A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.     

相空间重构与神经网络融合预测模型及其在害虫测报中的应用

针对混沌时间序列,建立了相空间重构和BP神经网络融合预测模型,经实例验证,该方法能有效地提高预测精度,避免了一般混沌预测要求较长时间序列的缺陷,为害虫的可预测性提供了一种新的依据。     

Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.     

混沌理论及其在建立神经网络模型中的应用

随着许多学科的相互紧密交叉以及混沌理论的日益深入的研究,人们从生物现象中提出了许多与混沌有关的神经网络模型,本文对混沌理论的基本原理做了简要概述,并着重介绍了四种有代表性的混沌神经网络模型及其应用．同时指出这一研究方向无论在理论还是在应用方面都具有十分诱人的前景．     

放射治疗网络系统的设计和应用

目的:研发放射治疗计划和放射治疗信息管理系统。方法:放疗网络采用客户机服务器模式,Oracle 9i为数据库服务器;使用PowerBuilder9i为编程语言进行开发。结果:该系统包括医生模块(放射治疗计划模块)、技术员模块、物理师模块、放射治疗电子病历查询和统计模块、系统管理模块五个部分。结论:该系统运行稳定,数据安全可靠,操作简单,可作为科室信息化建设的重要组成部分。     

生物神经系统的编码特性和小世界特性

本文主要研究视网膜神经系统和七鳃鳗脊椎神经系统的电位发放特性和网络特性,首先利用抑制神经系统的Winner Less Competition（WLC）模型,分析视网膜和七鳃鳗脊椎神经系统的电位发放．得到视网膜神经元和脊椎神经元的电位发放模式．然后利用Watts-Strogatz小世界网络的特性,分析两个生物神经系统的群集系数和特征路长,说明这些生物系统神经元之间的信息传递具有小世界网络的特性．     

Optimum DNA Curvature Using a Hybrid Approach Involving an Artificial Neural Network and Genetic Algorithm

Abstract

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the R_L value. Using the ANN-GA methodology, a number of sequences possessing high R_L values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high R_L values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

扫描介电成像技术及其在生物体系的应用

通过实例介绍了近一二年新开发的一种扫描探针显微技术——扫描介电显微镜(SDM)的基本原理、测量系统的构造和特点,并扼要地展望了它在生物体系及医学上的应用潜力.     

BP神经网络在农产品生产与检测中的应用

人工神经网络是人工智能领域中发展迅速的信息处理技术之一,充分发挥人工神经网络的技术优势,是在农业领域内实现生产劳动自动化的重要途径．本文对BP网络模型及其算法进行了分析研究,从农产品的外观评判、生产预测建模和分类分级鉴定等方面综述了国内外最新研究进展,并展望了今后的应用前景。     

Integrating Band Structure Engineering with All‐Scale Hierarchical Structuring for High Thermoelectric Performance in PbTe System

PbTe_1?x Se_x ‐2%Na‐y%SrTe system is investigated and a high maximum ZT of 2.3 at 923 K for PbTe_0.85Se_0.15‐2%Na‐4%SrTe is reported. This is achieved by performing electronic band structures modifications as well as all‐scale hierarchical structuring and combining the two effects. It is found that high ZTs in PbTe_0.85Se_0.15‐2%Na‐4%SrTe are possible at all temperature from 300 to 873 K with an average ZT_ave of 1.23. The high performance in PbTe_1?x Se_x ‐2%Na‐y%SrTe can be achieved by either choosing PbTe‐2Na‐4SrTe or PbTe_0.85Se_0.15‐2Na as a matrix. At room temperature the carrier mobility shows negligible variations as SrTe fraction is increased, however the lattice thermal conductivity is significantly reduced from ≈1.1 to ≈0.82 W m^?1 K^?1 when 5.0% SrTe is added, correspondingly, the lattice thermal conductivity at 923 K decreases from ≈0.59 to ≈0.43 W m?¹ K^?1. The power factor maxima of PbTe_1?x Se_x ‐2Na‐4SrTe shift systematically to higher temperature with rising Se fractions due to bands divergence. The maximum power factors reach ≈27, ≈30, ≈31 μW cm^?1 K^?2 for the x = 0, 0.05, and 0.15 samples peak at 473, 573, and 623 K, respectively. The results indicate that ZT can be increased by synergistic integration of band structure engineering and all‐scale hierarchical architectures.     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.     

Slow Noise in the Period of a Biological Oscillator Underlies Gradual Trends and Abrupt Transitions in Phasic Relationships in Hybrid Neural Networks

In order to study the ability of coupled neural oscillators to synchronize in the presence of intrinsic as opposed to synaptic noise, we constructed hybrid circuits consisting of one biological and one computational model neuron with reciprocal synaptic inhibition using the dynamic clamp. Uncoupled, both neurons fired periodic trains of action potentials. Most coupled circuits exhibited qualitative changes between one-to-one phase-locking with fairly constant phasic relationships and phase slipping with a constant progression in the phasic relationships across cycles. The phase resetting curve (PRC) and intrinsic periods were measured for both neurons, and used to construct a map of the firing intervals for both the coupled and externally forced (PRC measurement) conditions. For the coupled network, a stable fixed point of the map predicted phase locking, and its absence produced phase slipping. Repetitive application of the map was used to calibrate different noise models to simultaneously fit the noise level in the measurement of the PRC and the dynamics of the hybrid circuit experiments. Only a noise model that added history-dependent variability to the intrinsic period could fit both data sets with the same parameter values, as well as capture bifurcations in the fixed points of the map that cause switching between slipping and locking. We conclude that the biological neurons in our study have slowly-fluctuating stochastic dynamics that confer history dependence on the period. Theoretical results to date on the behavior of ensembles of noisy biological oscillators may require re-evaluation to account for transitions induced by slow noise dynamics.     

A Novel Hybrid Classification Model of Genetic Algorithms,Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher''s discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.     

BP人工神经网络在光谱定量预测中的应用

人工神经网络是模仿大脑神经元网络结构和功能而建立的一种信息处理系统,广泛的应用于各种波谱数据处理。误差反向传播多层前馈式网络(back-propagation network,简称BP网络)应用最广,发展最为迅速。将BP神经网络用于紫外-可见吸收光谱和拉曼光谱数据的定量分析和预测,与原文的一元线性回归模型数据处理方法相比,获得了比较满意的预测结果,预测精度有显著提高。这为相关的光谱分析和数据处理提供了一种更有效、更精确的方法。     

Evolution of the VEGF-Regulated Vascular Network from a Neural Guidance System

The vascular network is closely linked to the neural system, and an interdependence is displayed in healthy and in pathophysiological responses. How has close apposition of two such functionally different systems occurred? Here, we present a hypothesis for the evolution of the vascular network from an ancestral neural guidance system. Biological cornerstones of this hypothesis are the vascular endothelial growth factor (VEGF) protein family and cognate receptors. The primary sequences of such proteins are conserved from invertebrates, such as worms and flies that lack discernible vascular systems compared to mammals, but all these systems have sophisticated neuronal wiring involving such molecules. Ancestral VEGFs and receptors (VEGFRs) could have been used to develop and maintain the nervous system in primitive eukaryotes. During evolution, the demands of increased morphological complexity required systems for transporting molecules and cells, i.e., biological conductive tubes. We propose that the VEGF–VEGFR axis was subverted by evolution to mediate the formation of biological tubes necessary for transport of fluids, e.g., blood. Increasingly, there is evidence that aberrant VEGF-mediated responses are also linked to neuronal dysfunctions ranging from motor neuron disease, stroke, Parkinson’s disease, Alzheimer’s disease, ischemic brain disease, epilepsy, multiple sclerosis, and neuronal repair after injury, as well as common vascular diseases (e.g., retinal disease). Manipulation and correction of the VEGF response in different neural tissues could be an effective strategy to treat different neurological diseases.     

Neural Mechanisms Underlying the Computation of Hierarchical Tree Structures in Mathematics

Whether mathematical and linguistic processes share the same neural mechanisms has been a matter of controversy. By examining various sentence structures, we recently demonstrated that activations in the left inferior frontal gyrus (L. IFG) and left supramarginal gyrus (L. SMG) were modulated by the Degree of Merger (DoM), a measure for the complexity of tree structures. In the present study, we hypothesize that the DoM is also critical in mathematical calculations, and clarify whether the DoM in the hierarchical tree structures modulates activations in these regions. We tested an arithmetic task that involved linear and quadratic sequences with recursive computation. Using functional magnetic resonance imaging, we found significant activation in the L. IFG, L. SMG, bilateral intraparietal sulcus (IPS), and precuneus selectively among the tested conditions. We also confirmed that activations in the L. IFG and L. SMG were free from memory-related factors, and that activations in the bilateral IPS and precuneus were independent from other possible factors. Moreover, by fitting parametric models of eight factors, we found that the model of DoM in the hierarchical tree structures was the best to explain the modulation of activations in these five regions. Using dynamic causal modeling, we showed that the model with a modulatory effect for the connection from the L. IPS to the L. IFG, and with driving inputs into the L. IFG, was highly probable. The intrinsic, i.e., task-independent, connection from the L. IFG to the L. IPS, as well as that from the L. IPS to the R. IPS, would provide a feedforward signal, together with negative feedback connections. We indicate that mathematics and language share the network of the L. IFG and L. IPS/SMG for the computation of hierarchical tree structures, and that mathematics recruits the additional network of the L. IPS and R. IPS.     

A Pilot Study of Youth With Type 1 Diabetes Initiating Use of a Hybrid Closed-Loop System While Receiving a Behavioral Economics Intervention

ObjectiveMany youth do not use the hybrid closed-loop system for type 1 diabetes effectively. This study evaluated the impact of financial incentives for diabetes-related tasks on use of the 670G hybrid closed-loop system and on glycemia.MethodsAt auto mode initiation and for 16 weeks thereafter, participants received a flat rate for wearing and calibrating the sensor ($1/day), administering at least 3 mealtime insulin boluses per day ($1/day), and uploading ($5/week). Weekly bonuses were given for maintaining at least 70% of the time in auto mode, which were increased for persistent auto mode use from $3/week to a maximum of $13/week. If a participant failed to maintain auto mode for a week, the rewards were reset to baseline. Data from 17 participants aged 15.9 years ± 2.5 years (baseline hemoglobin A1c [HbA1c] 8.6% ± 1.1%) were collected at 6, 12, and 16 weeks. The reinforcers were withdrawn at 16 weeks, with a follow-up assessment at 24 weeks.ResultsWith reinforcers, the participants administered an average of at least 3 mealtime insulin boluses per day and wore the sensor over 70% of the time. However, auto mode use waned. HbA1c levels decreased by 0.5% after 6 weeks, and this improvement was maintained at 12 and 16 weeks (P < .05). Upon withdrawal of reinforcers, HbA1c levels increased back to baseline at 24 weeks.ConclusionCompensation for diabetes-related tasks was associated with lower HbA1c levels, consistent administration of mealtime insulin boluses, and sustained sensor use. These results support the potential of financial rewards for improving outcomes in youth with type 1 diabetes.     

Synthesis and Biological Evaluation of Some Succinimide Hybrid Molecules

Russian Journal of Bioorganic Chemistry - In this study, a new series of succinimide hybrid molecules containing isothiocyanate, coumarin, isatin, and furan moieties was synthesized and screened...