# Cognitive Neurodynamics (COGN NEURODYNAMICS )

Publisher: Springer Verlag

## Description

• Impact factor
1.74
Show impact factor history

Impact factor
.
Year
• 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

• Pre-print
• Author can archive a pre-print version
• Post-print
• Author can archive a post-print version
• Conditions
• 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

• ##### Article: A review of EEG and MEG for brainnetome research
Xin Zhang, Xu Lei, Ting Wu, Tianzi Jiang
[hide abstract]
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).
• ##### Article: Dynamic behavior analysis of fractional-order Hindmarsh–Rose neuronal model
Dong Jun, Zhang Guang-jun, Xie Yong, Yao Hong, Wang Jue
[hide abstract]
ABSTRACT: Previous experimental work has shown that the firing rate of multiple time-scales of adaptation for single rat neocortical pyramidal neurons is consistent with fractional-order differentiation, and the fractional-order neuronal models depict the firing rate of neurons more verifiably than other models do. For this reason, the dynamic characteristics of the fractional-order Hindmarsh–Rose (HR) neuronal model were here investigated. The results showed several obvious differences in dynamic characteristic between the fractional-order HR neuronal model and an integer-ordered model. First, the fractional-order HR neuronal model displayed different firing modes (chaotic firing and periodic firing) as the fractional order changed when other parameters remained the same as in the integer-order model. However, only one firing mode is displayed in integer-order models with the same parameters. The fractional order is the key to determining the firing mode. Second, the Hopf bifurcation point of this fractional-order model, from the resting state to periodic firing, was found to be larger than that of the integer-order model. Third, for the state of periodically firing of fractional-order and integer-order HR neuron model, the firing frequency of the fractional-order neuronal model was greater than that of the integer-order model, and when the fractional order of the model decreased, the firing frequency increased.
Cognitive Neurodynamics 04/2014; 8(2).
• ##### Article: Synchrony based learning rule of Hopfield like chaotic neural networks with desirable structure
[hide abstract]
ABSTRACT: In this paper a new learning rule for the coupling weights tuning of Hopfield like chaotic neural networks is developed in such a way that all neurons behave in a synchronous manner, while the desirable structure of the network is preserved during the learning process. The proposed learning rule is based on sufficient synchronization criteria, on the eigenvalues of the weight matrix belonging to the neural network and the idea of Structured Inverse Eigenvalue Problem. Our developed learning rule not only synchronizes all neuron’s outputs with each other in a desirable topology, but also enables us to enhance the synchronizability of the networks by choosing the appropriate set of weight matrix eigenvalues. Specifically, this method is evaluated by performing simulations on the scale-free topology.
Cognitive Neurodynamics 04/2014;
• ##### Article: Increase trend of correlation and phase synchrony of microwire iEEG before macroseizure onset
Sanqing Hu, Jianfen Chi, Jianhai Zhang, Wanzeng Kong, Yu Cao, Bin He
[hide abstract]
ABSTRACT: Micro/macrowire intracranial EEG (iEEG) signals recorded from implanted micro/macroelectrodes in epileptic patients have received great attention and are considered to include much information of neuron activities in seizure transition compared to scalp EEG from cortical electrodes. Microelectrode is contacted more close to neurons than macroelectrode and it is more sensitive to neuron activity changes than macroelectrode. Microwire iEEG recordings are inevitably advantageous over macrowire iEEG recordings to reveal neuronal mechanisms contributing to the generation of seizures. In this study, we investigate the seizure generation from microwire iEEG recordings and discuss synchronization of microwire iEEGs in four frequency bands: alpha (1−30 Hz), gamma (30−80 Hz), ripple (80–250 Hz), and fast ripple (>250 Hz) via two measures: correlation and phase synchrony. We find that an increase trend of correlation or phase synchrony exists before the macroseizure onset mostly in gamma and ripple bands where the duration of the preictal states varied in different seizures ranging up to a few seconds (minutes). This finding is contrast to the well-known result that a decrease of synchronization in macro domains exists before the macroseizure onset. The finding demonstrates that it is only when the seizure has recruited enough surrounding brain tissue does the signal become strong enough to be observed on the clinical macroelectrode and as a result support the hypothesis of progressive coalescence of microseizure domains. The potential ramifications of such an early detection of microscale seizure activity may open a new window on treatment by making possible disruption of seizure activity before it becomes fully established.
Cognitive Neurodynamics 04/2014; 8(2).
• Source
##### Article: Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures.
[hide abstract]
ABSTRACT: Correlations between ten-channel EEGs obtained from thirteen healthy adult participants were investigated. Signals were obtained in two behavioral states: eyes open no task and eyes closed no task. Four time domain measures were compared: Pearson product moment correlation, Spearman rank order correlation, Kendall rank order correlation and mutual information. The psychophysiological utility of each measure was assessed by determining its ability to discriminate between conditions. The sensitivity to epoch length was assessed by repeating calculations with 1, 2, 3, …, 8 s epochs. The robustness to noise was assessed by performing calculations with noise corrupted versions of the original signals (SNRs of 0, 5 and 10 dB). Three results were obtained in these calculations. First, mutual information effectively discriminated between states with less data. Pearson, Spearman and Kendall failed to discriminate between states with a 1 s epoch, while a statistically significant separation was obtained with mutual information. Second, at all epoch durations tested, the measure of between-state discrimination was greater for mutual information. Third, discrimination based on mutual information was more robust to noise. The limitations of this study are discussed. Further comparisons should be made with frequency domain measures, with measures constructed with embedded data and with the maximal information coefficient.
Cognitive Neurodynamics 02/2014; 8(1):1-15.
• ##### Article: Visual pattern discrimination by population retinal ganglion cells' activities during natural movie stimulation.
[hide abstract]
ABSTRACT: In the visual system, neurons often fire in synchrony, and it is believed that synchronous activities of group neurons are more efficient than single cell response in transmitting neural signals to down-stream neurons. However, whether dynamic natural stimuli are encoded by dynamic spatiotemporal firing patterns of synchronous group neurons still needs to be investigated. In this paper we recorded the activities of population ganglion cells in bullfrog retina in response to time-varying natural images (natural scene movie) using multi-electrode arrays. In response to some different brief section pairs of the movie, synchronous groups of retinal ganglion cells (RGCs) fired with similar but different spike events. We attempted to discriminate the movie sections based on temporal firing patterns of single cells and spatiotemporal firing patterns of the synchronous groups of RGCs characterized by a measurement of subsequence distribution discrepancy. The discrimination performance was assessed by a classification method based on Support Vector Machines. Our results show that different movie sections of the natural movie elicited reliable dynamic spatiotemporal activity patterns of the synchronous RGCs, which are more efficient in discriminating different movie sections than the temporal patterns of the single cells' spike events. These results suggest that, during natural vision, the down-stream neurons may decode the visual information from the dynamic spatiotemporal patterns of the synchronous group of RGCs' activities.
Cognitive Neurodynamics 02/2014; 8(1):27-35.
• ##### Article: Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays
[hide abstract]
ABSTRACT: In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov–Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.
Cognitive Neurodynamics 02/2014;
• ##### Article: Convergence analysis of fully complex backpropagation algorithm based on Wirtinger calculus
[hide abstract]
ABSTRACT: This paper considers the fully complex backpropagation algorithm (FCBPA) for training the fully complex-valued neural networks. We prove both the weak convergence and strong convergence of FCBPA under mild conditions. The decreasing monotonicity of the error functions during the training process is also obtained. The derivation and analysis of the algorithm are under the framework of Wirtinger calculus, which greatly reduces the description complexity. The theoretical results are substantiated by a simulation example.
Cognitive Neurodynamics 01/2014;
• ##### Article: Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays
[hide abstract]
ABSTRACT: This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen–Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen–Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.
Cognitive Neurodynamics 01/2014;
• ##### Article: A comparison study of two P300 speller paradigms for brain–computer interface
[hide abstract]
ABSTRACT: In this paper, a comparison of two existing P300 spellers is conducted. In the first speller, the visual stimuli of characters are presented in a single character (SC) paradigm and each button corresponding to a character flashes individually in a random order. The second speller is based on a region-based (RB) paradigm. In the first level, all characters are grouped and each button corresponding to a group flashes individually in a random order. Once a group is selected, the characters in it will appear on the flashing buttons of the second level for the selection of desired character. In a spelling experiment involving 12 subjects, higher online accuracy was obtained on the RB paradigm-based P300 speller than the SC paradigm-based P300 speller. Furthermore, we analyzed P300 detection performance, the P300 waveforms and Fisher ratios using the data collected by the two spellers. It was found that the stimuli display paradigm of the RB speller enhances P300 potential and is more suitable for P300 detection.
Cognitive Neurodynamics 12/2013;
• ##### Article: Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method
[hide abstract]
ABSTRACT: The human operator’s ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human–automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety–critical human–machine cooperative systems.
Cognitive Neurodynamics 12/2013;
• ##### Article: Coupling-induced population synchronization in an excitatory population of subthreshold Izhikevich neurons
[hide abstract]
ABSTRACT: We consider an excitatory population of subthreshold Izhikevich neurons which exhibit noise-induced firings. By varying the coupling strength J, we investigate population synchronization between the noise-induced firings which may be used for efficient cognitive processing such as sensory perception, multisensory binding, selective attention, and memory formation. As J is increased, rich types of population synchronization (e.g., spike, burst, and fast spike synchronization) are found to occur. Transitions between population synchronization and incoherence are well described in terms of an order parameter $\mathcal{O}$ O . As a final step, the coupling induces oscillator death (quenching of noise-induced spikings) because each neuron is attracted to a noisy equilibrium state. The oscillator death leads to a transition from firing to non-firing states at the population level, which may be well described in terms of the time-averaged population spike rate $\overline{R}$ R ¯ . In addition to the statistical-mechanical analysis using $\mathcal{O}$ O and $\overline{R}$ R ¯ , each population and individual state are also characterized by using the techniques of nonlinear dynamics such as the raster plot of neural spikes, the time series of the membrane potential, and the phase portrait. We note that population synchronization of noise-induced firings may lead to emergence of synchronous brain rhythms in a noisy environment, associated with diverse cognitive functions.
Cognitive Neurodynamics 12/2013;
• ##### Article: Predictive modeling of human operator cognitive state via sparse and robust support vector machines
[hide abstract]
ABSTRACT: The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human–machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.
Cognitive Neurodynamics 10/2013;

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