The dual effect of L-proline on spreading depression in the chicken retina

Evidence was presented for a glutamate agonistic effect of L-proline which promotes K+-based spreading depressions (SD) in chick retinas at relatively high concentrations (5 mM), in addition to an antagonistic effect which inhibits glutamate-based SDs at lower (2 mM) concentrations. Together these effects explain the observed biphasic effect of L-proline on the incidence of SD in the retina.     

Neural dynamics in cortex-striatum co-cultures-II. Spatiotemporal characteristics of neuronal activity

Neural dynamics in organotypic cortex-striatum co-cultures grown for three to six weeks under conditions of dopamine deficiency are described. Single neuron activities were recorded intra- and extracellularly, and spatiotemporal spreading of population activity was mapped using voltage-sensitive dyes. The temporal properties of spike firing were characterized by interspike interval histograms, autocorrelation and crosscorrelation.

Cortical pyramidal neurons (n = 40) showed irregular firing with a weak tendency to burst or to oscillate. Crosscorrelations revealed strong near-coincident firing and synaptic interactions. Disinhibition was a notable feature in a strongly firing cortical interneuron. Cortical activity spread in the co-culture, thus inducing an overall, homogeneous depolarization in the striatal part. Striatal cells were divided into principal cells and type I and II secondary cells. Principal cells (n = 40) were similar to those reported previously in vivo. Spiking activity ranged from irregular spiking at very low rates to episodic bursting, with an average burst duration of 1 s. Interspike intervals were single-peaked. Intracellular recordings revealed characteristic, long-lasting subthreshold depolarizations (“enabled state”) that were shortened by local muscarinic receptor blockade. During prolonged time periods in the “enabled state”, locally applied bicuculline induced strong firing in most principal neurons. Striatal secondary type I neurons (n = 25) showed high spiking rates, single- and double-peaked interval histograms and low-threshold, short-lasting stereotyped bursting activity and occasional rhythmic bursting. The firing of these neurons was increased by bicuculline. Crosscorrelations showed synchronization of these cells with principal cell activity. Secondary type II neurons (n = 15) revealed tonic, irregular firing patterns similar to cortical neurons, except with occasional firing in doublet spikes.

We conclude that under conditions of dopamine deficiency in corticostriatal co-cultures (i) the cortex induces the “enabled” state and typical bursting mode in striatal principal neurons; (ii) principal neurons are strongly inhibited during the “enabled” state; (iii) muscarinic activity, presumably from tonically active striatal cholinergic interneurons, stabilizes the “enabled” state; (iv) striatal GABAergic interneurons receive synaptic inhibition and take part in synchronized activity among striatal principal cells. Our results favor the view of the striatum as a lateral inhibition network.     

Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface

The paper presents an adaptive noise canceller (ANC) filter using an artificial neural network for real-time removal of electro-oculogram (EOG) interference from electro-encephalogram (EEG) signals. Conventional ANC filters are based on linear models of interference. Such linear models provide poorer prediction for biomedical signals. In this work, a recurrent neural network was employed for modelling the interference signals. The eye movement and eye blink artifacts were recorded by the placing of an electrode on the forehead above the left eye and an electrode on the left temple. The reference signal was then generated by the data collected from the forehead electrode being added to data recorded from the temple electrode. The reference signal was also contaminated by the EEG. To reduce the EEG interference, the reference signal was first low-pass filtered by a moving averaged filter and then applied to the ANC. Matlab Simulink was used for real-time data acquisition, filtering and ocular artifact suppression. Simulation results show the validity and effectiveness of the technique with different signal-to-noise ratios (SNRs) of the primary signal. On average, a significant improvement in SNR up to 27 dB was achieved with the recurrent neural network. The results from real data demonstrate that the proposed scheme removes ocular artifacts from contaminated EEG signals and is suitable for real-time and short-time EEG recordings.     

用于选择视觉的数据和智能控制神经网络系统(英文)

在这篇论文中,我们提出了用于选择视觉的数据和智能控制的动态网络系统的神经实现过程。模型由数个相互作用的子系统构成,用于不同的处理。所有的神经子系统与信息和控制流程的倒序和顺序紧密相关。     

Visuomotor control within a distributed parieto-frontal network

The aim of this functional magnetic resonance imaging study was to investigate differences in visuomotor control with increasing task complexity. Twelve right-handed volunteers were asked to perform their signature under different degrees of visual control: internally generated movement with closed eyes, signing with open eyes, tracking the line of the projected signature forwards, and tracking the line of the projected signature backwards. There was a gradual onset and disappearance of activation within a distributed network. Parietal, lateral and medial frontal brain areas were activated during all conditions, confirming the involvement of a parieto-frontal system. The weight of activation shifted with increasing task complexity. Internally generated movements activated predominantly the inferior parietal lobule and the ventral premotor cortex, as well as the rostral cingulate area, pre-supplementary motor area (pre-SMA) and SMA proper. Opening the eyes reduced SMA and cingulate activation and activated increasingly the occipito-parietal areas with higher task complexity. Visually guided movements produced an activation predominantly in the superior parietal lobule and dorsal premotor cortex. This study bridges human activation studies with the results of neurophysiological studies with monkeys. It confirms a gradual transition of visuomotor control with increasing task complexity within a distributed parieto-frontal network.     

基于自适应小波神经网络的心电图检测法

自适应小波神经网络检测法就是利用小波函数取代通常神经网络中隐层的作用函数来实现的，通过网络的学习自适应地调整尺度参数和时移因子，提高了特征提取能力，因此，该网络可提高心电信号的检测率和可靠性。     

基于边界特征的乳腺肿瘤超声图像识别

通过对乳腺肿瘤边界特征的分析，得到边界的特征量似圆度，面积比率，长宽比组成的特征矢量，最后用反向传播人工神经网络（BP）的算法对经病理证实的119幅乳腺良、恶性肿块超声图像进行分类识别。BP神经网络对良、恶性肿瘤正确识别率分别为89．7％、73．5N。量化后的乳腺超声图像肿瘤轮廓特征结合BP神经网络可以比较有效的用于肿瘤的良、恶性识别。     

Normal aging decreases regional homogeneity of the motor areas in the resting state

Our knowledge about aging modulation of the central motor system remains sparse and contradictory. In the current study, we used functional MRI (fMRI) to study the aging influence on regional homogeneity of the motor-related brain areas in the resting state. We found that regional homogeneity in extensive motor regions, like the cingulate motor area, cerebellum, primary motor cortex, premotor area, supplementary motor area, thalamus, globus pallidus and putamen was significantly decreased in aged subjects. Our study indicates that normal aging process may disrupt the function of motor areas in the resting state, which may contribute to the declined motor ability in aged population.     

Detection of ophthalmic arterial doppler signals with Behcet disease using multilayer perceptron neural network

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 106 subjects, 54 of whom suffered from ocular Behcet disease while the rest were healthy subjects. Multilayer perceptron neural network (MLPNN) employing delta-bar-delta training algorithm was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by least squares (LS) autoregressive (AR) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 96.43% for healthy subjects and 93.75% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the MLPNN employing delta-bar-delta training algorithm was effective to detect the ophthalmic arterial Doppler signals with Behcet disease.