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1.
Article in Chinese | WPRIM | ID: wpr-921837

ABSTRACT

Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.


Subject(s)
Algorithms , Artificial Intelligence , Brain , Neural Networks, Computer
2.
Article in Chinese | WPRIM | ID: wpr-888222

ABSTRACT

Transcranial direct current stimulation (tDCS) is a brain stimulation intervention technique, which has the problem of different criteria for the selection of stimulation parameters. In this study, a four-layer real head model was constructed. Based on this model, the changes of the electric field distribution in the brain with the current intensity, electrode shape, electrode area and electrode spacing were analyzed by using finite element simulation technology, and then the optimal scheme of electrical stimulation parameters was discussed. The results showed that the effective stimulation region decreased and the focusing ability increased with the increase of current intensity. The normal current density of the quadrilateral electrode was obviously larger than that of the circular electrode, which indicated that the quadrilateral electrode was more conducive to current stimulation of neurons. Moreover, the effective stimulation region of the quadrilateral electrode was more concentrated and the focusing ability was stronger. The focusing ability decreased with the increase of electrode area. Specifically, the focusing tended to increase first and then decrease with the increase of electrode spacing and the optimal electrode spacing was 64.0-67.2 mm. These results could provide some basis for the selection of electrical stimulation parameters.


Subject(s)
Brain , Electric Stimulation , Electrodes , Head , Transcranial Direct Current Stimulation
3.
Article in Chinese | WPRIM | ID: wpr-888206

ABSTRACT

Transcranial direct current stimulation (tDCS) is an emerging non-invasive brain stimulation technique. However, the rehabilitation effect of tDCS on stroke disease is unclear. In this paper, based on electroencephalogram (EEG) and complex network analysis methods, the effect of tDCS on brain function network of stroke patients during rehabilitation was investigated. The resting state EEG signals of 31 stroke rehabilitation patients were collected and divided into stimulation group (16 cases) and control group (15 cases). The Pearson correlation coefficients were calculated between the channels, brain functional network of two groups were constructed before and after stimulation, and five characteristic parameters were analyzed and compared such as node degree, clustering coefficient, characteristic path length, global efficiency, and small world attribute. The results showed that node degree, clustering coefficient, global efficiency, and small world attributes of brain functional network in the tDCS group were significantly increased, characteristic path length was significantly reduced, and the difference was statistically significant (


Subject(s)
Brain , Electroencephalography , Humans , Stroke , Stroke Rehabilitation , Transcranial Direct Current Stimulation
4.
Article in Chinese | WPRIM | ID: wpr-888201

ABSTRACT

Affective brain-computer interfaces (aBCIs) has important application value in the field of human-computer interaction. Electroencephalogram (EEG) has been widely concerned in the field of emotion recognition due to its advantages in time resolution, reliability and accuracy. However, the non-stationary characteristics and individual differences of EEG limit the generalization of emotion recognition model in different time and different subjects. In this paper, in order to realize the recognition of emotional states across different subjects and sessions, we proposed a new domain adaptation method, the maximum classifier difference for domain adversarial neural networks (MCD_DA). By establishing a neural network emotion recognition model, the shallow feature extractor was used to resist the domain classifier and the emotion classifier, respectively, so that the feature extractor could produce domain invariant expression, and train the decision boundary of classifier learning task specificity while realizing approximate joint distribution adaptation. The experimental results showed that the average classification accuracy of this method was 88.33% compared with 58.23% of the traditional general classifier. It improves the generalization ability of emotion brain-computer interface in practical application, and provides a new method for aBCIs to be used in practice.


Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography , Emotions , Humans , Reproducibility of Results
5.
Article in Chinese | WPRIM | ID: wpr-879269

ABSTRACT

As a noninvasive neuromodulation technique, transcranial magnetic stimulation (TMS) is widely used in the clinical treatment of neurological and psychiatric diseases, but the mechanism of its action is still unclear. The purpose of this paper is to investigate the effects of different frequencies of magnetic stimulation (MS) on neuronal excitability and voltage-gated potassium channels in the


Subject(s)
Action Potentials , Animals , Magnetic Phenomena , Mental Disorders , Mice , Neurons , Patch-Clamp Techniques , Potassium Channels, Voltage-Gated
6.
Article in Chinese | WPRIM | ID: wpr-879263

ABSTRACT

With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer's disease, and discusses the existing problems and gives the possible development directions in order to provide some references.


Subject(s)
Alzheimer Disease/diagnostic imaging , Cognitive Dysfunction/diagnosis , Humans , Image Processing, Computer-Assisted , Magnetic Resonance Imaging , Neural Networks, Computer
7.
Article in Chinese | WPRIM | ID: wpr-888239

ABSTRACT

Transcranial magnetic stimulation (TMS) as a noninvasive neuromodulation technique can improve the impairment of learning and memory caused by diseases, and the regulation of learning and memory depends on synaptic plasticity. TMS can affect plasticity of brain synaptic. This paper reviews the effects of TMS on synaptic plasticity from two aspects of structural and functional plasticity, and further reveals the mechanism of TMS from synaptic vesicles, neurotransmitters, synaptic associated proteins, brain derived neurotrophic factor and related pathways. Finally, it is found that TMS could affect neuronal morphology, glutamate receptor and neurotransmitter, and regulate the expression of synaptic associated proteins through the expression of brain derived neurotrophic factor, thus affecting the learning and memory function. This paper reviews the effects of TMS on learning, memory and plasticity of brain synaptic, which provides a reference for the study of the mechanism of TMS.


Subject(s)
Brain , Humans , Learning , Neuronal Plasticity , Transcranial Magnetic Stimulation
8.
Article in Chinese | WPRIM | ID: wpr-828172

ABSTRACT

With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference ( < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.


Subject(s)
Brain , Physiology , Electroencephalography , Healthy Volunteers , Humans , Mental Fatigue , Virtual Reality
9.
Article in Chinese | WPRIM | ID: wpr-828156

ABSTRACT

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that has been paid attention to with increasing interests as a therapeutic neural rehabilitative tool. Studies confirmed that high-frequency rTMS could improve the cognitive performance in behavioral test as well as the excitability of the neuron in animals. This study aimes to investigate the effects of rTMS on the cognition and neuronal excitability of Kunming mice during the natural aging. Twelve young mice, 12 adult mice, and 12 aged mice were used, and each age group were randomly divided into rTMS group and control group. rTMS-treated groups were subjected to high-frequency rTMS treatment for 15 days, and control groups were treated with sham stimulation for 15 days. Then, novel object recognition and step-down tests were performed to examine cognition of learning and memory. Whole-cell patch clamp technique was used to record and analyze resting membrane potential, action potential (AP), and related electrical properties of AP of hippocampal dentate gyrus (DG) granule neurons. Data analysis showed that cognition of mice and neuronal excitability of DG granule neurons were degenerated significantly as the age increased. Cognitive damage and degeneration of some electrical properties were alleviated under the condition of high-frequency rTMS. It may be one of the mechanisms of rTMS to alleviate cognitive damage and improve cognitive ability by changing the electrophysiological properties of DG granule neurons and increasing neuronal excitability.

10.
Article in Chinese | WPRIM | ID: wpr-828136

ABSTRACT

Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.

11.
Article in Chinese | WPRIM | ID: wpr-879202

ABSTRACT

Repetitive transcranial magnetic stimulation(rTMS) is a painless and non-invasive method for stimulation and modulation in the field of cognitive neuroscience research and clinical neurological regulation. In this paper, adult Wistar rats were divided into the rTMS group and control group randomly. Rats in the rTMS group were stimulated with 5 Hz rTMS for 14 days, while the rats in the control group did not accept any stimulation. Then, the behavior and local field potentials (LFPs) were recorded synchronously when the rats perform a working memory (WM) task with T-maze. Finally, the time-frequency distribution and coherence characteristics of the LFPs signal in the prefrontal cortex (PFC) during working memory task were analyzed. The results showed that the rats in the rTMS group needed less training days to reach the task correction criterion than the control group (


Subject(s)
Animals , Memory, Short-Term , Neurons , Prefrontal Cortex , Rats , Rats, Wistar , Transcranial Magnetic Stimulation
12.
Article in Chinese | WPRIM | ID: wpr-774174

ABSTRACT

Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.


Subject(s)
Algorithms , Brain-Computer Interfaces , Discriminant Analysis , Electroencephalography , Event-Related Potentials, P300 , Humans , Support Vector Machine
13.
Article in Chinese | WPRIM | ID: wpr-773349

ABSTRACT

The possible influence of electromagnetic field (EMF) on the function of neural systems has been widely concerned. In this article, we intend to investigate the effects of long term power frequency EMF exposure on brain cognitive functions and it's mechanism. The Sprague-Dawley (SD) rats were randomly divided into 3 groups: the rats in EMF Ⅰ group were placed in the 2 mT power frequency EMF for 24 days. The rats in EMF Ⅱ group were placed in the 2 mT power frequency EMF for 48 days. The rats in control group were not exposed to the EMF. Then, the 16 channel local field potentials (LFPs) were recorded from rats' prefrontal cortex (PFC) in each group during the working memory (WM) tasks. The causal networks of LFPs were also established by applying the directed transfer function (DTF). Based on that, the differences of behavior and the LFPs network connection patterns between different groups were compared in order to investigate the influence of long term power frequency EMF exposure on working memory. The results showed the rats in the EMF Ⅱ group needed more training to reach the task correction criterion (over 80%). Moreover, the causal network connection strength and the global efficiency of the rats in EMF Ⅰ and EMF Ⅱ groups were significantly lower than the corresponding values of the control group. Meanwhile, significant differences of causal density values were found between EMF Ⅱ group and the other two groups. These results indicate that long term exposure to 2 mT power frequency EMF will reduce the connection strength and the information transfer efficiency of the LFPs causal network in the PFC, as well as the behavior performance of the rats. These results may explain the effect of EMF exposure on working memory from the view of neural network connectivity and provide a support for further studies on the mechanism of the effect of EMF on cognition.

14.
Article in Chinese | WPRIM | ID: wpr-687649

ABSTRACT

This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.

15.
Article in Chinese | WPRIM | ID: wpr-687606

ABSTRACT

The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of information, and realize the imitation of brain information processing. However, its hardware is an important way to realize its powerful computing ability, and it is also a challenging technical problem. The memristor currently is the electronic devices that functions closest to the neuron synapse, and able to respond to spike voltage in a highly similar spike timing dependent plasticity (STDP) mechanism with a biological brain, and has become a research hotspot to construct spiking neuron networks hardware circuit in recent years. Through consulting the relevant literature at home and abroad, this paper has made a thorough understanding and introduction to the research work of the spiking neuron networks based on the memristor in recent years.

16.
Article in Chinese | WPRIM | ID: wpr-687577

ABSTRACT

Mental fatigue is a subjective fatigue state caused by long-term brain activity, which is the core of health problems among brainworkers. However, its influence on the process of brain information transmission integration is not clear. In this paper, phase amplitude coupling (PAC) between theta and gamma rhythm was used to study the electroencephalogram (EEG) data recorded before and after mental fatigue, so as to explain the effect of mental fatigue on brain information transmission mechanism. The experiment used a 4-hour professional English reading to induce brain fatigue. EEG signals of 14 male undergraduate volunteers before and after mental fatigue were recorded by Neuroscan EEG system. Phase amplitude coupling value was calculated and analyzed. test was used to compare the results between two states. The results showed that theta phase of more than 90% of the electrodes in the whole brain area jointly modulated gamma amplitude of the right central area and the right parietal area, and the coupling effect among different brain regions significantly decreased ( < 0.05) when participants had felt mental fatigue. This paper shows that phase amplitude coupling can explain the influence of mental fatigue on information transmission mechanism. It could be an important indicator for mental fatigue detection. On the other hand, the results also provide a new measure to evaluate the effect of neuromodulation in relieving mental fatigue.

17.
Article in Chinese | WPRIM | ID: wpr-266732

ABSTRACT

To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.


Subject(s)
Action Potentials , Models, Neurological , Nerve Net , Neuronal Plasticity , Neurons
18.
Journal of Biomedical Engineering ; (6): 1195-1201, 2014.
Article in Chinese | WPRIM | ID: wpr-234431

ABSTRACT

In the present investigation, we studied four methods of blind source separation/independent component analysis (BSS/ICA), AMUSE, SOBI, JADE, and FastICA. We did the feature extraction of electroencephalogram (EEG) signals of brain computer interface (BCI) for classifying spontaneous mental activities, which contained four mental tasks including imagination of left hand, right hand, foot and tongue movement. Different methods of extract physiological components were studied and achieved good performance. Then, three combined methods of SOBI and FastICA for extraction of EEG features of motor imagery were proposed. The results showed that combining of SOBI and ICA could not only reduce various artifacts and noise but also localize useful source and improve accuracy of BCI. It would improve further study of physiological mechanisms of motor imagery.


Subject(s)
Algorithms , Artifacts , Brain , Physiology , Brain-Computer Interfaces , Electroencephalography , Foot , Hand , Humans , Imagination , Movement , Signal Processing, Computer-Assisted , Tongue
19.
Article in Chinese | WPRIM | ID: wpr-597968

ABSTRACT

ObjectiveTo evaluate the effect of electroencephalography(EEG) recognition rate resulted by stimulus of visual, auditory and combined perception. MethodsIn virtual traffic environment, the stimuli were designed in traffic information based on visual, auditory and audio-visual fusion. When one of the three stimuli appeared, the subjects completed starting and braking vehicle by imaginary using of right and left hands at the same time. The EEG signals were recorded and the imaginary motion-related features from C3, C4 were extracted and classified. ResultsThe best recognition rates in visual, auditory and fusion perception were 100%, 100%and 83%, respectively, while the averages of the rates were 68.8% 、82.2%、76.9%, respectively. Conclusion The recognition rates are affected by the audio, visual and fusion stimulus and apparent individual differences exist.

20.
Article in Chinese | WPRIM | ID: wpr-535886

ABSTRACT

Objective To investigate causes for Kojewnikow syndrome,by observing its clinical characteristics,electroencephalography and findings in imaging scanning. Methods Twelve patients with Kojewnikow syndrome were accessed with clinical observation, electroencephalography and imaging scanning. Results Kojewnikow syndrome is clinically charaterized by secouse twitching and simple partialis motor continua.Viral encephalitis is by far the most of common cause for Kojewnikow syndrome, followed by meningo-encephalitis,cerebral glioma,cerebral cysticercosis, cerebral infarction, diabetes, and cryptogenic epilepsyKojewnikow syndrome tends to occur in watershed area. Conclusions Kojewnikow syndrome falls into two main groups.It is important to improve the diagnosis and find the causes for Kojewnikow and to access its clinical manifestations, electroencephalography and findings in the imaging scanning.The seizures cannot be readily controlled by the anticonvulsants,so the prognosis is poor.

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