Extending cable theory to heterogeneous dendrites

Dendrites may exhibit many types of electrical and morphological heterogeneities at the scale of a few micrometers. Models of neurons, even so-called detailed models, rarely consider such heterogeneities. Small-scale fluctuations in the membrane conductances and the diameter of dendrites are generally disregarded and spines merely incorporated into the dendritic shaft. Using the two-scales method known as homogenization, we establish explicit expressions for the small-scale fluctuations of the membrane voltage, and we derive the cable equation satisfied by the voltage when these fluctuations are averaged out. This allows us to rigorously establish under what conditions a heterogeneous dendrite can be approximated by a homogeneous cable. We consider different distributions of synapses, orderly or random, on a passive dendrite, and we investigate when replacing excitatory and inhibitory synaptic conductances by their local averages leads to a small error in the voltage. This indicates in which regimes the approximations made in compartmental models are justified. We extend these results to active membranes endowed with voltage-dependent conductances or NMDA receptors. Then we examine under which conditions a spiny dendrite behaves as a smooth dendrite. We discover a new regime where this holds true, namely, when the conductance of the spine neck is small compared to the conductance of the synapses impinging on the spine head. Spines can then be taken into account by an effective excitatory current, the capacitance of the dendrite remaining unchanged. In this regime, the synaptic current transmitted from a spine to the dendritic shaft is strongly attenuated by the weak coupling conductance, but the total current they deliver can be quite substantial. These results suggest that pedunculated spines and stubby spines might play complementary roles in synaptic integration. Finally, we analyze how varicosities affect voltage diffusion in dendrites and discuss their impact on the spatiotemporal integration of synaptic input.     

Neural bistability and amplification mediated by NMDA receptors: Analysis of stationary equations

The macroscopic current/voltage relationship of NMDA receptor ion channels is nonmonotonic under physiological conditions, which can give rise to bistable and amplifying/facilitatory behavior in neurons and neural structures, supporting significant computational primitives. Conditions under which bistable regimes of operation prevail, and also general amplifying properties associated with active NMDA receptors, are examined in a single compartment enclosed by a cell membrane, and subsequently in cable-like dendrites under varying boundary conditions. Methodology consists of numerical and mathematical analyses of stationary versions of equations governing the electrical behavior of these systems. Bistability mediated by NMDA receptors requires interaction with other conductances in the membrane or cytoplasm, with particular importance attached to membrane potassium conductance, especially that of inward-rectifying potassium channels. A corollary conclusion is that coactivation of GABA_B synaptic receptors or SK channels is a computationally powerful and sometimes necessary adjunct condition for NMDA receptor-mediated bistability. Neural multistability due to dendritic bistability is considered, including the case of closely coupled dendrites. The characteristics of coactivation-dependent facilitation, and amplifying states in which NMDA receptor activation boosts the efficacy of other classes of synapses, are also described. Coactive inward-rectifying potassium channels are found to significantly affect the characteristics of such amplification.     

Detecting and estimating signals in noisy cable structures, II: information theoretical analysis

This is the second in a series of articles that seek to recast classical single-neuron biophysics in information-theoretical terms. Classical cable theory focuses on analyzing the voltage or current attenuation of a synaptic signal as it propagates from its dendritic input location to the spike initiation zone. On the other hand, we are interested in analyzing the amount of information lost about the signal in this process due to the presence of various noise sources distributed throughout the neuronal membrane. We use a stochastic version of the linear one-dimensional cable equation to derive closed-form expressions for the second-order moments of the fluctuations of the membrane potential associated with different membrane current noise sources: thermal noise, noise due to the random opening and closing of sodium and potassium channels, and noise due to the presence of "spontaneous" synaptic input. We consider two different scenarios. In the signal estimation paradigm, the time course of the membrane potential at a location on the cable is used to reconstruct the detailed time course of a random, band-limited current injected some distance away. Estimation performance is characterized in terms of the coding fraction and the mutual information. In the signal detection paradigm, the membrane potential is used to determine whether a distant synaptic event occurred within a given observation interval. In the light of our analytical results, we speculate that the length of weakly active apical dendrites might be limited by the information loss due to the accumulated noise between distal synaptic input sites and the soma and that the presence of dendritic nonlinearities probably serves to increase dendritic information transfer.     

A multi-user linear equalizer for uplink broadband millimeter wave massive MIMO

Most previous work on hybrid beamforming millimeter wave (mmWave) communications and massive multiple input multiple output (MIMO) systems focused on narrowband channels. However, it is expected that the mmWave channels will be wideband, and for that reason, it is important to design solutions for frequency-selective channels. Therefore, in this paper, we consider the design of a hybrid analog/digital multiuser linear equalizer for broadband mmWave massive MIMO systems, optimized using the bit error rate (BER) as a metric. For the digital part, a closed-form solution is obtained by showing that the minimum mean square error (MMSE) and minimum BER solutions are identical. The analog part, which is assumed to be the same for all subcarriers, is computed iteratively over the radio frequency (RF) chains, and it is shown that the solution obtained in a previous iteration may be updated inexpensively to obtain a solution for the current iteration. The simulation results show that the performance gap between the proposed hybrid broadband multiuser equalizer and full digital equalizer decreases monotonically and exponentially with the number of RF chains until reaching zero. Moreover, the gap decay rate is faster for sparser mmWave channels, where fewer RF chains are required to achieve the same performance of the full digital architecture.     

Optimization of the exponential stabilization problem in active suspension system using PSO

This paper proposes an active suspension mechanism for three-degree-of-freedom (3-DOF) twin-shaft vehicles of front axle suspension with bounded uncertainties using exponential decay control and particle swarm optimization (PSO) techniques. A new exponential stabilization criterion of the system via the dynamic state feedback control is derived and the optimization problem of exponential stabilization is discussed. The optimization problem is solved by using PSO method to guarantee all the states of vehicles in an optimal exponential decay in nearly real-time. Simulation results in both frequency and time domains show that the vibration characteristics of vehicles for the proposed active suspension system achieve significant improvements over the passive and linear quadratic (LQ) active suspension systems.     

Computer networking for the robotic neural and sensory systems

At the present time, robotic systems are composed mainly of mechanical functions connected for the purpose of achieving a specific form of automation. An overall plan for the robotic system computer architecture is described to investigate optimal, efficient and cost effective mimicking of human nervous system functions in the robotic system. The following component analogies are drawn between the computer network and the human nervous system: computer node to soma (cell body), input communication channels (links) to dendrites, output communication channels to axons and communications processor ports to buttons. The robotic nervous system therefore is structured as a larger internetwork (similar to the DARPA internetwork) of gateway nodes (controlling neuron cell bodies) which connect neuron intranetworks together. As in the human body, the robotic nervous system is the controlling system for visual and sensory functions.     

Compartmental modelling with artificial neural networks

In this paper, we introduce an abbreviated compartmental modelling scheme which may be of interest to those in neuron- based adaptive systems because of the additional scope it provides for studying biologically-inspired learning mechanisms. The scheme, although not as flexible and precise as the general compartmental approach, allows one to design Hodgkin-Huxley style cells, and passive dendritic trees with an arbitrary number of synaptic connections. The trade-offs made for computational performance, may make the modelling scheme more appropriate for practical applications. The modelling scheme is based upon artificial neural networks, which we have used to represent cylindrical compartments (both passive and active) of different lengths, two types of voltage-dependent channels, and basic chemical synapses with variable time constants.     

Detecting systematic deviations from single exponentiality,using numerical integration and difference plots

Non-exponential behavior due to, e.g., a second exponential, a second-order process or a poor base line, is made more obvious with enhanced S/N when the data are numerically integrated. Both model calculations and decay data show ≈ 10-fold enhancement. It is shown that ≈ 2% of a second exponential (with twice the rate constant) is detected in a decay that is almost single exponential.     

How membrane properties shape the discharge of motoneurons: a detailed analytical study

Electrophysiological experiments and modeling studies have shown that afterhyperpolarization regulates the discharge of lumbar motoneurons in anesthetized cats and is an important determinant of their firing properties. However, it is still unclear how firing properties depend on slow afterhyperpolarization, input conductance, and the fast currents responsible for spike generation. We study a single-compartment integrate-and-fire model with a slow potassium conductance that exponentially decays between spikes. We show that this model is analytically solvable, and we investigate how passive and active membrane properties control the discharge. We show that the model exhibits three distinct firing ranges (primary, secondary, and high frequency), and we explain the origin of these three ranges. Explicit expressions are established for the gain of the steady-state current-frequency (I-f) curve in the primary range and for the gain of the I-f curve for the first interspike interval. They show how the gain is controlled by the maximal conductance and the kinetic parameters of the afterhyperpolarization conductance. The gain also depends on the spike voltage threshold, and we compute how it is decreased by threshold accommodation (i.e., the increase of the threshold with the injected current). In contrast, the gain is not very sensitive to the input conductance. This implies that tonic synaptic activity shifts the current-frequency curve in its primary range, in agreement with experiments. Taking into account the absolute refractory period associated with spikes somewhat reduces the gain in the primary range. More importantly, it accounts for the approximately linear and steep secondary range observed in many motoneurons. In the nonphysiological high-frequency range, the behavior of the I-f curve is determined by the fast voltage-dependent currents, via the amplitude of the fast repolarization afterspike, the duration of the refractory period, and voltage threshold accommodation, if present.     

Activity-dependent development of axonal and dendritic delays, or, why synaptic transmission should be unreliable

Systematic temporal relations between single neuronal activities or population activities are ubiquitous in the brain. No experimental evidence, however, exists for a direct modification of neuronal delays during Hebbian-type stimulation protocols. We show that in fact an explicit delay adaptation is not needed if one assumes that the synaptic strengths are modified according to the recently observed temporally asymmetric learning rule with the downregulating branch dominating the upregulating branch. During development, slow, unbiased fluctuations in the transmission time, together with temporally correlated network activity, may control neural growth and implicitly induce drifts in the axonal delays and dendritic latencies. These delays and latencies become optimally tuned in the sense that the synaptic response tends to peak in the soma of the postsynaptic cell if this is most likely to fire. The nature of the selection process requires unreliable synapses in order to give successful synapses an evolutionary advantage over the others. The width of the learning function also determines the preferred dendritic delay and the preferred width of the postsynaptic response. Hence, it may implicitly determine whether a synaptic connection provides a precisely timed or a broadly tuned "contextual" signal.     

融合结构化信息与时序演化信息的多变量间歇性时间序列预测

复杂装备制造企业的售后配件需求不定时发生、需求波动大,导致需求数据呈现典型的间歇性、小样本特点.当面对间歇性程度高、突发需求较大的序列时,现有预测模型难以准确捕获其需求波动规律,无法有效预测配件需求走势.为提高多组配件的预测精度和稳定性,同时考虑序列间结构化信息和序列自身时序演化信息,提出一种新的多变量间歇性时间序列预测方法.首先,提出一种基于张量的轻型梯度提升机模型,通过张量分解,重构原始需求数据,修正序列中的异常需求值,并利用轻型梯度提升机对多组序列进行联合预测;然后,构建一种新的线性衰减修正模型,将修正因子引入线性衰减指数平滑方法,对每条序列分别预测需求量和间隔区间;最后,将2个预测模型进行加权融合,得到最终预测结果.分别在2个复杂装备制造企业的售后配件需求数据集上进行实验验证,实验结果表明,与多个时间序列预测算法相比,所提出方法能够有效预测需求波动趋势,提升预测精度和数值稳定性.     

Detection and characterization of peaks and estimation of instantaneous secretory rate for episodic pulsatile hormone secretion

We have developed a new computer program for detection of "peaks" in sequential hormone measurements in longitudinal studies of episodic hormone secretion. The program provides: (a) several statistically based approaches to the estimation of the random measurement error as a function of hormone level; (b) peak detection based on analysis of first derivatives with logic that has been optimized for asymmetrical peaks with exponential decays; (c) several approaches to the estimation of tolerances for the first and second derivatives; (d) a sensitive curve-fitting approach, to distinguish between upstrokes, exponential decays, and flat baselines; (e) ability to detect multiple overlapping peaks; (f) analysis of "robustness" by systematically varying the threshold around the most-likely value; (g) superimposition of detected peaks, to evaluate "average peak shape"; (h) analysis of the "decay rate," to obtain an estimate of the disappearance rate constant and half-life; (i) use of a "discrete deconvolution" approach, to solve for the apparent instantaneous rate of secretion, and provision of an error analysis to obtain estimates of the precision of these derived values; and (j) correlation with other relevant series as a means of cross validating. The program has been tested extensively on real and synthetic data, and appears to perform well. The frequency of "false positive" peaks can be held at any desired low level, and can be prevented from increasing as sampling frequency increases. The number of arbitrary assumptions, approximations, or thresholds is held to an absolute minimum. These methods are natural, logical, and follow from first principles of statistics.     

The effect of dendritic topology on firing patterns in model neurons

Neuronal firing patterns are influenced by both membrane properties and dendritic morphology. Distinguishing two sources of morphological variability-metrics and topology-we investigate the extent to which model neurons that have the same metrical and membrane properties can still produce different firing patterns as a result of differences in dendritic topology. Within a set of dendritic trees that have the same number of terminal segments and the same total dendritic length, we show that firing frequency strongly correlates with topology as expressed by the mean dendritic path length. The effect of dendritic topology on firing frequency is bigger for trees with equal segment diameters than for trees whose segment diameters obey Rall's 3/2 power law. If active dendritic channels are present, dendritic topology influences not only firing frequency but also type of firing (regular, bursting).     

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.     

A new approach for EEG signal classification of schizophrenic and control participants

This paper is concerned with a two stage procedure for analysis and classification of electroencephalogram (EEG) signals for twenty schizophrenic patients and twenty age-matched control participants. For each case, 20 channels of EEG are recorded. First, the more informative channels are selected using the mutual information techniques. Then, genetic programming is employed to select the best features from the selected channels. Several features including autoregressive model parameters, band power and fractal dimension are used for the purpose of classification. Both linear discriminant analysis (LDA) and adaptive boosting (Adaboost) are trained using tenfold cross validation to classify the reduced feature set and a classification accuracy of 85.90% and 91.94% is obtained by LDA and Adaboost, respectively. Another interesting observation from the channel selection procedure is that most of the selected channels are located in the prefrontal and temporal lobes confirming neuropsychological and neuroanatomical findings. The results obtained by the proposed approach are compared with a one stage procedure, the principal component analysis (PCA)-based feature selection, utilizing only 100 features selected from all channels. It is illustrated that the two stage procedure consisting of channel selection followed by feature reduction gives a more enhanced results in an efficient computation time.     

基于安卓5G信号参数获取上报的APP设计与实现

随着互联网和移动网络通信技术的快速发展,移动网络通信在短短的几年间就由2G时代、3G时代发展为目前的5G时代。在5G部署的过程中,为方便计算机能获取与分析5G信号参数,设计了一款基于安卓移动端的5G信号获取上报软件。该安卓软件以手机端为基础,通过5G移动传输网络和内部传输网络为传输途径,第一:实现了5G信号中的重要参数RSRP、SINR等的获取,并且将这些重要参数以JSON格式文件发送至计算机服务器端,用于信号参数分析;第二:实现了RSRP、SINR等以图表的形式动态展示给工作人员观察;第三:实现了计算机服务器端下发一些简单指令给手机端,手机端能够执行一些Linux指令。实验结果表明,该安卓应用软件非常灵活,能够完成5G部署过程中信号参数的获取、信号参数上报的任务。     

Guaranteed Parameter Set Estimation for Exponential Sums: The Three-Terms Case

In this paper the problem of parameter estimation for exponential sums with three terms is considered. This task consists of finding the set of parameters (amplitudes as well as decay constants) such that the exponential sum attains values in specified intervals at prescribed time data points. These intervals represent uncertainties in the measurements. An interval variant of Prony’s method is given by which intervals can be found containing all the consistent values of the respective parameters. By the use of interval arithmetic these enclosures can also be guaranteed in the presence of rounding errors. Support from the Ministry of Education and Research of the Federal Republic of Germany under contract no. 1705803 and from the DAAD program PROCOPE under contract no. D/0205730 is gratefully acknowledged.     

Discrimination of a refrigerant gas in a humidity controlled atmosphere by using modelling parameters

For real life conditions of use, the application based on tin oxide gas sensors must be humidity controlled, because humidity is a very influent parameter which may cause false gas detection. In our application, we want to distinguish a refrigerant gas (Forane R134a) in an air conditioned atmosphere using a TGS gas sensor array. At first, this paper presents a summary of the sensor array time-dependent response in a Forane R134a gas concentration (0–1000 ppm) for a humid air atmosphere (0–85%). We show that these responses can be well fitted by a double exponential model for which we extract five modelling parameters. These variables are coupled or not with two experimental parameters, the steady-state conductance and the conductance dynamic slope, and then arranged in data bases. Afterwards, these data bases are treated by using two complementary pattern recognition methods: the principal component analysis (PCA) followed by the discriminant factorial analysis (DFA). We show the ability to discriminate the target gas, whatever the humidity rate, when the modelling parameters are coupled or not with the experimental parameters. Finally, the identification of unknown cases is described.     

基于SOPC的复合式生理信号检测系统设计

设计完成了一种多生物电信号采集能力并能完成生物电信号模式识别和辅助诊断的复合式生物电信号检测系统。系统通过具备双通道的低噪声高共模抑制比的前置采集放大电路,可实现心电信号和表面肌电信号两种体表生物电信号的检测。通过FPGA硬件化实现的小波分解模块和在NiosⅡ软核中实现的FFT和BP神经网络算法,可以完成对采集到的心电信号心率监测、QRS波群的检测和ST段形态识别反馈监护者的健康信息;并通过提取表面肌电信号活跃段数据和时频域参数为运动性肌肉疲劳评估提供参考。系统通过LCD屏、音频输出和SD卡存储能够完成对信号实时波形和监护参数显示、报警输出和长时间监护数据的存储。