CDK5-dependent inhibitory phosphorylation of Drp1 during neuronal maturation

Mitochondrial functions are essential for the survival and function of neurons. Recently, it has been demonstrated that mitochondrial functions are highly associated with mitochondrial morphology, which is dynamically changed by the balance between fusion and fission. Mitochondrial morphology is primarily controlled by the activation of dynamin-related proteins including dynamin-related protein 1 (Drp1), which promotes mitochondrial fission. Drp1 activity is regulated by several post-translational modifications, thereby modifying mitochondrial morphology. Here, we found that phosphorylation of Drp1 at serine 616 (S616) is mediated by cyclin-dependent kinase 5 (CDK5) in post-mitotic rat neurons. Perturbation of CDK5 activity modified the level of Drp1^S616 phosphorylation and mitochondrial morphology in neurons. In addition, phosphorylated Drp1^S616 preferentially localized as a cytosolic monomer compared with total Drp1. Furthermore, roscovitine, a chemical inhibitor of CDKs, increased oligomerization and mitochondrial translocation of Drp1, suggesting that CDK5-dependent phosphorylation of Drp1 serves to reduce Drp1''s fission-promoting activity. Taken together, we propose that CDK5 has a significant role in the regulation of mitochondrial morphology via inhibitory phosphorylation of Drp1^S616 in post-mitotic neurons.     

一种改进的Jacobi正交多项式的BP神经网络算法

 根据多项式理论,构造一种以Jacobi正交多项式作为隐层神经元激励函数的BP(back-propagation)神经网络模型.针对该网络,提出一种改进算法即隐层神经元数可快速确定的权值直接确定算法.首先介绍正交基函数和Jacobi多项式的定义,以及BP神经网络的基本原理.然后进行网络隐层数设计及其隐神经元数的确定,且设置各层连接权值、给出改进算法的步骤.最后,将其与传统矩阵迭代法和Levenberg-Marquardt训练算法进行比较.计算机实验结果表明,该算法具有比传统的BP迭代法更快的计算速度,并且能够达到更高的工作精度.     

新生大鼠大脑皮层和海马神经元体外培养方法及改进

探索用24 h内新生SD大鼠进行神经元体外培养的方法.取新生SD大鼠的大脑皮层和海马,胰酶消化5～10 min后,放入含100 g/L血清的DMEM/F12培养基中培养.24 h后,更换含B27无血清培养基.以后每隔1d,半换液.结果显示绝大部分神经元于第2天长出突起,第3天神经元间形成复杂的联系,第7天神经元发育基本成熟.经神经元特异性烯醇酶(NSE)鉴定,培养细胞均为神经元,纯度可达90%以上,可以用于实验.     

基于粗糙-模糊神经网络的焊接图像缺陷识别

针对焊接图像缺陷识别中提取的特征受噪声干扰比较严重以及现有的识别算法准确率低的问题,提出了一种基于粗糙模糊神经网络的缺陷识别算法。该算法充分利用了粗糙集的属性约简、模糊集的处理不精确数据以及神经网络的自学习、对任意函数逼近的优点,有效地解决了不确定建模过程中样本数据受到噪声干扰、模型结构难以确定的问题。仿真结果表明:该算法能有效地提高焊缝图像的缺陷识别能力。     

FitzHugh-Nagumo神经元网络的同步振荡与联想记忆

本文研究由FitzHugh—Nagumo神经元所组成的脉动神经元网络的同步与联想记忆恢复。基于神经元微观生理结构，本文给出具有空间随机分布延时的神经元间耦合，而这种随机分布延时描述了脉动信号从突触前神经元到突触后神经元在轴突上传播所需要的时间。记忆由空时发放的神经元集群表达，在噪声涨落的作用下，系统取得了对不完整输入的记忆恢复。     

基于改进型BP神经网络的无刷直流电机调速系统的应用研究

针对无刷直流电动机的速度问题,提出了一种基于改进型BP神经网络的无刷直流电机调速系统,即利用改进型BP神经网络来优化PID控制器的比例,积分,微分系数。所采用的BP神经网络的学习算法为粒子群算法,可以有效克服标准BP算法一般所存在收敛速度慢、存在局部极小值等问题,从而可以实现对无刷直流电动机的高精度的速度控制。最后,建立了仿真系统,其结果表明该算法效果良好。     

基于模糊神经元PID算法的注塑机注射速度控制算法研究

基于一款市场较为畅销的注塑机, 设计出一种能精确控制注射速度的模糊神经元PID控制器. 首先, 设计出具有自学能力的神经元PID控制器, 利用模糊算法对其进行优化; 其次, 在原有注射速度线性数学模型的基础上, 构建注塑机注射速度的非线性模型; 最后, 利用MATLAB在所建数学模型的基础上对模糊神经元PID控制器进行仿真实验. 实验结果表明, 所设计控制器具有响应迅速、无超调量、控制精度高、控制稳定等优点.     

简谐噪声激励下FitzHugh-Nagumo神经元的动力学行为

研究了简谐噪声激励下的FitzHugh-Nagumo神经元模型, 其放电形式、相干共振等动力学行为均受噪声阻尼参数和频率参数的影响.选择不同的参数所得到的神经元的放电形式不同.神经元存在共振特性,对某一频率的噪声有更强的响应,在此频率参数下的峰序列更有序,出现相干共振系数的极小值.噪声的阻尼参数越大,不同的频率成分越多,神经元的响应也变得杂乱,进而导致神经元与噪声的同步变弱,峰序列相干共振系数也相应增大. 关键词： 简谐噪声 FitzHugh-Nagumo神经元 相干共振 峰峰间隔     

Spiking patterns of a hippocampus model in electric fields

We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective. Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study. The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity. It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field. Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude. These findings are qualitatively in accordance with the results of relevant experimental and numerical studies. It is implied that the external or endogenous electric field can modulate the neural code in the brain. Furthermore, it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy.