Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current （AC） stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.

Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.

Data-Driven Modeling of Partially Observed Biological Systems

We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.

Simulation of Low TDS and Biological Units of Fajr Industrial Wastewater Treatment Plant Using Artificial Neural Network and Principal Component Analysis Hybrid Method

Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.

Kinetic model of vibration screening for granular materials based on biological neural network

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis

The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient,called ANNs-SCG.The fractional derivatives have been applied to get more reliable performances of the system.The mathematical form of the biological Leptospirosis system is divided into five categories,and the numerical performances of each model class will be provided by using the ANNs-SCG.The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results.The reference solutions have been obtained by using theAdams numerical scheme.For these investigations,the data selection is performed at 82%for training,while the statics for both testing and authentication is selected as 9%.The procedures based on the recurrence,mean square error,error histograms,regression,state transitions,and correlation will be accomplished to validate the fitness,accuracy,and reliability of the ANNs-SCG scheme.

Lateral interaction by Laplacian‐based graph smoothing for deep neural networks

Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be used.Some approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor extendibility.The efficient way to implement lateral interaction in deep neural networks is not well established.The use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise form.The authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving capability.The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders.Additionally,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated.Furthermore,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks.Overall,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

Model Identification of Water Purification Systems Using RBF Neural Network

Aim The RFB (radial hats function) netal network was studied for the model indentificaiton of an ozonation/BAC system. Methods The optimal ozone's dosage and the remain time in carbon tower were analyzed to build the neural network model by which the expected outflow CODM can be acquired under the inflow CODM condition. Results The improved self-organized learning algorithm can assign the centers into appropriate places , and the RBF network's outputs at the sample points fit the experimental data very well. Conclusion The model of ozonation /BAC system based on the RBF network am describe the relationshipamong various factors correctly, a new prouding approach tO the wate purification process is provided.

Small-worldness of brain networks after brachial plexus injury: a resting-state functional magnetic resonance imaging study

Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may help understanding of brain plasticity at the global level.We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury.Thus,in this cross-sectional study,we recruited eight male patients with unilateral brachial plexus injury（right handedness,mean age of 27.9±5.4years old）and eight male healthy controls（right handedness,mean age of 28.6±3.2）.After acquiring and preprocessing resting-state magnetic resonance imaging data,the cerebrum was divided into 90 regions and Pearson’s correlation coefficient calculated between regions.These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1–0.46.Under sparsity conditions,both groups satisfied this small-world property.The clustering coefficient was markedly lower,while average shortest path remarkably higher in patients compared with healthy controls.These findings confirm that cerebral functional networks in patients still show smallworld characteristics,which are highly effective in information transmission in the brain,as well as normal controls.Alternatively,varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.

Coherence Resonance and Noise-Induced Synchronization in Hindmarsh-Rose Neural Network with Different Topologies

In this paper, we investigate coherence resonance （CR） and noise-induced synchronization in Hindmarsh- Rose （HR） neural network with three different types of topologies： regular, random, and small-world. It is found that the additive noise can induce CR in HR neural network with different topologies and its coherence is optimized by a proper noise level. It is also found that as coupling strength increases the plateau in the measure of coherence curve becomes broadened and the effects of network topology is more pronounced simultaneously. Moreover, we find that increasing the probability p of the network topology leads to an enhancement of noise-induced synchronization in HR neurons network.

基于注意力机制的鸟类识别算法

针对现有细粒度鸟类目标识别算法准确率不高的问题,提出一种鸟类目标检测算法YOLOv5-Bird。首先,在YOLOv5主干网络中引入基于混合域的坐标注意力(CA)机制,增大有价值的通道权重,以区分目标特征和背景中的冗余特征;其次,在原始主干网络中采用双层路由注意力(BRA)模块替换原网络中的部分C3模块,过滤低相关度的键值对信息,获得高效的长距离依赖关系;最后,使用WIoU(Wise-Intersection over Union)损失函数,增强算法对目标的定位能力。实验结果表明,YOLOv5-Bird在自建数据集上取得了82.8%的精确率和77.0%的召回率,比YOLOv5算法分别提高4.3和7.6个百分点,也优于增加其他注意力机制的算法。验证了YOLOv5-Bird在鸟类目标检测场景中具有较好的性能。

移动机器人全覆盖路径的BINN-元胞自动机规划

为了实现机器人对工作区域的全覆盖,提出了基于生物激励神经网络-元胞自动机系统的全覆盖路径规划方法。介绍了生物激励神经网络算法的基本原理,分析了该算法在机器人陷入死区时无法逃逸的问题。基于元胞自动机系统设计了机器人逃逸机制,包括逃逸点的确定和逃逸路径的规划方法。在仿真环境下,将元胞系统逃逸机制与基本RRT、文献[10]的BINN-RRT逃逸机制进行对比,结果表明元胞系统逃逸机制的规划时间比基本RRT小2个数量级,比BINN-RRT小1个数量级,且逃逸路径短于另外两种方法,验证了元胞系统逃逸机制的有效性和优越性。基于BINN和元胞系统的全覆盖路径比BINN-RRT规划路径更加平滑,验证了全覆盖方法的优越性和有效性。

基于IWOA-PNN模型的生物组织变性识别方法

为了提高高强度聚焦超声(HIFU)治疗过程中生物组织变性识别率,提出了一种基于改进鲸鱼优化算法优化概率神经网络(IWOA-PNN)模型的生物组织变性识别方法。首先通过改进收敛因子和加入自适应权重因子提高WOA优化算法的寻优速度和精度,然后利用IWOA算法优化PNN的平滑因子,以提高变性识别精度,最后以超声回波信号多尺度熵为特征参数输入IWOA-PNN模型,得出生物组织变性识别率。实验结果表明,与普通PNN和WOA-PNN模型相比,基于IWOA-PNN模型的生物组织变性识别率更高,更能精确地识别HIFU治疗过程中生物组织是否变性,指导临床医生进行准确的HIFU疗效评价。

基于深度学习的生物组织病理图像分析在海洋监测中的发展潜力及案例分析

生物组织病理指标可用于评价海洋生物健康,但在应用中存在效率低、成本高、主观性强等缺陷。将人工智能技术引入生物组织病理分析,可以发挥其高通量的图像分析优势,突破其在海洋生物健康评价和监测中的应用限制。该文通过对海洋生物组织健康评价指标、人工智能技术的图像分析应用以及利用人工智能开展组织病理图像处理的文献调研,提出基于深度学习的海洋动物组织病理图像分析思路,并以海洋贻贝作为模式生物进行技术开发。经过对贻贝鳃组织病理影像数据的训练、验证和预测等过程,确定Res-UNet深度学习模型可对贻贝在典型环境污染物胁迫下的病理损伤进行高效、准确定量,构建了一种能够自动化、高通量和弱主观性地分析海洋贻贝组织病理影像的工作流程,为海洋生物健康评价、海洋监测提供新思路与新技术。

光电忆阻器用于突触仿生领域的研究进展

存算分离的传统计算模式已经无法满足信息爆炸时代对大数据处理的需求,因此,基于类神经网络的神经形态计算被广泛应用于人工智能的研究。随着集成技术的进步,新形态硬件系统开始进入大众视野,忆阻器作为新兴的除电容、电感和电阻之外的第四种基本电路元件,综合了光电子学、半导体科学等多领域的优点,能够为神经形态计算硬件化提供新的思路。本文首先简述了光电忆阻器件的基本结构、机制和脉冲时间依赖可塑性等类生物突触的功能;其次介绍了四种光电型和全光型忆阻器件;然后综述了光电忆阻器件的类脑特性,如模拟巴甫洛夫经典实验和味觉厌恶过程的联想式学习、习惯化和敏化模式的非联想式学习、具有“学习-遗忘-再学习”特征的经验学习,以及结合人工神经网络实现图像记忆、处理和识别等仿生功能;最后总结了光电忆阻器件在突触仿生领域所面临的挑战,并展望其在神经形态计算方向的广阔应用前景。

基于遗传-卷积神经网络算法的废水处理预测模型研究

在废水生物处理过程建立出水COD与出水SS的预测模型中,针对卷积神经网络在设计时没有规律遵循并很难保证网络最优化的问题,提出了一种基于遗传算法降维的卷积神经网络优化方法｡本文将遗传算法(GA)与卷积神经网络(CNN)耦合起来形成一种新颖的混合算法--GA-CNN算法,并将该算法与CNN算法和BP神经网络的预测效果进行对比｡仿真结果表明,对于出水COD的浓度预测,GA-CNN的预测性能相比于CNN提升了13.66%,相比于BP提升了19.40%,其中GA-CNN算法的最优预测效果如下:均方根误差(RMSE)为3.5303,平均绝对百分比误差(MAPE)为3.92%,决定系数(R^(2))为0.7195。对于出水SS的浓度预测,GA-CNN的预测性能相比于CNN提升了9.26%,相比于BP提升了13.43%,其中GA-CNN算法的最优预测效果如下:均方根误差(RMSE)为0.5883,平均绝对百分比误差为1.99%,决定系数(R^(2))为0.6770。

Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy

Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.

基于多普勒特征的蝙蝠生物声呐环境识别及其水声场景应用

蝙蝠生物声呐仿生技术结合深度学习算法为智能信号处理领域带来了许多新应用和研究方向。飞行蝙蝠能利用回声与环境多普勒信息感知环境并识别目标,并以此为基础进行捕食、导航与交流等活动。通过研究飞行蝙蝠使用回声和多普勒信息感知环境和识别目标的生物机理,可以为人类创造更加高效、精确的探测和识别系统奠定基础。文中从飞行蝙蝠生物声呐感知系统的基本原理入手,建立了蝙蝠生物声呐飞行状态的杂波环境数学模型。针对发射恒频信号的蝙蝠,提出了一种基于时频特征平面与卷积神经网络的环境场景识别方法。通过时频分析获取杂波环境的多普勒与距离维特征,采用卷积神经网络从大量数据中高效地提取特征,根据不同方向杂波的多普勒与距离维差异实现环境识别。本研究通过计算机仿真对蝙蝠飞行过程中具备的环境分类与感知能力给出了数值模拟说明。仿真结果表明,所提算法具有良好的分类识别能力,且信噪比为30 dB时,对不同杂波环境的分类准确率可达96%以上。同时分析了飞行蝙蝠生物声呐环境识别技术在水声领域的应用,模拟海港口岸建立了水下声呐混响环境模型,并针对水下潜航器等运动平台环境类型识别进行了性能仿真验证。结果表明,所提模型与分类识别算法取得了良好的分类识别效果。

羧酸类ALR2抑制剂生物活性的神经网络模拟研究

为了研究和开发醛糖还原酶抑制剂,从而设计高效抗糖尿病并发症的新药,采用拓扑理论计算了以112个羧酸类衍生物作为有潜力的ALR2抑制剂分子的电拓扑状态指数(Ei)和电性距离矢量(Mj),通过最佳变量子集回归的方法建立了这112个化合物生物活性(pIC50)的六元(M_(62),E_(13),M_(63),M_(14),E_(32),E_(42))QSAR模型,并以这6个参数作为输入层构建6∶9∶1的人工神经网络反向传播(BP)算法模型.该模型的相关系数由多元线性归回的0.859提升为0.979,预测能力由平均相对误差0.366降为0.119.通过对模型的6个参数进行分析,找出影响112个ALR2抑制剂分子生物活性的结构片段为—C—、—N—、>S<、—OH、—X,并以此设计了5个具有较高活性的分子.该研究为设计高效抗糖尿病并发症的新药提供了理论基础.

海水有机污染物对海洋生物种群变化的影响研究

有机污染物极大影响海洋生物种群结构,为此,深入探究两者间的相互作用与影响。运用拓扑指数划分有机污染物原子结构特征,创建定量结构-生物富集因子解析式,使用误差反向传输人工神经网络,构建有机污染物生物富集;并通过吸收演变算子与毒素演变算子量化有机污染物对海洋生物的不良影响,组建海洋生物种群变化函数。以厦门东海鱼类和浮游植物为例进行实验分析,得出适当浓度有机污染物会加速浮游植物生长速率,但浓度过高会使植物种群数量大幅降低。