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Cognitive Neurodynamics

Andrew A Fingelkurts, Alexander A Fingelkurts
No abstract text is available yet for this article.
February 2019: Cognitive Neurodynamics
Victor J Barranca, Han Huang, Sida Li
A dynamic balance between strong excitatory and inhibitory neuronal inputs is hypothesized to play a pivotal role in information processing in the brain. While there is evidence of the existence of a balanced operating regime in several cortical areas and idealized neuronal network models, it is important for the theory of balanced networks to be reconciled with more physiological neuronal modeling assumptions. In this work, we examine the impact of spike-frequency adaptation, observed widely across neurons in the brain, on balanced dynamics...
February 2019: Cognitive Neurodynamics
G Rigatos, P Wira, A Melkikh
The article proposes a nonlinear optimal control method for synchronization of neurons that exhibit nonlinear dynamics and are subject to time-delays. The model of the Hindmarsh-Rose (HR) neurons is used as a case study. The dynamic model of the coupled HR neurons undergoes approximate linearization around a temporary operating point which is recomputed at each iteration of the control method. The linearization procedure relies on Taylor series expansion of the model and on computation of the associated Jacobian matrices...
February 2019: Cognitive Neurodynamics
Fengyun Zhu, Rubin Wang, Xiaochuan Pan, Zhenyu Zhu
Brief bursts of high-frequency spikes are a common firing pattern of neurons. The cellular mechanisms of bursting and its biological significance remain a matter of debate. Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting. The flow of ions across the membrane of the bursting neuron with or without current stimulation and its power which contributes to the change of the transmembrane electrical potential energy are analyzed here in detail...
February 2019: Cognitive Neurodynamics
Sang-Yoon Kim, Woochang Lim
We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabási-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity...
February 2019: Cognitive Neurodynamics
Mahda Nasrolahzadeh, Zeynab Mohammadpoory, Javad Haddadnia
In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation...
February 2019: Cognitive Neurodynamics
Ren-Jen Hwang, Hsin-Ju Chen, Zhan-Xian Guo, Yu-Sheun Lee, Tai-Ying Liu
The effects of exercise on cognitive abilities have been studied. However, evidence regarding the neural substrates of sad emotion regulation is limited. Women have higher rates for affective disorders than men, but insufficient outcomes assess how aerobic exercises modulate central frontal activation in sad emotion inhibition and resilience among healthy women. This study investigated the effects of aerobic exercise-related brain activity on sad emotion inhibition processing in young women. Sad facial Go/No-Go and neutral Go/No-Go trials were conducted among 30 healthy young women to examine the changes in the N2 component, which reflects frontal inhibition responses, between pre-exercise and post-exercise periods...
February 2019: Cognitive Neurodynamics
Shankha Sanyal, Sayan Nag, Archi Banerjee, Ranjan Sengupta, Dipak Ghosh
Can we hear the sound of our brain? Is there any technique which can enable us to hear the neuro-electrical impulses originating from the different lobes of brain? The answer to all these questions is YES. In this paper we present a novel method with which we can sonify the electroencephalogram (EEG) data recorded in "control" state as well as under the influence of a simple acoustical stimuli-a tanpura drone. The tanpura has a very simple construction yet the tanpura drone exhibits very complex acoustic features, which is generally used for creation of an ambience during a musical performance...
February 2019: Cognitive Neurodynamics
Sou Nobukawa, Teruya Yamanishi, Haruhiko Nishimura, Yuji Wada, Mitsuru Kikuchi, Tetsuya Takahashi
Recent advances in nonlinear analytic methods for electroencephalography have clarified the reduced complexity of spatiotemporal dynamics in brain activity observed in Alzheimer's disease (AD). However, there are far fewer studies exploring temporal scale dependent fractal properties in AD, despite the importance of studying the dynamics of brain activity within physiologically relevant frequency ranges. Higuchi's fractal dimension is a widely used index for evaluating fractality in brain activity, but temporal-scale-specific characteristics are lost due to its requirement of averaging over the entire range of temporal scales...
February 2019: Cognitive Neurodynamics
JiaYi Wang, XiaoLi Yang, ZhongKui Sun
Excessive synchronization of neurons in cerebral cortex is believed to play a crucial role in the emergence of neuropsychological disorders such as Parkinson's disease, epilepsy and essential tremor. This study, by constructing a modular neuronal network with modified Oja's learning rule, explores how to eliminate the pathological synchronized rhythm of interacted busting neurons numerically. When all neurons in the modular neuronal network are strongly synchronous within a specific range of coupling strength, the result reveals that synaptic plasticity with large learning rate can suppress bursting synchronization effectively...
December 2018: Cognitive Neurodynamics
Guanzheng Wang, Rubin Wang, Wanzheng Kong, Jianhai Zhang
Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves-Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed...
December 2018: Cognitive Neurodynamics
Fatemeh Parastesh, Karthikeyan Rajagopal, Anitha Karthikeyan, Ahmed Alsaedi, Tasawar Hayat, Viet-Thanh Pham
The last two decades have seen many literatures on the mathematical and computational analysis of neuronal activities resulting in many mathematical models to describe neuron. Many of those models have described the membrane potential of a neuron in terms of the leakage current and the synaptic inputs. Only recently researchers have proposed a new neuron model based on the electromagnetic induction theorem, which considers inner magnetic fluctuation and external electromagnetic radiation as a significant missing part that can participate in neural activity...
December 2018: Cognitive Neurodynamics
Hong Zeng, Chen Yang, Guojun Dai, Feiwei Qin, Jianhai Zhang, Wanzeng Kong
Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers...
December 2018: Cognitive Neurodynamics
Mahda Nasrolahzadeh, Zeynab Mohammadpoory, Javad Haddadnia
An early and accurate diagnosis of Alzheimer's disease (AD) has been progressively attracting more attention in recent years. One of the main problems of AD is the loss of language skills. This paper presents a computational framework for classifying AD patients compared to healthy control subjects using information from spontaneous speech signals. Spontaneous speech data are obtained from 30 AD patients and 30 healthy controls. Because of the nonlinear and dynamic nature of speech signals, higher order spectral features (specifically bispectrum) were used for analysis...
December 2018: Cognitive Neurodynamics
Jichi Chen, Hong Wang, Chengcheng Hua, Qiaoxiu Wang, Chong Liu
A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states...
December 2018: Cognitive Neurodynamics
Chaolin Teng, Yao Cheng, Chao Wang, Yijing Ren, Weiyong Xu, Jin Xu
Differences of EEG synchronization between normal old and young people during a working memory (WM) task were investigated. The synchronization likelihood (SL) is a novel method to assessed synchronization in multivariate time series for non-stationary systems. To evaluate this method to study the mechanisms of WM, we calculated the SL values in brain electrical activity for both resting state and task state. EEG signals were recorded from 14 young adults and 12 old adults during two different states, respectively...
December 2018: Cognitive Neurodynamics
Balázs Szalkai, Bálint Varga, Vince Grolmusz
Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or connectomes which were defined in the last twenty years for enormous networks-like the graph of the World Wide Web-with hundreds of millions of nodes. Since the connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain...
December 2018: Cognitive Neurodynamics
Mauro Ursino, Cristiano Cuppini, Stefano F Cappa, Eleonora Catricalà
According with a featural organization of semantic memory, this work is aimed at investigating, through an attractor network, the role of different kinds of features in the representation of concepts, both in normal and neurodegenerative conditions. We implemented new synaptic learning rules in order to take into account the role of partially shared features and of distinctive features with different saliency. The model includes semantic and lexical layers, coding, respectively for object features and word-forms...
December 2018: Cognitive Neurodynamics
Motoki Yamada, Yoshio Sakurai
Observational learning, which modulates one's own behavior by observing the adaptive behavior of others, is crucial for behaving efficiently in social communities. Although many behavioral experiments have reported observational learning in monkeys and humans, its neural mechanisms are still unknown. In order to conduct neuroscientific researches with recording neural activities, we developed an observational learning task for rats. We designed the task using Barnes circular maze and then tested whether rats (observers) could actually improve their learning by observing the behavior of other rats (models) that had already acquired the task...
October 2018: Cognitive Neurodynamics
Yingmei Qin, Chunxiao Han, Yanqiu Che, Jia Zhao
A randomly connected network is constructed with similar characteristics (e.g., the ratio of excitatory and inhibitory neurons, the connection probability between neurons, and the axonal conduction delays) as that in the mammalian neocortex and the effects of high-frequency electrical field on the response of the network to a subthreshold low-frequency electrical field are studied in detail. It is found that both the amplitude and frequency of the high-frequency electrical field can modulate the response of the network to the low-frequency electric field...
October 2018: Cognitive Neurodynamics
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