Cognitive Neurodynamics (COGN NEURODYNAMICS )

Publisher: Springer Verlag

Description

  • Impact factor
    1.74
    Show impact factor history
     
    Impact factor
  • 5-year impact
    1.73
  • Cited half-life
    3.20
  • Immediacy index
    0.45
  • Eigenfactor
    0.00
  • Article influence
    0.36
  • ISSN
    1871-4080
  • OCLC
    219471552
  • Material type
    Periodical, Internet resource
  • Document type
    Journal / Magazine / Newspaper, Internet Resource

Publisher details

Springer Verlag

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    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
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    • Authors own final version only can be archived
    • Publisher's version/PDF cannot be used
    • On author's website or institutional repository
    • On funders designated website/repository after 12 months at the funders request or as a result of legal obligation
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (The original publication is available at www.springerlink.com)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • Cognitive Neurodynamics 10/2014;
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    ABSTRACT: A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain's responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain's activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.
    Cognitive Neurodynamics 10/2014; 8(5):389-97.
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    ABSTRACT: Clothianidin (CLO) is one of the pesticides used to protect against insects, and its potential toxic effects on cognitive functions are not clearly known. This study aims to evaluate the possible effects of dose-dependent CLO on learning and memory in infant and adult male rats and the expression of related genes in the hippocampus. Doses of 2, 8 and 24 mg/kg of CLO were administered to newborn infant and adult albino Winstar rats in the form of gavage and dissolved in vehicle matter. Their cognitive and learning functions were evaluated by the Morris water maze and probe tests. Expression levels of N-methyl D-aspartate 1 (GRIN1), muscuranic receptor M1, synoptophysin (SYP) and growth-associated protein 43 (GAP-43) of tissues isolated from the hippocampus were determined using the real-time PCR method. In the Morris water maze test, no change (p > 0.05) was exhibited in the adult and infant rats after CLO was applied, although there was a significant difference (p GAP-43 did not change when compared to the control (p > 0.05). Our study shows that exposure to high doses of CLO causes deterioration of cognitive functions in infant rats.
    Cognitive Neurodynamics 10/2014; 8(5).
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    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.
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    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).
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    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).
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    ABSTRACT: A system with some degree of biological plausibility is developed to categorise items from a widely used machine learning benchmark. The system uses fatiguing leaky integrate and fire neurons, a relatively coarse point model that roughly duplicates biological spiking properties; this allows spontaneous firing based on hypo-fatigue so that neurons not directly stimulated by the environment may be included in the circuit. A novel compensatory Hebbian learning algorithm is used that considers the total synaptic weight coming into a neuron. The network is unsupervised and entirely self-organising. This is relatively effective as a machine learning algorithm, categorising with just neurons, and the performance is comparable with a Kohonen map. However the learning algorithm is not stable, and behaviour decays as length of training increases. Variables including learning rate, inhibition and topology are explored leading to stable systems driven by the environment. The model is thus a reasonable next step toward a full neural memory model.
    Cognitive Neurodynamics 08/2014; 8(4):299-311.
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    ABSTRACT: Recent studies have shown that the dendrites of several neurons are not simple translators but are crucial facilitators of excitatory postsynaptic potential (EPSP) propagation and summation of synaptic inputs to compensate for inherent voltage attenuation. Granule cells (GCs)are located at the gateway for valuable information arriving at the hippocampus from the entorhinal cortex. However, the underlying mechanisms of information integration along the dendrites of GCs in the hippocampus are still unclear. In this study, we investigated the input integration around dendritic branches of GCs in the rat hippocampus. We applied differential spatiotemporal stimulations to the dendrites using a high-speed glutamate-uncaging laser. Our results showed that when two sites close to and equidistant from a branching point were simultaneously stimulated, a nonlinear summation of EPSPs was observed at the soma. In addition, nonlinear summation (facilitation) depended on the stimulus location and was significantly blocked by the application of a voltage-dependent Ca2+ channel antagonist. These findings suggest that the nonlinear summation of EPSPs around the dendritic branches of hippocampal GCs is a result of voltage-dependent Ca2+ channel activation and may play a crucial role in the integration of input information.
    Cognitive Neurodynamics 08/2014; 8(4).
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    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.
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    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;
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    ABSTRACT: In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment's data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system.
    Cognitive Neurodynamics 06/2014; 8(3):227-38.
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    ABSTRACT: This paper is pertained with the synchronization problem for an array of coupled discrete-time complex networks with the presence of both time-varying delays and parameter uncertainties. The time-varying delays are considered both in the network couplings and dynamical nodes. By constructing suitable Lyapunov–Krasovskii functional and utilizing convex reciprocal lemma, new synchronization criteria for the complex networks are established in terms of linear matrix inequalities. Delay-partitioning technique is employed to incur less conservative results. All the results presented here not only depend upon lower and upper bounds of the time-delay, but also the number of delay partitions. Numerical simulations are rendered to exemplify the effectiveness and applicability of the proposed results.
    Cognitive Neurodynamics 06/2014; 8(3).
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    ABSTRACT: According to biological knowledge, the central nervous system controls the central pattern generator (CPG) to drive the locomotion. The brain is a complex system consisting of different functions and different interconnections. The topological properties of the brain display features of small-world network. The synchronization and stochastic resonance have important roles in neural information transmission and processing. In order to study the synchronization and stochastic resonance of the brain based on the CPG, we establish the model which shows the relationship between the small-world neural network (SWNN) and the CPG. We analyze the synchronization of the SWNN when the amplitude and frequency of the CPG are changed and the effects on the CPG when the SWNN’s parameters are changed. And we also study the stochastic resonance on the SWNN. The main findings include: (1) When the CPG is added into the SWNN, there exists parameters space of the CPG and the SWNN, which can make the synchronization of the SWNN optimum. (2) There exists an optimal noise level at which the resonance factor Q gets its peak value. And the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the noise intensity. The results could have important implications for biological processes which are about interaction between the neural network and the CPG.
    Cognitive Neurodynamics 06/2014; 8(3).
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    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.
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    ABSTRACT: The present article develops quantitative behavioral and neurophysiological predictions for rabbits trained on an air-puff version of the trace-interval classical conditioning paradigm. Using a minimal hippocampal model, consisting of 8,000 primary cells sparsely and randomly interconnected as a model of hippocampal region CA-3, the simulations identify conditions which produce a clear split in the number of trials individual animals should need to learn a criterion response. A trace interval that is difficult to learn, but still learnable by half the experimental population, produces a bimodal population of learners: an early learner group and a late learner group. The model predicts that late learners are characterized by two kinds of CA-3 neuronal activity fluctuations that are not seen in the early learners. As is typical in our minimal hippocampal models, the off-rate constant of the N-methyl-d-aspartate receptor receptor gives a timescale to the model that leads to a temporally quantifiable behavior, the learnable trace interval.
    Cognitive Neurodynamics 04/2014;
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    ABSTRACT: The effects of noise on patterns and collective phenomena are studied in a small-world neuronal network with the dynamics of each neuron being described by a two-dimensional Rulkov map neuron. It is shown that for intermediate noise levels, noise-induced ordered patterns emerge spatially, which supports the spatiotemporal coherence resonance. However, the inherent long range couplings of small-world networks can effectively disrupt the internal spatial scale of the media at small fraction of long-range couplings. The temporal order, characterized by the autocorrelation of a firing rate function, can be greatly enhanced by the introduction of small-world connectivity. There exists an optimal fraction of randomly rewired links, where the temporal order and synchronization can be optimized.
    Cognitive Neurodynamics 04/2014;
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    ABSTRACT: The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain’s multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new –ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG’s applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.
    Cognitive Neurodynamics 04/2014; 8(2).