Cognitive Neurodynamics (COGN NEURODYNAMICS )

Publisher: Springer Verlag


  • Impact factor
    Hide impact factor history
    Impact factor
  • 5-year impact
  • Cited half-life
  • Immediacy index
  • Eigenfactor
  • Article influence
  • ISSN
  • OCLC
  • Material type
    Periodical, Internet resource
  • Document type
    Journal / Magazine / Newspaper, Internet Resource

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: How task focus affects recognition of change in vocal emotion remains in debate. In this study, we investigated the role of task focus for change detection in emotional prosody by measuring changes in event-related electroencephalogram (EEG) power. EEG was recorded for prosodies with and without emotion change while subjects performed emotion change detection task (explicit) and visual probe detection task (implicit). We found that vocal emotion change induced theta event-related synchronization during 100–600 ms regardless of task focus. More importantly, vocal emotion change induced significant beta event-related desynchronization during 400–750 ms under explicit instead of implicit task condition. These findings suggest that the detection of emotional changes is independent of task focus, while the task focus effect in neural processing of vocal emotion change is specific to the integration of emotional deviations.
    Cognitive Neurodynamics 12/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recent experimental studies have revealed that up and down transitions exist in membrane potential of neurons. This paper focuses on the neurodynamical research of these transitions in a single neuron since it is the basic to study the transitions in the neural network for further work. The results show there exists two stable levels in the neuron called up and down states. And transitions between these two states are bidirectional or unidirectional with the values of parameters changing. We also study the periodic spontaneous activity of the transitions between up and down states without any inputting stimulus which coheres with the experimental results.
    Cognitive Neurodynamics 12/2014; 8(6).
  • [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    Cognitive Neurodynamics 12/2014; 8(6).
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this study, we tested the underlying mechanisms of early emotional prosody perception, especially examined whether change detection in oddball paradigm was caused by emotional category and physical properties. Using implicit oddball paradigms, the current study manipulated the cues for detecting deviant stimuli from standards in three conditions: the simultaneous changes in emotional category and physical properties (EP condition), change in emotional category alone (E condition), and change in physical properties alone (P condition). ERP results revealed that physical property change increased brain responses to deviant stimuli in the EP than in the E condition at early stage 90–160 ms, suggesting that physical property change of emotional sounds can also be detected at the early stage. At the later stage 160–260 ms, the simultaneous and respective changes in emotional category and physical properties were reliably detected, and the sum of the brain responses to the corresponding changes in E and P conditions was equal to the brain responses to the simultaneous changes in EP condition. Source analysis further revealed that stimuli-driven regions (inferior parietal lobule), temporal and frontal cortices were activated at early stage, while only frontal cortices for higher cognitive processing were activated at later stage. These findings suggest that emotional prosody changes in physical properties and emotion category are perceived as domain-general change information in emotional prosody perception.
    Cognitive Neurodynamics 12/2014; 8(6).
  • Cognitive Neurodynamics 10/2014;
  • Cognitive Neurodynamics 10/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain-computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system).
    Cognitive Neurodynamics 10/2014; 8(5):399-409.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper addresses the stability problem on the memristive neural networks with time-varying impulses. Based on the memristor theory and neural network theory, the model of the memristor-based neural network is established. Different from the most publications on memristive networks with fixed-time impulse effects, we consider the case of time-varying impulses. Both the destabilizing and stabilizing impulses exist in the model simultaneously. Through controlling the time intervals of the stabilizing and destabilizing impulses, we ensure the effect of the impulses is stabilizing. Several sufficient conditions for the globally exponentially stability of memristive neural networks with time-varying impulses are proposed. The simulation results demonstrate the effectiveness of the theoretical results.
    Cognitive Neurodynamics 10/2014; 8(5).
  • [Show abstract] [Hide abstract]
    ABSTRACT: Theta–gamma coupling in the hippocampus is thought to be involved in cognitive processes. A large body of research establishes that the hippocampus plays a crucial role in the organization and maintenance of episodic memory, and that sharp-wave ripples (SWR) contribute to memory consolidation processes. Here, we investigated how the local field potentials in the hippocampal CA1 area adapted along with rats’ behavioral changes within a session during a spatial alternation task that included a 1-s fixation and a 1.5-s delay. We observed that, as the session progressed, the duration from fixation onset to nose-poking in the choice hole reduced as well as the number of premature responses during the delay. Parallel with the behavioral transitions, the power of high gamma during the delay period increased whereas that of low gamma decreased later in the session. Furthermore, the strength of theta–gamma modulation later in the session showed significant increase as compared to earlier in the session. Examining SWR during the reward period, we found that the number of SWR events decreased as well as the power in a wide frequency range during SWR events. In addition, the correlation between SWR and gamma oscillations just before SWR events was higher in the earlier trials than in the later trials. Our findings support the notion that the inputs from CA3 and entorhinal cortex play a critical role in memory consolidation as well as in cognitive processes. We suggest that SWR and the inputs from the two areas serve to stabilize the task behavior and neural activities.
    Cognitive Neurodynamics 10/2014; 8(5).
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper is concerned with a class of nonlinear uncertain switched networks with discrete time-varying delays . Based on the strictly complete property of the matrices system and the delay-decomposing approach, exploiting a new Lyapunov-Krasovskii functional decomposing the delays in integral terms, the switching rule depending on the state of the network is designed. Moreover, by piecewise delay method, discussing the Lyapunov functional in every different subintervals, some new delay-dependent robust stability criteria are derived in terms of linear matrix inequalities, which lead to much less conservative results than those in the existing references and improve previous results. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.
    Cognitive Neurodynamics 08/2014; 8(4):313-26.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Functional brain network, one of the main methods for brain functional studies, can provide the connectivity information among brain regions. In this research, EEG-based functional brain network is built and analyzed through a new wavelet limited penetrable visibility graph (WLPVG) approach. This approach first decompose EEG into δ, θ, α, β sub-bands, then extracting nonlinear features from single channel signal, in addition forming a functional brain network for each sub-band. Manual acupuncture (MA) as a stimulation to the human nerve system, may evoke varied modulating effects in brain activities. To investigating whether and how this happens, WLPVG approach is used to analyze the EEGs of 15 healthy subjects with MA at acupoint ST36 on the right leg. It is found that MA can influence the complexity of EEG sub-bands in different ways and lead the functional brain networks to obtain higher efficiency and stronger small-world property compared with pre-acupuncture control state.
    Cognitive Neurodynamics 06/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.
    Cognitive Neurodynamics 06/2014; 8(3):251-9.