothesis, we recorded auditory brainstem responses to the synthesized syllables ‘ba’ and ‘ga’ in two groups of bilingual children who were matched for age at test (8.4+/−0.67 years) but differed in their age of second language acquisition. One group learned English and Spanish simultaneously from birth (n=13), while the second group learned the two languages sequentially (n=15), spending on average their first four years as monolingual Spanish speakers. We find that simultaneous bilinguals have a larger F0 response to ‘ba’ and ‘ga’ and a more consistent response to ‘ba’ compared to sequential bilinguals. We also demonstrate that these neural enhancements positively relate with years of bilingual experience. These findings support the notion that bilingualism enhances subcortical auditory processing. PMID:25445377
Nowak, Grzegorz; Rusin, Andrzej
Highlights: • Inverse Artificial Neural Network has a potential to control the start-up process of a steam turbine. • Two serial neural networks made it possible to model the rotor stress based of steam parameters. • An ANN with feedback enables transient stress modelling with good accuracy. - Abstract: Due to the significant share of renewable energy sources (RES) – wind farms in particular – in the power sector of many countries, power generation systems become sensitive to variable weather conditions. Under unfavourable changes in weather, ensuring required energy supplies involves hasty start-ups of conventional steam power units whose operation should be characterized by higher and higher flexibility. Controlling the process of power engineering machinery operation requires fast predictive models that will make it possible to analyse many parallel scenarios and select the most favourable one. This approach is employed by the algorithm for the inverse neural network control presented in this paper. Based on the current thermal state of the turbine casing, the algorithm controls the steam temperature at the turbine inlet to keep both the start-up rate and the safety of the machine at the allowable level. The method used herein is based on two artificial neural networks (ANN) working in series.
McCabe, Ciara; Mishor, Zevic; Cowen, Philip J.; Harmer, Catherine J.
Background Selective serotonin reuptake inhibitors (SSRIs) are popular medications for anxiety and depression, but their effectiveness, particularly in patients with prominent symptoms of loss of motivation and pleasure, has been questioned. There are few studies of the effect of SSRIs on neural reward mechanisms in humans. Methods We studied 45 healthy participants who were randomly allocated to receive the SSRI citalopram, the noradrenaline reuptake inhibitor reboxetine, or placebo for 7 days in a double-blind, parallel group design. We used functional magnetic resonance imaging to measure the neural response to rewarding (sight and/or flavor of chocolate) and aversive stimuli (sight of moldy strawberries and/or an unpleasant strawberry taste) on the final day of drug treatment. Results Citalopram reduced activation to the chocolate stimuli in the ventral striatum and the ventral medial/orbitofrontal cortex. In contrast, reboxetine did not suppress ventral striatal activity and in fact increased neural responses within medial orbitofrontal cortex to reward. Citalopram also decreased neural responses to the aversive stimuli conditions in key “punishment” areas such as the lateral orbitofrontal cortex. Reboxetine produced a similar, although weaker effect. Conclusions Our findings are the first to show that treatment with SSRIs can diminish the neural processing of both rewarding and aversive stimuli. The ability of SSRIs to decrease neural responses to reward might underlie the questioned efficacy of SSRIs in depressive conditions characterized by decreased motivation and anhedonia and could also account for the experience of emotional blunting described by some patients during SSRI treatment. PMID:20034615
Vanni, Simo; Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo
Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. Copyright © 2015 the American Physiological Society.
Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng
Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.
Full Text Available Contemporary functional neuroimaging research has increasingly focused on characterization of intrinsic or “spontaneous” brain activity. Analysis of intrinsic activity is often contrasted with analysis of task-evoked activity that has traditionally been the focus of cognitive neuroscience. But does this evoked/intrinsic dichotomy adequately characterize human brain function? Based on empirical data demonstrating a close functional interdependence between intrinsic and task-evoked activity, we argue that the dichotomy between intrinsic and task-evoked activity as unobserved contributions to brain activity is artificial. We present an alternative picture of brain function in which the brain’s spatiotemporal dynamics do not consist of separable intrinsic and task-evoked components, but reflect the enaction of a system of mutual constraints to move the brain into and out of task-appropriate functional configurations. According to this alternative picture, cognitive neuroscientists are tasked with describing both the temporal trajectory of brain activity patterns across time, and the modulation of this trajectory by task states, without separating this process into intrinsic and task-evoked components. We argue that this alternative picture of brain function is best captured in a novel explanatory framework called enabling constraint. Overall, these insights call for a reconceptualization of functional brain activity, and should drive future methodological and empirical efforts.
Barahona da Fonseca, José; Barahona da Fonseca, Isabel; Suarez Araujo, Carmen Paz; Simões da Fonseca, José
A reinterpretation of experimental data on learning was used to formulate a law on data acquisition similar to the Hamiltonian of a mechanical system. A matrix of costs in decision making specifies values attributable to a barrier that opposed to hypothesis formation about decision making. The interpretation of the encoding costs as frequencies of oscillatory phenomena leads to a quantum paradigm based in the models of photoelectric effect as well as of a particle against a potential barrier. Cognitive processes are envisaged as complex phenomena represented by structures linked by valence bounds. This metaphor is used to find some prerequisites to certain types of conscious experience as well as to find an explanation for some pathological distortions of cognitive operations as they are represented in the context of the isolobal model. Those quantum phenomena are understood as representing an analogue programming for specific special purpose computations. The formation of complex chemical structures within the context of isolobal theory is understood as an analog quantum paradigm for complex cognitive computations.
Weber, F; Eichner, H; Borst, A; Cuntz, H
Flies gain information about self-motion during free flight by processing images of the environment moving across their retina. The visual course control center in the brain of the blowfly contains, among others, a population of ten neurons, the so-called vertical system (VS) cells that are mainly sensitive to downward motion. VS cells are assumed to encode information about rotational optic flow induced by self-motion (Krapp and Hengstenberg 1996 Nature 384 463-6). Recent evidence supports a connectivity scheme between the VS cells where neurons with neighboring receptive fields are connected to each other by electrical synapses at the axonal terminals, whereas the boundary neurons in the network are reciprocally coupled via inhibitory synapses (Haag and Borst 2004 Nat. Neurosci. 7 628-34; Farrow et al 2005 J. Neurosci. 25 3985-93; Cuntz et al 2007 Proc. Natl Acad. Sci. USA). Here, we investigate the functional properties of the VS network and its connectivity scheme by reducing a biophysically realistic network to a simplified model, where each cell is represented by a dendritic and axonal compartment only. Eigenanalysis of this model reveals that the whole population of VS cells projects the synaptic input provided from local motion detectors on to its behaviorally relevant components. The two major eigenvectors consist of a horizontal and a slanted line representing the distribution of vertical motion components across the fly's azimuth. They are, thus, ideally suited for reliably encoding translational and rotational whole-field optic flow induced by respective flight maneuvers. The dimensionality reduction compensates for the contrast and texture dependence of the local motion detectors of the correlation-type, which becomes particularly pronounced when confronted with natural images and their highly inhomogeneous contrast distribution
Barraza, Paulo; Chavez, Mario; Rodríguez, Eugenio
Similar to linguistic stimuli, music can also prime the meaning of a subsequent word. However, it is so far unknown what is the brain dynamics underlying the semantic priming effect induced by music, and its relation to language. To elucidate these issues, we compare the brain oscillatory response to visual words that have been semantically primed either by a musical excerpt or by an auditory sentence. We found that semantic violation between music-word pairs triggers a classical ERP N400, and induces a sustained increase of long-distance theta phase synchrony, along with a transient increase of local gamma activity. Similar results were observed after linguistic semantic violation except for gamma activity, which increased after semantic congruence between sentence-word pairs. Our findings indicate that local gamma activity is a neural marker that signals different ways of semantic processing between music and language, revealing the dynamic and self-organized nature of the semantic processing. Copyright © 2015 Elsevier Inc. All rights reserved.
Decker, Arthur J.
A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation
Li, Xiaoqing; Zhang, Yuping; Xia, Jinyan; Swaab, Tamara Y
Although numerous studies have demonstrated that the language processing system can predict upcoming content during comprehension, there is still no clear picture of the anticipatory stage of predictive processing. This electroencephalograph study examined the cognitive and neural oscillatory mechanisms underlying anticipatory processing during language comprehension, and the consequences of this prediction for bottom-up processing of predicted/unpredicted content. Participants read Mandarin Chinese sentences that were either strongly or weakly constraining and that contained critical nouns that were congruent or incongruent with the sentence contexts. We examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. The results revealed that, at the integration stage, the strong-constraint condition (compared to the weak-constraint condition) elicited a reduced N400 and reduced theta activity (4-7Hz) for the congruent nouns, but induced beta (13-18Hz) and theta (4-7Hz) power decreases for the incongruent nouns, indicating benefits of confirmed predictions and potential costs of disconfirmed predictions. More importantly, at the anticipatory stage, the strongly constraining context elicited an enhanced sustained anterior negativity and beta power decrease (19-25Hz), which indicates that strong prediction places a higher processing load on the anticipatory stage of processing. The differences (in the ease of processing and the underlying neural oscillatory activities) between anticipatory and integration stages of lexical processing were discussed with regard to predictive processing models. Copyright © 2017 Elsevier Ltd. All rights reserved.
Yu, Lianchun; Yu, Yuguo
Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Full Text Available The possibilities of application informational-computer technologies for automated handling of a measuring information about development of seaweed (evtrofication in household reservoirs are considered. The input data’s for a research of processes evtrofication are videoimages of tests of water, which are used for the definition of geometric characteristics, number and biomass of seaweed. For handling a measuring information the methods of digital handling videoimages and mathematical means of artificial neural networks are offered.
Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela
Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763
Bogert, Brigitte; Numminen-Kontti, Taru; Gold, Benjamin; Sams, Mikko; Numminen, Jussi; Burunat, Iballa; Lampinen, Jouko; Brattico, Elvira
Music is often used to regulate emotions and mood. Typically, music conveys and induces emotions even when one does not attend to them. Studies on the neural substrates of musical emotions have, however, only examined brain activity when subjects have focused on the emotional content of the music. Here we address with functional magnetic resonance imaging (fMRI) the neural processing of happy, sad, and fearful music with a paradigm in which 56 subjects were instructed to either classify the emotions (explicit condition) or pay attention to the number of instruments playing (implicit condition) in 4-s music clips. In the implicit vs. explicit condition, stimuli activated bilaterally the inferior parietal lobule, premotor cortex, caudate, and ventromedial frontal areas. The cortical dorsomedial prefrontal and occipital areas activated during explicit processing were those previously shown to be associated with the cognitive processing of music and emotion recognition and regulation. Moreover, happiness in music was associated with activity in the bilateral auditory cortex, left parahippocampal gyrus, and supplementary motor area, whereas the negative emotions of sadness and fear corresponded with activation of the left anterior cingulate and middle frontal gyrus and down-regulation of the orbitofrontal cortex. Our study demonstrates for the first time in healthy subjects the neural underpinnings of the implicit processing of brief musical emotions, particularly in frontoparietal, dorsolateral prefrontal, and striatal areas of the brain. Copyright © 2016 Elsevier Ltd. All rights reserved.
Hemmen, J; Schulten, Klaus
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...
Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.
A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.
Seymour, Ben; O'Doherty, John P; Koltzenburg, Martin; Wiech, Katja; Frackowiak, Richard; Friston, Karl; Dolan, Raymond
Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.
Germine, Laura T; Garrido, Lucia; Bruce, Lori; Hooker, Christine
Human beings are social organisms with an intrinsic desire to seek and participate in social interactions. Social anhedonia is a personality trait characterized by a reduced desire for social affiliation and reduced pleasure derived from interpersonal interactions. Abnormally high levels of social anhedonia prospectively predict the development of schizophrenia and contribute to poorer outcomes for schizophrenia patients. Despite the strong association between social anhedonia and schizophrenia, the neural mechanisms that underlie individual differences in social anhedonia have not been studied and are thus poorly understood. Deficits in face emotion recognition are related to poorer social outcomes in schizophrenia, and it has been suggested that face emotion recognition deficits may be a behavioral marker for schizophrenia liability. In the current study, we used functional magnetic resonance imaging (fMRI) to see whether there are differences in the brain networks underlying basic face emotion processing in a community sample of individuals low vs. high in social anhedonia. We isolated the neural mechanisms related to face emotion processing by comparing face emotion discrimination with four other baseline conditions (identity discrimination of emotional faces, identity discrimination of neutral faces, object discrimination, and pattern discrimination). Results showed a group (high/low social anhedonia) × condition (emotion discrimination/control condition) interaction in the anterior portion of the rostral medial prefrontal cortex, right superior temporal gyrus, and left somatosensory cortex. As predicted, high (relative to low) social anhedonia participants showed less neural activity in face emotion processing regions during emotion discrimination as compared to each control condition. The findings suggest that social anhedonia is associated with abnormalities in networks responsible for basic processes associated with social cognition, and provide a
Nielsen, Andreas Brinch; Hansen, Lars Kai
as a preprocessing step to select and synchronize recordings before further processing. The two methods perform similarly in classification, but fingerprinting scales better with the number of recordings, while cross-correlation can offer sample resolution synchronization. We propose and investigate the benefits...... of combining the two. In particular we show that the combination allows sample resolution synchronization and scalability....
Chen Shaoqi; Zhang Yanzhen; Xiao Zhuangwei; Zhang Xuexin
Objective: To address the neural basis of Chinese idioms processing with different kinds of stimuli using an event-related fMRI design. Methods: Sixteen native Chinese speakers were asked to perform a semantic decision task during fMRI scanning. Three kinds of stimuli were used: Real idioms (Real-idiom condition); Literally plausible phrases (Pseudo-idiom condition, the last character of a real idiom was replaced by a character with similar meaning); Literally implausible strings (Non-idiom condition, the last character of a real idiom was replaced by a character with unrelated meaning). Reaction time and correct rate were recorded at the same time. Results: The error rate was 2.6%, 5.2% and 0.9% (F=3.51, P 0.05) for real idioms, pseudo-idioms and wrong idioms, respectively. Similar neural network was activated in all of the three conditions. However, the right hippocampus was only activated in the real idiom condition, and significant activations were found in anterior portion of left inferior frontal gyms (BA47) in real-and pseudo-idiom conditions, but not in non-idiom condition. Conclusion: The right hippocampus plays a specific role in the particular wording of the Chinese idioms. And the left anterior inferior frontal gyms (BA47) may be engaged in the semantic processing of Chinese idioms. The results support the notion that there were specific neural bases for Chinese idioms processing. (authors)
Balaban, Halely; Luria, Roy
Visual working memory (VWM) guides behavior by holding a set of active representations and modifying them according to changes in the environment. This updating process relies on a unique mapping between each VWM representation and an actual object in the environment. Here, we destroyed this mapping by either presenting a coherent object but then breaking it into independent parts or presenting an object but then abruptly replacing it with a different object. This allowed us to introduce the neural marker and behavioral consequence of an online resetting process in humans' VWM. Across seven experiments, we demonstrate that this resetting process involves abandoning the old VWM contents because they no longer correspond to the objects in the environment. Then, VWM encodes the novel information and reestablishes the correspondence between the new representations and the objects. The resetting process was marked by a unique neural signature: a sharp drop in the amplitude of the electrophysiological index of VWM contents (the contralateral delay activity), presumably indicating the loss of the existent object-to-representation mappings. This marker was missing when an updating process occurred. Moreover, when tracking moving items, VWM failed to detect salient changes in the object's shape when these changes occurred during the resetting process. This happened despite the object being fully visible, presumably because the mapping between the object and a VWM representation was lost. Importantly, we show that resetting, its neural marker, and the behavioral cost it entails, are specific to situations that involve a destruction of the objects-to-representations correspondence. Visual working memory (VWM) maintains task-relevant information in an online state. Previous studies showed that VWM representations are accessed and modified after changes in the environment. Here, we show that this updating process critically depends on an ongoing mapping between the
Reem S.W. Alyahya
Full Text Available The behavioural and neural processes underpinning different word classes, particularly nouns and verbs, have been a long-standing area of interest in psycholinguistic, neuropsychology and aphasiology research. This topic has theoretical implications concerning the organisation of the language system, as well as clinical consequences related to the management of patients with language deficits. Research findings, however, have diverged widely, which might, in part, reflect methodological differences, particularly related to controlling the psycholinguistic variations between nouns and verbs. The first aim of this study, therefore, was to develop a set of neuropsychological tests that assessed single-word production and comprehension with a matched set of nouns and verbs. Secondly, the behavioural profiles and neural correlates of noun and verb processing were explored, based on these novel tests, in a relatively large cohort of 48 patients with chronic post-stroke aphasia. A data-driven approach, principal component analysis (PCA, was also used to determine how noun and verb production and comprehension were related to the patients' underlying fundamental language domains. The results revealed no performance differences between noun and verb production and comprehension once matched on multiple psycholinguistic features including, most critically, imageability. Interestingly, the noun-verb differences found in previous studies were replicated in this study once un-matched materials were used. Lesion-symptom mapping revealed overlapping neural correlates of noun and verb processing along left temporal and parietal regions. These findings support the view that the neural representation of noun and verb processing at single-word level are jointly-supported by distributed cortical regions. The PCA generated five fundamental language and cognitive components of aphasia: phonological production, phonological recognition, semantics, fluency, and
Full Text Available Hostile jokes provide aggressive catharsis and a feeling of superiority. Behavioral research has found that hostile jokes are perceived as funnier than non-hostile jokes. The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing hostile jokes (HJs, non-hostile jokes (NJs, and their corresponding hostile sentences (HSs and non-hostile sentences (NSs. Hostile jokes primarily showed activation in the dorsomedial prefrontal cortex (dmPFC and midbrain compared with the corresponding hostile baseline. Conversely, non-hostile jokes primarily revealed activation in the ventromedial PFC (vmPFC, amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc compared with the corresponding non-hostile baseline. These results support the critical role of the medial prefrontal cortex (mPFC for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of hostile jokes showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of non-hostile jokes displayed increased activation in the vmPFC, which suggested social-affective engagement. Hostile jokes versus non-hostile jokes primarily showed increased activation in the dmPFC and midbrain, whereas non-hostile jokes versus hostile jokes primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI analysis demonstrated functional coupling of the dmPFC-dlPFC and midbrain-dmPFC for hostile jokes and functional coupling of the vmPFC-midbrain and amygdala-midbrain-NAcc for non-hostile jokes. Surprisingly, the neural correlates of hostile jokes were not perceived as funnier than non-hostile jokes. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation
Zhao, T Christina; Kuhl, Patricia K
Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants' neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants' neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants' neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants' ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing.
Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming
The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.
Kuuluvainen, Soila; Nevalainen, Päivi; Sorokin, Alexander; Mittag, Maria; Partanen, Eino; Putkinen, Vesa; Seppänen, Miia; Kähkönen, Seppo; Kujala, Teija
We addressed the neural organization of speech versus nonspeech sound processing by investigating preattentive cortical auditory processing of changes in five features of a consonant-vowel syllable (consonant, vowel, sound duration, frequency, and intensity) and their acoustically matched nonspeech counterparts in a simultaneous EEG-MEG recording of mismatch negativity (MMN/MMNm). Overall, speech-sound processing was enhanced compared to nonspeech sound processing. This effect was strongest for changes which affect word meaning (consonant, vowel, and vowel duration) in the left and for the vowel identity change in the right hemisphere also. Furthermore, in the right hemisphere, speech-sound frequency and intensity changes were processed faster than their nonspeech counterparts, and there was a trend for speech-enhancement in frequency processing. In summary, the results support the proposed existence of long-term memory traces for speech sounds in the auditory cortices, and indicate at least partly distinct neural substrates for speech and nonspeech sound processing. Copyright © 2014 Elsevier Inc. All rights reserved.
Full Text Available Foreign-accented speech often presents a challenging listening condition. In addition to deviations from the target speech norms related to the inexperience of the nonnative speaker, listener characteristics may play a role in determining intelligibility levels. We have previously shown that an implicit visual bias for associating East Asian faces and foreignness predicts the listeners’ perceptual ability to process Korean-accented English audiovisual speech (Yi et al., 2013. Here, we examine the neural mechanism underlying the influence of listener bias to foreign faces on speech perception. In a functional magnetic resonance imaging (fMRI study, native English speakers listened to native- and Korean-accented English sentences, with or without faces. The participants’ Asian-foreign association was measured using an implicit association test (IAT, conducted outside the scanner. We found that foreign-accented speech evoked greater activity in the bilateral primary auditory cortices and the inferior frontal gyri, potentially reflecting greater computational demand. Higher IAT scores, indicating greater bias, were associated with increased BOLD response to foreign-accented speech with faces in the primary auditory cortex, the early node for spectrotemporal analysis. We conclude the following: (1 foreign-accented speech perception places greater demand on the neural systems underlying speech perception; (2 face of the talker can exaggerate the perceived foreignness of foreign-accented speech; (3 implicit Asian-foreign association is associated with decreased neural efficiency in early spectrotemporal processing.
Full Text Available The present article develops a decentralized interbase station slot synchronization algorithm suitable for cellular mobile communication systems. The proposed cellular firefly synchronization (CelFSync algorithm is derived from the theory of pulse-coupled oscillators, common to describe synchronization phenomena in biological systems, such as the spontaneous synchronization of fireflies. In order to maintain synchronization among base stations (BSs, even when there is no direct link between adjacent BSs, some selected user terminals (UTs participate in the network synchronization process. Synchronization emerges by exchanging two distinct synchronization words, one transmitted by BSs and the other by active UTs, without any a priori assumption on the initial timing misalignments of BSs and UTs. In large-scale networks with inter-BS site distances up to a few kilometers, propagation delays severely affect the attainable timing accuracy of CelFSync. We show that by an appropriate combination of CelFSync with the timing advance procedure, which aligns uplink transmission of UTs to arrive simultaneously at the BS, a timing accuracy within a fraction of the inter-BS propagation delay is retained.
Giese, Martin A; Rizzolatti, Giacomo
Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits. Copyright © 2015 Elsevier Inc. All rights reserved.
Yuwen Sun; Shimeng Huang; Oresko, Joseph J; Cheng, Allen C
Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.
Chandra, A.K.; Rao, V.S.
The simplest prototypical hydrogen transfer reaction, i.e., H + H 2 → H 2 + H, is studied by the quantum-mechanical ab initio methods. Results reveal that during this reaction free valence which almost equals the square of the spin density develops on the migrating hydrogen atom. Bond orders are calculated using Mayer's formalism. Both the variations of bond orders and bond lengths along the reaction path are examined. This analysis reveals that the bond formation and bond cleavage processes in this reaction are not perfectly synchronous. The bond clevage process is slightly more advanced on the reaction path. 38 refs., 6 figs., 2 tabs
Obert, Alexandre; Gierski, Fabien; Calmus, Arnaud; Flucher, Aurélie; Portefaix, Christophe; Pierot, Laurent; Kaladjian, Arthur; Caillies, Stéphanie
Irony is a kind of figurative language used by a speaker to say something that contrasts with the context and, to some extent, lends humor to a situation. However, little is known about the brain regions that specifically support the processing of these two common features of irony. The present study had two main aims: (i) investigate the neural basis of irony processing, by delivering short ironic spoken sentences (and their literal counterparts) to participants undergoing fMRI; and (ii) assess the neural effect of two irony parameters, obtained from normative studies: degree of contrast and humor appreciation. Results revealed activation of the bilateral inferior frontal gyrus (IFG), posterior part of the left superior temporal gyrus, medial frontal cortex, and left caudate during irony processing, suggesting the involvement of both semantic and theory-of-mind networks. Parametric models showed that contrast was specifically associated with the activation of bilateral frontal and subcortical areas, and that these regions were also sensitive to humor, as shown by a conjunction analysis. Activation of the bilateral IFG is consistent with the literature on humor processing, and reflects incongruity detection/resolution processes. Moreover, the activation of subcortical structures can be related to the reward processing of social events. PMID:27851821
Jokić Aleksandar I.
Full Text Available In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.
Diler, Rasim Somer; Ladouceur, Cecile D; Segreti, Annamaria; Almeida, Jorge R C; Birmaher, Boris; Axelson, David A; Phillips, Mary L; Pan, Lisa A
Depressive mood in adolescents with bipolar disorder (BDd) is associated with significant morbidity and mortality, but we have limited information about neural correlates of depression and treatment response in BDd. Ten adolescents with BDd (8 females, mean age = 15.6 ± 0.9) completed two (fearful and happy) face gender labeling fMRI experiments at baseline and after 6-weeks of open treatment. Whole-brain analysis was used at baseline to compare their neural activity with those of 10 age and sex-matched healthy controls (HC). For comparisons of the neural activity at baseline and after treatment of youth with BDd, region of interest analysis for dorsal/ventral prefrontal, anterior cingulate, and amygdala activity, and significant regions identified by wholebrain analysis between BDd and HC were analyzed. There was significant improvement in depression scores (mean percentage change on the Child Depression Rating Scale-Revised 57 % ± 28). Neural activity after treatment was decreased in left occipital cortex in the intense fearful experiment, but increased in left insula, left cerebellum, and right ventrolateral prefrontal cortex in the intense happy experiment. Greater improvement in depression was associated with baseline higher activity in ventral ACC to mild happy faces. Study sample size was relatively small for subgroup analysis and consisted of mainly female adolescents that were predominantly on psychotropic medications during scanning. Our results of reduced negative emotion processing versus increased positive emotion processing after treatment of depression (improvement of cognitive bias to negative and away from positive) are consistent with the improvement of depression according to Beck's cognitive theory.
Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.
Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic
Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.
Miskowiak, Kamilla W; Favaron, Elisa; Hafizi, Sepehr
Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The curren...... study investigates the effects of Epo on the neural and cognitive response to emotional facial expressions in depressed patients.......Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The current...
Ji, Ellen; Weickert, Cynthia Shannon; Lenroot, Rhoshel; Catts, Stanley V; Vercammen, Ans; White, Christopher; Gur, Raquel E; Weickert, Thomas W
Growing evidence suggests that testosterone may play a role in the pathophysiology of schizophrenia given that testosterone has been linked to cognition and negative symptoms in schizophrenia. Here, we determine the extent to which serum testosterone levels are related to neural activity in affective processing circuitry in men with schizophrenia. Functional magnetic resonance imaging was used to measure blood-oxygen-level-dependent signal changes as 32 healthy controls and 26 people with schizophrenia performed a facial emotion identification task. Whole brain analyses were performed to determine regions of differential activity between groups during processing of angry versus non-threatening faces. A follow-up ROI analysis using a regression model in a subset of 16 healthy men and 16 men with schizophrenia was used to determine the extent to which serum testosterone levels were related to neural activity. Healthy controls displayed significantly greater activation than people with schizophrenia in the left inferior frontal gyrus (IFG). There was no significant difference in circulating testosterone levels between healthy men and men with schizophrenia. Regression analyses between activation in the IFG and circulating testosterone levels revealed a significant positive correlation in men with schizophrenia (r=.63, p=.01) and no significant relationship in healthy men. This study provides the first evidence that circulating serum testosterone levels are related to IFG activation during emotion face processing in men with schizophrenia but not in healthy men, which suggests that testosterone levels modulate neural processes relevant to facial emotion processing that may interfere with social functioning in men with schizophrenia. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.
Hamamé, Carlos M; Cosmelli, Diego; Henriquez, Rodrigo; Aboitiz, Francisco
Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing. We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.
Xu, Min; Xu, Guiping; Yang, Yang
Understanding how the nature of interference might influence the recruitments of the neural systems is considered as the key to understanding cognitive control. Although, interference processing in the emotional domain has recently attracted great interest, the question of whether there are separable neural patterns for emotional and non-emotional interference processing remains open. Here, we performed an activation likelihood estimation meta-analysis of 78 neuroimaging experiments, and exam...
Sergey Victor Fogelson
Full Text Available Visual stimuli can be kept from awareness using various methods. The extent of processing that a given stimulus receives in the absence of awareness is typically used to make claims about the role of consciousness more generally. The neural processing elicited by a stimulus, however, may also depend on the method used to keep it from awareness, and not only on whether the stimulus reaches awareness. Here we report that the method used to render an image invisible has a dramatic effect on how category information about the unseen stimulus is encoded across the human brain. We collected fMRI data while subjects viewed images of faces and tools, that were rendered invisible using either continuous flash suppression (CFS or chromatic flicker fusion (CFF. In a third condition, we presented the same images under normal fully visible viewing conditions. We found that category information about visible images could be extracted from patterns of fMRI responses throughout areas of neocortex known to be involved in face or tool processing. However, category information about stimuli kept from awareness using CFS could be recovered exclusively within occipital cortex, whereas information about stimuli kept from awareness using CFF was also decodable within temporal and frontal regions. We conclude that unconsciously presented objects are processed differently depending on how they are rendered subjectively invisible. Caution should therefore be used in making generalizations on the basis of any one method about the neural basis of consciousness or the extent of information processing without consciousness.
Fogelson, Sergey V; Kohler, Peter J; Miller, Kevin J; Granger, Richard; Tse, Peter U
Visual stimuli can be kept from awareness using various methods. The extent of processing that a given stimulus receives in the absence of awareness is typically used to make claims about the role of consciousness more generally. The neural processing elicited by a stimulus, however, may also depend on the method used to keep it from awareness, and not only on whether the stimulus reaches awareness. Here we report that the method used to render an image invisible has a dramatic effect on how category information about the unseen stimulus is encoded across the human brain. We collected fMRI data while subjects viewed images of faces and tools, that were rendered invisible using either continuous flash suppression (CFS) or chromatic flicker fusion (CFF). In a third condition, we presented the same images under normal fully visible viewing conditions. We found that category information about visible images could be extracted from patterns of fMRI responses throughout areas of neocortex known to be involved in face or tool processing. However, category information about stimuli kept from awareness using CFS could be recovered exclusively within occipital cortex, whereas information about stimuli kept from awareness using CFF was also decodable within temporal and frontal regions. We conclude that unconsciously presented objects are processed differently depending on how they are rendered subjectively invisible. Caution should therefore be used in making generalizations on the basis of any one method about the neural basis of consciousness or the extent of information processing without consciousness.
Full Text Available Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN is proposed. The input of SAE-DPNN is time-varying process signal and the output is pattern category. It combines the time-varying signal classification method of process neural network (PNN and the data feature extraction and hierarchical sparse representation mechanism of sparse automatic encoder (SAE. Based on the feedforward PNN model, SAE-DPNN is constructed by stacking the process neurons, SAE network and softmax classifier. It can maintain the time-sequence and structure of the input signal, express and synthesize the process distribution characteristics of multidimensional time-varying signals and their combinations. SAE-DPNN improves the identification of complex features and distinguishes between different types of signals, realizes the direct classification of time-varying signals. In this paper, the feature extraction and representation mechanism of time-varying signal in SAE-DPNN are analyzed, and a specific learning algorithm is given. The experimental results verify the effectiveness of the model and algorithm.
Justin B Knight
Full Text Available Prospective memory, or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT, followed by a LDT with an embedded prospective memory (PM component. Event-based cues were constituted by color and lexicality (red words. Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.
Chad Edward Forbes
Full Text Available The Implicit Association Test (IAT is a popular behavioral measure that assesses the associative strength between outgroup members and stereotypical and counterstereotypical traits. Less is known, however, about the degree to which the IAT reflects automatic processing. Two studies examined automatic processing contributions to a gender-IAT using a data driven, social neuroscience approach. Performance on congruent (e.g., categorizing male names with synonyms of strength and incongruent (e.g., categorizing female names with synonyms of strength IAT blocks were separately analyzed using EEG (event-related potentials, or ERPs, and coherence; Study 1 and lesion (Study 2 methodologies. Compared to incongruent blocks, performance on congruent IAT blocks was associated with more positive ERPs that manifested in frontal and occipital regions at automatic processing speeds, occipital regions at more controlled processing speeds and was compromised by volume loss in the anterior temporal lobe, insula and medial PFC. Performance on incongruent blocks was associated with volume loss in supplementary motor areas, cingulate gyrus and a region in medial PFC similar to that found for congruent blocks. Greater coherence was found between frontal and occipital regions to the extent individuals exhibited more bias. This suggests there are separable neural contributions to congruent and incongruent blocks of the IAT but there is also a surprising amount of overlap. Given the temporal and regional neural distinctions, these results provide converging evidence that stereotypic associative strength assessed by the IAT indexes automatic processing to a degree.
Full Text Available Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.
Knight, Justin B; Ethridge, Lauren E; Marsh, Richard L; Clementz, Brett A
Prospective memory (PM), or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT), followed by a LDT with an embedded PM component. Event-based cues were constituted by color and lexicality (red words). Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP) revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.
Frässle, Stefan; Krach, Sören; Paulus, Frieder Michel; Jansen, Andreas
While the right-hemispheric lateralization of the face perception network is well established, recent evidence suggests that handedness affects the cerebral lateralization of face processing at the hierarchical level of the fusiform face area (FFA). However, the neural mechanisms underlying differential hemispheric lateralization of face perception in right- and left-handers are largely unknown. Using dynamic causal modeling (DCM) for fMRI, we aimed to unravel the putative processes that mediate handedness-related differences by investigating the effective connectivity in the bilateral core face perception network. Our results reveal an enhanced recruitment of the left FFA in left-handers compared to right-handers, as evidenced by more pronounced face-specific modulatory influences on both intra- and interhemispheric connections. As structural and physiological correlates of handedness-related differences in face processing, right- and left-handers varied with regard to their gray matter volume in the left fusiform gyrus and their pupil responses to face stimuli. Overall, these results describe how handedness is related to the lateralization of the core face perception network, and point to different neural mechanisms underlying face processing in right- and left-handers. In a wider context, this demonstrates the entanglement of structurally and functionally remote brain networks, suggesting a broader underlying process regulating brain lateralization.
Caspers, Svenja; Heim, Stefan; Lucas, Marc G; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl
Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible
Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido
Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.
Rashmi L. Malghan
Full Text Available The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based approach, i.e. ANN based on three process parameters, such as spindle speed, feed rate and depth of cut.The comparing reverse model is likewise created to prescribe the ideal settings of processing parameters for accomplishing the desired responses as indicated by the necessities of the end clients. These modelling approaches are very proficient to foresee the benefits of machining responses and also process parameter settings in light of the experimental technique. Keywords: ANN, Forward mapping, Reverse mapping, Milling process
Zhou, Zhiyi; Bernard, Melanie R; Bonds, A B
Spatiotemporal relationships among contour segments can influence synchronization of neural responses in the primary visual cortex. We performed a systematic study to dissociate the impact of spatial and temporal factors in the signaling of contour integration via synchrony. In addition, we characterized the temporal evolution of this process to clarify potential underlying mechanisms. With a 10 x 10 microelectrode array, we recorded the simultaneous activity of multiple cells in the cat primary visual cortex while stimulating with drifting sine-wave gratings. We preserved temporal integrity and systematically degraded spatial integrity of the sine-wave gratings by adding spatial noise. Neural synchronization was analyzed in the time and frequency domains by conducting cross-correlation and coherence analyses. The general association between neural spike trains depends strongly on spatial integrity, with coherence in the gamma band (35-70 Hz) showing greater sensitivity to the change of spatial structure than other frequency bands. Analysis of the temporal dynamics of synchronization in both time and frequency domains suggests that spike timing synchronization is triggered nearly instantaneously by coherent structure in the stimuli, whereas frequency-specific oscillatory components develop more slowly, presumably through network interactions. Our results suggest that, whereas temporal integrity is required for the generation of synchrony, spatial integrity is critical in triggering subsequent gamma band synchronization.
Timm, Lydia; Vuust, Peter; Brattico, Elvira; Agrawal, Deepashri; Debener, Stefan; Büchner, Andreas; Dengler, Reinhard; Wittfoth, Matthias
Auditory processing in general and music perception in particular are hampered in adult cochlear implant (CI) users. To examine the residual music perception skills and their underlying neural correlates in CI users implanted in adolescence or adulthood, we conducted an electrophysiological and behavioral study comparing adult CI users with normal-hearing age-matched controls (NH controls). We used a newly developed musical multi-feature paradigm, which makes it possible to test automatic auditory discrimination of six different types of sound feature changes inserted within a musical enriched setting lasting only 20 min. The presentation of stimuli did not require the participants’ attention, allowing the study of the early automatic stage of feature processing in the auditory cortex. For the CI users, we obtained mismatch negativity (MMN) brain responses to five feature changes but not to changes of rhythm, whereas we obtained MMNs for all the feature changes in the NH controls. Furthermore, the MMNs to deviants of pitch of CI users were reduced in amplitude and later than those of NH controls for changes of pitch and guitar timber. No other group differences in MMN parameters were found to changes in intensity and saxophone timber. Furthermore, the MMNs in CI users reflected the behavioral scores from a respective discrimination task and were correlated with patients’ age and speech intelligibility. Our results suggest that even though CI users are not performing at the same level as NH controls in neural discrimination of pitch-based features, they do possess potential neural abilities for music processing. However, CI users showed a disrupted ability to automatically discriminate rhythmic changes compared with controls. The current behavioral and MMN findings highlight the residual neural skills for music processing even in CI users who have been implanted in adolescence or adulthood. Highlights: -Automatic brain responses to musical feature changes
Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P.
According to embodied cognition theories concepts are contextually-situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and non-emotional (e.g., thinking, hunger) mental states with internal focus (i.e. focusing on bodily sensations and introspection) or external focus (i.e. focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources. PMID:25748274
Oyedotun, Oyebade K; Khashman, Adnan
Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.
Full Text Available Punishing norm violations is considered an important motive during rejection of unfair offers in the Ultimatum Game (UG. The present study investigates the impact of the power to punish norm violations on people’s responses to unfairness and associated neural correlates. In the UG condition participants had the power to punish norm violations, while an alternate condition, the Impunity Game (IG, was presented where participants had no power to punish norm violations since rejection only reduced the responder’s income to zero. Results showed that unfair offers were rejected more often in UG compared to IG. At the neural level, anterior insula and dorsal anterior cingulate cortex were more active when participants received and rejected unfair offers in both UG and IG. Moreover, greater dorsolateral prefrontal cortex activity was observed when participants rejected than accepted unfair offers in UG but not in IG. Ventromedial prefrontal cortex activation was higher in UG than IG when unfair offers were accepted as well as when rejecting unfair offers in IG as opposed to UG. Taken together, our results demonstrate that the power to punish norm violations affects not only people’s behavioral responses to unfairness but also the neural correlates of the fairness-related social decision-making process.
Ku, Yixuan; Hong, Bo; Zhou, Wenjing; Bodner, Mark; Zhou, Yong-Di
Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes) swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms) after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms). Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7) and bilateral middle frontal gyri (BA 6). These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.
Yu, Wenwu; Cao, Jinde
Parameter identification of dynamical systems from time series has received increasing interest due to its wide applications in secure communication, pattern recognition, neural networks, and so on. Given the driving system, parameters can be estimated from the time series by using an adaptive control algorithm. Recently, it has been reported that for some stable systems, in which parameters are difficult to be identified [Li et al., Phys Lett. A 333, 269-270 (2004); Remark 5 in Yu and Cao, Physica A 375, 467-482 (2007); and Li et al., Chaos 17, 038101 (2007)], and in this paper, a brief discussion about whether parameters can be identified from time series is investigated. From some detailed analyses, the problem of why parameters of stable systems can be hardly estimated is discussed. Some interesting examples are drawn to verify the proposed analysis.
Tseng, Angela; Wang, Zhishun; Huo, Yuankai; Goh, Suzanne; Russell, James A; Peterson, Bradley S
Individuals with autism spectrum disorders (ASD) often have difficulty recognizing and interpreting facial expressions of emotion, which may impair their ability to navigate and communicate successfully in their social, interpersonal environments. Characterizing specific differences between individuals with ASD and their typically developing (TD) counterparts in the neural activity subserving their experience of emotional faces may provide distinct targets for ASD interventions. Thus we used functional magnetic resonance imaging (fMRI) and a parametric experimental design to identify brain regions in which neural activity correlated with ratings of arousal and valence for a broad range of emotional faces. Participants (51 ASD, 84 TD) were group-matched by age, sex, IQ, race, and socioeconomic status. Using task-related change in blood-oxygen-level-dependent (BOLD) fMRI signal as a measure, and covarying for age, sex, FSIQ, and ADOS scores, we detected significant differences across diagnostic groups in the neural activity subserving the dimension of arousal but not valence. BOLD-signal in TD participants correlated inversely with ratings of arousal in regions associated primarily with attentional functions, whereas BOLD-signal in ASD participants correlated positively with arousal ratings in regions commonly associated with impulse control and default-mode activity. Only minor differences were detected between groups in the BOLD signal correlates of valence ratings. Our findings provide unique insight into the emotional experiences of individuals with ASD. Although behavioral responses to face-stimuli were comparable across diagnostic groups, the corresponding neural activity for our ASD and TD groups differed dramatically. The near absence of group differences for valence correlates and the presence of strong group differences for arousal correlates suggest that individuals with ASD are not atypical in all aspects of emotion-processing. Studying these similarities
Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng
The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.
Full Text Available Methods of process control of eutrophication in water are based on water sampling, handling them in the laboratory and calculation of indexes of pond ecosystem. However, these methods have some significant drawbacks associated with using manual labor. The method of determining of the geometric parameters of phytoplankton through the use of neural networks for processing water samples is developed. Due to this method eutrophic processes of reservoirs of river Teteriv are investigated. A comparative analysis of eutrophic processes of reservoirs "Denyshi" and “Vidsichne” intake during 2014-2015 years are given. The differences between qualitative and quantitative composition of phytoplankton algae in two reservoirs of the river Teteriv used for water supply of Zhitomir city area are found out. The influence of exogenous and endogenous factors on the expansion of phytoplankton is researched. Research results can be used for monitoring and forecasting of ecological state of water for household purposes, used for water supply of cities.
Synchronization is a common phenomenon in physical and biological systems. We examine the synchronization of two (and more) metronomes placed on a freely moving base. The small motion of the base couples the pendulums causing synchronization. The synchronization is generally in-phase, with antiphase synchronization occurring only under special conditions. The metronome system provides a mechanical realization of the popular Kuramoto model for synchronization of biological oscillators, and is excellent for classroom demonstrations and an undergraduate physics lab.
Chan, Yu-Chen; Liao, Yi-Jun; Tu, Cheng-Hao; Chen, Hsueh-Chih
Hostile jokes (HJs) provide aggressive catharsis and a feeling of superiority. Behavioral research has found that HJs are perceived as funnier than non-hostile jokes (NJs). The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing HJs, NJs, and their corresponding hostile sentences (HSs) and non-hostile sentences (NSs). HJs primarily showed activation in the dorsomedial prefrontal cortex (dmPFC) and midbrain compared with the corresponding hostile baseline. Conversely, NJs primarily revealed activation in the ventromedial PFC (vmPFC), amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc) compared with the corresponding non-hostile baseline. These results support the critical role of the medial PFC (mPFC) for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of HJs showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of NJs displayed increased activation in the vmPFC, which suggested social-affective engagement. HJs versus NJs primarily showed increased activation in the dmPFC and midbrain, whereas NJs versus HJs primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI) analysis demonstrated functional coupling of the dmPFC-dlPFC and midbrain-dmPFC for HJs and functional coupling of the vmPFC-midbrain and amygdala-midbrain-NAcc for NJs. Surprisingly, HJs were not perceived as funnier than NJs. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation based on the psychoanalytic and superiority theories of humor.
Casement, Melynda D; Keenan, Kate E; Hipwell, Alison E; Guyer, Amanda E; Forbes, Erika E
Emerging evidence suggests that insomnia may disrupt reward-related brain function-a potentially important factor in the development of depressive disorder. Adolescence may be a period during which such disruption is especially problematic given the rise in the incidence of insomnia and ongoing development of neural systems that support reward processing. The present study uses longitudinal data to test the hypothesis that disruption of neural reward processing is a mechanism by which insomnia symptoms-including nocturnal insomnia symptoms (NIS) and nonrestorative sleep (NRS)-contribute to depressive symptoms in adolescent girls. Participants were 123 adolescent girls and their caregivers from an ongoing longitudinal study of precursors to depression across adolescent development. NIS and NRS were assessed annually from ages 9 to 13 years. Girls completed a monetary reward task during a functional MRI scan at age 16 years. Depressive symptoms were assessed at ages 16 and 17 years. Multivariable regression tested the prospective associations between NIS and NRS, neural response during reward anticipation, and the mean number of depressive symptoms (omitting sleep problems). NRS, but not NIS, during early adolescence was positively associated with late adolescent dorsal medial prefrontal cortex (dmPFC) response to reward anticipation and depressive symptoms. DMPFC response mediated the relationship between early adolescent NRS and late adolescent depressive symptoms. These results suggest that NRS may contribute to depression by disrupting reward processing via altered activity in a region of prefrontal cortex involved in affective control. The results also support the mechanistic differentiation of NIS and NRS. © 2016 Associated Professional Sleep Societies, LLC.
Full Text Available Approximately 10% of young adults report non-medical use of stimulants (cocaine, amphetamine, methylphenidate, which puts them at risk for the development of dependence. This fMRI study investigates whether subjects at early stages of stimulant use show altered decision making processing.158 occasional stimulants users (OSU and 50 comparison subjects (CS performed a "risky gains" decision making task during which they could select safe options (cash in 20 cents or gamble them for double or nothing in two consecutive gambles (win or lose 40 or 80 cents, "risky decisions". The primary analysis focused on risky versus safe decisions. Three secondary analyses were conducted: First, a robust regression examined the effect of lifetime exposure to stimulants and marijuana; second, subgroups of OSU with >1000 (n = 42, or <50 lifetime marijuana uses (n = 32, were compared to CS with <50 lifetime uses (n = 46 to examine potential marijuana effects; third, brain activation associated with behavioral adjustment following monetary losses was probed.There were no behavioral differences between groups. OSU showed attenuated activation across risky and safe decisions in prefrontal cortex, insula, and dorsal striatum, exhibited lower anterior cingulate cortex (ACC and dorsal striatum activation for risky decisions and greater inferior frontal gyrus activation for safe decisions. Those OSU with relatively more stimulant use showed greater dorsal ACC and posterior insula attenuation. In comparison, greater lifetime marijuana use was associated with less neural differentiation between risky and safe decisions. OSU who chose more safe responses after losses exhibited similarities with CS relative to those preferring risky options.Individuals at risk for the development of stimulant use disorders presented less differentiated neural processing of risky and safe options. Specifically, OSU show attenuated brain response in regions critical for performance monitoring
Chan, Yu-Chen; Liao, Yi-Jun; Tu, Cheng-Hao
Hostile jokes (HJs) provide aggressive catharsis and a feeling of superiority. Behavioral research has found that HJs are perceived as funnier than non-hostile jokes (NJs). The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing HJs, NJs, and their corresponding hostile sentences (HSs) and non-hostile sentences (NSs). HJs primarily showed activation in the dorsomedial prefrontal cortex (dmPFC) and midbrain compared with the corresponding hostile baseline. Conversely, NJs primarily revealed activation in the ventromedial PFC (vmPFC), amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc) compared with the corresponding non-hostile baseline. These results support the critical role of the medial PFC (mPFC) for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of HJs showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of NJs displayed increased activation in the vmPFC, which suggested social-affective engagement. HJs versus NJs primarily showed increased activation in the dmPFC and midbrain, whereas NJs versus HJs primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI) analysis demonstrated functional coupling of the dmPFC–dlPFC and midbrain–dmPFC for HJs and functional coupling of the vmPFC–midbrain and amygdala–midbrain–NAcc for NJs. Surprisingly, HJs were not perceived as funnier than NJs. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation based on the psychoanalytic and superiority theories of humor. PMID:27840604
Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer
The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.
Caffrey, Martha K.; Nephew, Benjamin C.; Febo, Marcelo
Vasopressin V1a receptors in the rat brain have been studied for their role in modulating aggression and anxiety. In the current study blood-oxygen-level-dependent (BOLD) functional MRI was used to test whether V1a receptors modulate neural processing in the maternal brain when dams are exposed to a male intruder. Primiparous females were given an intracerebroventricular (ICV) injection of vehicle or V1a receptor antagonist ([deamino-Pen1, O-Me-Try, Arg8]-Vasopressin, 125 ng/10 μL) 90-120 min...
Denby, Bruce; Lindsey, Clark; Lyons, Louis
The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive
Xin, Yao; Li, Will X Y; Min, Biao; Han, Yan; Cheung, Ray C C
Stochastic State Point Process Filter (SSPPF) is effective for adaptive signal processing. In particular, it has been successfully applied to neural signal coding/decoding in recent years. Recent work has proven its efficiency in non-parametric coefficients tracking in modeling of mammal nervous system. However, existing SSPPF has only been realized in commercial software platforms which limit their computational capability. In this paper, the first hardware architecture of SSPPF has been designed and successfully implemented on field-programmable gate array (FPGA), proving a more efficient means for coefficient tracking in a well-established generalized Laguerre-Volterra model for mammalian hippocampal spiking activity research. By exploring the intrinsic parallelism of the FPGA, the proposed architecture is able to process matrices or vectors with random size, and is efficiently scalable. Experimental result shows its superior performance comparing to the software implementation, while maintaining the numerical precision. This architecture can also be potentially utilized in the future hippocampal cognitive neural prosthesis design.
Heitmann, Carina Y; Peterburs, Jutta; Mothes-Lasch, Martin; Hallfarth, Marlit C; Böhme, Stephanie; Miltner, Wolfgang H R; Straube, Thomas
Fear of negative evaluation, such as negative social performance feedback, is the core symptom of social anxiety. The present study investigated the neural correlates of anticipation and perception of social performance feedback in social anxiety. High (HSA) and low (LSA) socially anxious individuals were asked to give a speech on a personally relevant topic and received standardized but appropriate expert performance feedback in a succeeding experimental session in which neural activity was measured during anticipation and presentation of negative and positive performance feedback concerning the speech performance, or a neutral feedback-unrelated control condition. HSA compared to LSA subjects reported greater anxiety during anticipation of negative feedback. Functional magnetic resonance imaging results showed deactivation of medial prefrontal brain areas during anticipation of negative feedback relative to the control and the positive condition, and medial prefrontal and insular hyperactivation during presentation of negative as well as positive feedback in HSA compared to LSA subjects. The results indicate distinct processes underlying feedback processing during anticipation and presentation of feedback in HSA as compared to LSA individuals. In line with the role of the medial prefrontal cortex in self-referential information processing and the insula in interoception, social anxiety seems to be associated with lower self-monitoring during feedback anticipation, and an increased self-focus and interoception during feedback presentation, regardless of feedback valence. © 2014 Wiley Periodicals, Inc.
Seok, J-W; Park, M-S; Sohn, J-H
Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.
Dennis, Nancy A; Need, Anna C; LaBar, Kevin S; Waters-Metenier, Sheena; Cirulli, Elizabeth T; Kragel, James; Goldstein, David B; Cabeza, Roberto
The relationship between cognition and a functional polymorphism in the catechol-O-methlytransferase (COMT) gene, val108/158met, is one of debate in the literature. Furthermore, based on the dopaminergic differences associated with the COMT val108/158met genotype, neural differences during cognition may be present, regardless of genotypic differences in cognitive performance. To investigate these issues the current study aimed to 1) examine the effects of COMT genotype using a large sample of healthy individuals (n = 496-1218) and multiple cognitive measures, and using a subset of the sample (n = 22), 2) examine whether COMT genotype effects medial temporal lobe (MTL) and frontal activity during successful relational memory processing, and 3) investigate group differences in functional connectivity associated with successful relational memory processing. Results revealed no significant group difference in cognitive performance between COMT genotypes in any of the 19 cognitive measures. However, in the subset sample, COMT val homozygotes exhibited significantly decreased MTL and increased prefrontal activity during both successful relational encoding and retrieval, and reduced connectivity between these regions compared with met homozygotes. Taken together, the results suggest that although the COMT val108/158met genotype has no effect on cognitive behavioral measures in healthy individuals, it is associated with differences in neural process underlying cognitive output.
Itier, Roxane J; Neath-Tavares, Karly N
Task demands shape how we process environmental stimuli but their impact on the early neural processing of facial expressions remains unclear. In a within-subject design, ERPs were recorded to the same fearful, happy and neutral facial expressions presented during a gender discrimination, an explicit emotion discrimination and an oddball detection tasks, the most studied tasks in the field. Using an eye tracker, fixation on the face nose was enforced using a gaze-contingent presentation. Task demands modulated amplitudes from 200 to 350ms at occipito-temporal sites spanning the EPN component. Amplitudes were more negative for fearful than neutral expressions starting on N170 from 150 to 350ms, with a temporo-occipital distribution, whereas no clear effect of happy expressions was seen. Task and emotion effects never interacted in any time window or for the ERP components analyzed (P1, N170, EPN). Thus, whether emotion is explicitly discriminated or irrelevant for the task at hand, neural correlates of fearful and happy facial expressions seem immune to these task demands during the first 350ms of visual processing. Copyright © 2017 Elsevier B.V. All rights reserved.
Wang, Chao; Ding, Mingzhou; Kluger, Benzi M
It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs) were identified: 1) A left-frontotemporal negativity (250-700 ms) that was positively associated with word-reading performance; 2) a midline-frontal negativity (450-800 ms) that was positively associated with color-naming and incongruent performance; 3) a left-frontal negativity (450-800 ms) that was positively associated with switch trial performance; and 4) a centroparietal positivity (450-800 ms) that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1) domain-specific task facilitation; 2) switch-specific task-set reconfiguration; 3) preparation for response conflict; and 4) proactive attentional control. Examining the relationship between ERPs and behavioral
Full Text Available It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs were identified: 1 A left-frontotemporal negativity (250-700 ms that was positively associated with word-reading performance; 2 a midline-frontal negativity (450-800 ms that was positively associated with color-naming and incongruent performance; 3 a left-frontal negativity (450-800 ms that was positively associated with switch trial performance; and 4 a centroparietal positivity (450-800 ms that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1 domain-specific task facilitation; 2 switch-specific task-set reconfiguration; 3 preparation for response conflict; and 4 proactive attentional control. Examining the relationship between ERPs and behavioral
Bressloff, Paul C; Lai, Yi Ming
We extend the theory of noise-induced phase synchronization to the case of a neural master equation describing the stochastic dynamics of an ensemble of uncoupled neuronal population oscillators with intrinsic and extrinsic noise. The master
Zhou, Jiantao; Xu, Qinzhen; Pei, Wenjiang; He, Zhenya; Szu, Harold
Synchronization of neural networks by mutual learning has been demonstrated to be possible for constructing key exchange protocol over public channel. However, the neural cryptography schemes presented so far are not the securest under regular flipping attack (RFA) and are completely insecure under majority flipping attack (MFA). We propose a scheme by splitting the mutual information and the training process to improve the security of neural cryptosystem against flipping attacks. Both analytical and simulation results show that the success probability of RFA on the proposed scheme can be decreased to the level of brute force attack (BFA) and the success probability of MFA still decays exponentially with the weights' level L. The synchronization time of the parties also remains polynomial with L. Moreover, we analyze the security under an advanced flipping attack.
Caroline M. Whiting
Full Text Available Rapid and automatic processing of grammatical complexity is argued to take place during speech comprehension, engaging a left-lateralised fronto-temporal language network. Here we address how neural activity in these regions is modulated by the grammatical properties of spoken words. We used combined magneto- and electroencephalography (MEG, EEG to delineate the spatiotemporal patterns of activity that support the recognition of morphologically complex words in English with inflectional (-s and derivational (-er affixes (e.g. bakes, baker. The mismatch negativity (MMN, an index of linguistic memory traces elicited in a passive listening paradigm, was used to examine the neural dynamics elicited by morphologically complex words. Results revealed an initial peak 130-180 ms after the deviation point with a major source in left superior temporal cortex. The localisation of this early activation showed a sensitivity to two grammatical properties of the stimuli: 1 the presence of morphological complexity, with affixed words showing increased left-laterality compared to non-affixed words; and 2 the grammatical category, with affixed verbs showing greater left-lateralisation in inferior frontal gyrus compared to affixed nouns (bakes vs. beaks. This automatic brain response was additionally sensitive to semantic coherence (the meaning of the stem vs. the meaning of the whole form in fronto-temporal regions. These results demonstrate that the spatiotemporal pattern of neural activity in spoken word processing is modulated by the presence of morphological structure, predominantly engaging the left-hemisphere’s fronto-temporal language network, and does not require focused attention on the linguistic input.
Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.
Haase, Lori; May, April C; Falahpour, Maryam; Isakovic, Sara; Simmons, Alan N; Hickman, Steven D; Liu, Thomas T; Paulus, Martin P
The ability to pay close attention to the present moment can be a crucial factor for performing well in a competitive situation. Training mindfulness is one approach to potentially improve elite athletes' ability to focus their attention on the present moment. However, virtually nothing is known about whether these types of interventions alter neural systems that are important for optimal performance. This pilot study examined whether an intervention aimed at improving mindfulness [Mindful Performance Enhancement, Awareness and Knowledge (mPEAK)] changes neural activation patterns during an interoceptive challenge. Participants completed a task involving anticipation and experience of loaded breathing during functional magnetic resonance imaging recording. There were five main results following mPEAK training: (1) elite athletes self-reported higher levels of interoceptive awareness and mindfulness and lower levels of alexithymia; (2) greater insula and anterior cingulate cortex (ACC) activation during anticipation and post-breathing load conditions; (3) increased ACC activation during the anticipation condition was associated with increased scores on the describing subscale of the Five Facet Mindfulness Questionnaire; (4) increased insula activation during the post-load condition was associated with decreases in the Toronto Alexithymia Scale identifying feelings subscale; (5) decreased resting state functional connectivity between the PCC and the right medial frontal cortex and the ACC. Taken together, this pilot study suggests that mPEAK training may lead to increased attention to bodily signals and greater neural processing during the anticipation and recovery from interoceptive perturbations. This association between attention to and processing of interoceptive afferents may result in greater adaptation during stressful situations in elite athletes.
McDermott, Timothy J; Badura-Brack, Amy S; Becker, Katherine M; Ryan, Tara J; Khanna, Maya M; Heinrichs-Graham, Elizabeth; Wilson, Tony W
Posttraumatic stress disorder (PTSD) is associated with executive functioning deficits, including disruptions in working memory. In this study, we examined the neural dynamics of working memory processing in veterans with PTSD and a matched healthy control sample using magnetoencephalography (MEG). Our sample of recent combat veterans with PTSD and demographically matched participants without PTSD completed a working memory task during a 306-sensor MEG recording. The MEG data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach to identify spatiotemporal dynamics. Fifty-one men were included in our analyses: 27 combat veterans with PTSD and 24 controls. Across all participants, a dynamic wave of neural activity spread from posterior visual cortices to left frontotemporal regions during encoding, consistent with a verbal working memory task, and was sustained throughout maintenance. Differences related to PTSD emerged during early encoding, with patients exhibiting stronger α oscillatory responses than controls in the right inferior frontal gyrus (IFG). Differences spread to the right supramarginal and temporal cortices during later encoding where, along with the right IFG, they persisted throughout the maintenance period. This study focused on men with combat-related PTSD using a verbal working memory task. Future studies should evaluate women and the impact of various traumatic experiences using diverse tasks. Posttraumatic stress disorder is associated with neurophysiological abnormalities during working memory encoding and maintenance. Veterans with PTSD engaged a bilateral network, including the inferior prefrontal cortices and supramarginal gyri. Right hemispheric neural activity likely reflects compensatory processing, as veterans with PTSD work to maintain accurate performance despite known cognitive deficits associated with the disorder.
Lacombe, Jacinthe; Jolicoeur, Pierre; Grimault, Stephan; Pineault, Jessica; Joubert, Sven
Semantic memory recruits an extensive neural network including the left inferior prefrontal cortex (IPC) and the left temporoparietal region, which are involved in semantic control processes, as well as the anterior temporal lobe region (ATL) which is considered to be involved in processing semantic information at a central level. However, little is known about the underlying neuronal integrity of the semantic network in normal aging. Young and older healthy adults carried out a semantic judgment task while their cortical activity was recorded using magnetoencephalography (MEG). Despite equivalent behavioral performance, young adults activated the left IPC to a greater extent than older adults, while the latter group recruited the temporoparietal region bilaterally and the left ATL to a greater extent than younger adults. Results indicate that significant neuronal changes occur in normal aging, mainly in regions underlying semantic control processes, despite an apparent stability in performance at the behavioral level. Copyright © 2015 Elsevier Inc. All rights reserved.
Andrade, Roberto Márcio de; Eduardo, Alexandre Carlos
In the ceramic industry, rarely testing systems were employed to on-line detect the presence of defects in ceramic tiles. This paper is concerned with the problem of automatic inspection of ceramic tiles using Infrared Images and Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally from laboratory and on line tile samples. It has been performed system for IR image processing and, utilizing an Artificial Neural Network (ANN), det...
Humor operates through a variety of techniques, which first generate surprise and then amusement and laughter once the unexpected incongruity is resolved. As different types of jokes use different techniques, the corresponding humor processes also differ. The present study builds on the framework of the 'tri-component theory of humor,' which details the mechanisms involved in cognition (comprehension), affect (appreciation), and laughter (expression). This study seeks to identify differences among joke types and between sexes/genders in the neural mechanisms underlying humor processing. Three types of verbal jokes, bridging-inference jokes (BJs), exaggeration jokes (EJs), and ambiguity jokes (AJs), were used as stimuli. The findings revealed differences in brain activity for an interaction between sex/gender and joke type. For BJs, women displayed greater activation in the temporoparietal-mesocortical-motor network than men, demonstrating the importance of the temporoparietal junction (TPJ) presumably for 'theory of mind' processing, the orbitofrontal cortex for motivational functions and reward coding, and the supplementary motor area for laughter. Women also showed greater activation than men in the frontal-mesolimbic network associated with EJs, including the anterior (frontopolar) prefrontal cortex (aPFC, BA 10) for executive control processes, and the amygdala and midbrain for reward anticipation and salience processes. Conversely, AJs elicited greater activation in men than women in the frontal-paralimbic network, including the dorsal prefrontal cortex (dPFC) and parahippocampal gyrus. All joke types elicited greater activation in the aPFC of women than of men, whereas men showed greater activation than women in the dPFC. To confirm the findings related to sex/gender differences, random group analysis and within group variance analysis were also performed. These findings help further establish the mechanisms underlying the processing of different joke types
Full Text Available Humor operates through a variety of techniques, which first generate surprise and then amusement and laughter once the unexpected incongruity is resolved. As different types of jokes use different techniques, the corresponding humor processes also differ. The present study builds on the framework of the ‘tri-component theory of humor’, which details the mechanisms involved in cognition (comprehension, affect (appreciation, and laughter (expression. This study seeks to identify differences among joke types and between sexes/genders in the neural mechanisms underlying humor processing. Three types of verbal jokes, bridging-inference jokes (BJs, exaggeration jokes (EJs, and ambiguity jokes (AJs, were used as stimuli. The findings revealed differences in brain activity for an interaction between sex/gender and joke type. For BJs, women displayed greater activation in the temporoparietal-mesocortical-motor network than men, demonstrating the importance of the temporoparietal junction (TPJ presumably for ‘theory of mind’ processing, the orbitofrontal cortex for motivational functions and reward coding, and the supplementary motor area for laughter. Women also showed greater activation than men in the frontal-mesolimbic network associated with EJs, including the anterior (frontopolar prefrontal cortex (aPFC, BA 10 for executive control processes, and the amygdala and midbrain for reward anticipation and salience processes. Conversely, AJs elicited greater activation in men than women in the frontal-paralimbic network, including the dorsal prefrontal cortex (dPFC and parahippocampal gyrus. All joke types elicited greater activation in the aPFC of women than of men, whereas men showed greater activation than women in the dPFC. To confirm the findings related to sex/gender differences, random group analysis and within group variance analysis were also performed. These findings help further establish the mechanisms underlying the processing of
Chen, Hung Yi; Huang, Shiuh Jer
The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system's nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semi experimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional Pid controller
Feng Yuan-Yuan; Wu Liang; Zhu Shi-Qun
Synchronization of Kuramoto phase oscillators arranged in real complex neural networks is investigated. It is shown that the synchronization greatly depends on the sets of natural frequencies of the involved oscillators. The influence of network connectivity heterogeneity on synchronization depends particularly on the correlation between natural frequencies and node degrees. This finding implies a potential application that inhibiting the effects caused by the changes of network structure can be balanced out nicely by choosing the correlation parameter appropriately. (general)
McDermott, Timothy J; Badura-Brack, Amy S; Becker, Katherine M; Ryan, Tara J; Bar-Haim, Yair; Pine, Daniel S; Khanna, Maya M; Heinrichs-Graham, Elizabeth; Wilson, Tony W
Posttraumatic stress disorder (PTSD) is associated with executive functioning deficits, including disruptions in working memory (WM). Recent studies suggest that attention training reduces PTSD symptomatology, but the underlying neural mechanisms are unknown. We used high-density magnetoencephalography (MEG) to evaluate whether attention training modulates brain regions serving WM processing in PTSD. Fourteen veterans with PTSD completed a WM task during a 306-sensor MEG recording before and after 8 sessions of attention training treatment. A matched comparison sample of 12 combat-exposed veterans without PTSD completed the same WM task during a single MEG session. To identify the spatiotemporal dynamics, each group's data were transformed into the time-frequency domain, and significant oscillatory brain responses were imaged using a beamforming approach. All participants exhibited activity in left hemispheric language areas consistent with a verbal WM task. Additionally, veterans with PTSD and combat-exposed healthy controls each exhibited oscillatory responses in right hemispheric homologue regions (e.g., right Broca's area); however, these responses were in opposite directions. Group differences in oscillatory activity emerged in the theta band (4-8 Hz) during encoding and in the alpha band (9-12 Hz) during maintenance and were significant in right prefrontal and right supramarginal and inferior parietal regions. Importantly, following attention training, these significant group differences were reduced or eliminated. This study provides initial evidence that attention training improves aberrant neural activity in brain networks serving WM processing.
Thompson, Elaine C.; Carr, Kali Woodruff; White-Schwoch, Travis; Otto-Meyer, Sebastian; Kraus, Nina
From bustling classrooms to unruly lunchrooms, school settings are noisy. To learn effectively in the unwelcome company of numerous distractions, children must clearly perceive speech in noise. In older children and adults, speech-in-noise perception is supported by sensory and cognitive processes, but the correlates underlying this critical listening skill in young children (3–5 year olds) remain undetermined. Employing a longitudinal design (two evaluations separated by ~12 months), we followed a cohort of 59 preschoolers, ages 3.0–4.9, assessing word-in-noise perception, cognitive abilities (intelligence, short-term memory, attention), and neural responses to speech. Results reveal changes in word-in-noise perception parallel changes in processing of the fundamental frequency (F0), an acoustic cue known for playing a role central to speaker identification and auditory scene analysis. Four unique developmental trajectories (speech-in-noise perception groups) confirm this relationship, in that improvements and declines in word-in-noise perception couple with enhancements and diminishments of F0 encoding, respectively. Improvements in word-in-noise perception also pair with gains in attention. Word-in-noise perception does not relate to strength of neural harmonic representation or short-term memory. These findings reinforce previously-reported roles of F0 and attention in hearing speech in noise in older children and adults, and extend this relationship to preschool children. PMID:27864051
Full Text Available A BP (backpropagation neural network method is employed to solve the problems existing in the synthetic characteristic curve processing of hydroturbine at present that most studies are only concerned with data in the high efficiency and large guide vane opening area, which can hardly meet the research requirements of transition process especially in large fluctuation situation. The principle of the proposed method is to convert the nonlinear characteristics of turbine to torque and flow characteristics, which can be used for real-time simulation directly based on neural network. Results show that obtained sample data can be extended successfully to cover working areas wider under different operation conditions. Another major contribution of this paper is the resampling technique proposed in the paper to overcome the limitation to sample period simulation. In addition, a detailed analysis for improvements of iteration convergence of the pressure loop is proposed, leading to a better iterative convergence during the head pressure calculation. Actual applications verify that methods proposed in this paper have better simulation results which are closer to the field and provide a new perspective for hydroturbine synthetic characteristic curve fitting and modeling.
Gottlieb, Lauren J.; Rugg, Michael D.
Prior research has demonstrated that the neural correlates of successful encoding (“subsequent memory effects”) partially overlap with neural regions selectively engaged by the on-line demands of the study task. The primary goal of the present experiment was to determine whether this overlap is associated solely with encoding processes supporting later recollection, or whether overlapping subsequent memory and study condition effects are also evident when later memory is familiarity-based. Subjects (N = 17) underwent fMRI scanning while studying a series of visually and auditorily presented words. Memory for the words was subsequently tested with a modified Remember/Know procedure. Auditorily selective subsequent familiarity effects were evident in bilateral temporal regions that also responded preferentially to auditory items. Although other interpretations are possible, these findings suggest that overlap between study condition-selective subsequent memory effects and regions selectively sensitive to study demands is not uniquely associated with later recollection. In addition, modality-independent subsequent memory effects were identified in several cortical regions. In every case, the effects were greatest for later recollected items, and smaller for items later recognized on the basis of familiarity. The implications of this quantitative dissociation for dual-process models of recognition memory are discussed. PMID:21852431
Full Text Available Recent neuroimaging studies have identified a set of brain regions that are metabolically active during wakeful rest and consistently deactivate in a variety the performance of demanding tasks. This "default network" has been functionally linked to the stream of thoughts occurring automatically in the absence of goal-directed activity and which constitutes an aspect of mental behavior specifically addressed by many meditative practices. Zen meditation, in particular, is traditionally associated with a mental state of full awareness but reduced conceptual content, to be attained via a disciplined regulation of attention and bodily posture. Using fMRI and a simplified meditative condition interspersed with a lexical decision task, we investigated the neural correlates of conceptual processing during meditation in regular Zen practitioners and matched control subjects. While behavioral performance did not differ between groups, Zen practitioners displayed a reduced duration of the neural response linked to conceptual processing in regions of the default network, suggesting that meditative training may foster the ability to control the automatic cascade of semantic associations triggered by a stimulus and, by extension, to voluntarily regulate the flow of spontaneous mentation.
Pedersen, Dennis; Pedersen, Torben Bach
The increasing availability of XML-based data sources, e.g., for publishing data on the WWW, means that more and more applications (data consumers) rely on accessing and using XML data. Typically, the access is achieved by defining views over the XML data, and accessing data through these views....... However, the XML data sources are often independent of the data consumers and may change their schemas without notification, invalidating the XML views defined by the data consumers. This requires the view definitions to be updated to reflect the new structure of the data sources, a process termed view...... synchronization. XPath is the most commonly used language for retrieving parts of XML documents, and is thus an important cornerstone for XML view definitions. This paper presents techniques for discovering schema changes in XML data sources and synchronizing XPath-based views to reflect these schema changes...
Zhao, Jun; Luo, Shan-Ming; Li, Feng-Qiang; Xu, Chen-Bing [Xiamen University of Technology, Xiamen (China)
Failure analysis shows that increased die temperature caused by severe plastic deformation of material and heat conduction between hot billet and cavity significantly affects the distortion of gear cavity in steel synchronizer ring forging process. The forging process of steel synchronizer ring and die temperature distribution under different forging conditions are analyzed through finite element method. Simulation results show that severe plastic deformation occurs in the gear cavity. The improvement of lubrication condition results in decreased die temperature. When the initial billet temperature is high, the die temperature is also high. Increasing forging speed in a certain range facilitates the die temperature decrease. The distribution of die temperature in synthetic forming technology is more reasonable than that of one step forging. The synthetic forming technology is adopted in production to reduce the effects of severe plastic deformation caused by die temperature. The ejection mechanism and control system of the double disc friction press are improved to reduce the contact time between the hot billet and cavity. Experimental results show that synthetic forming technology is reasonable, and that the die service life is prolonged.