Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques

One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of surface electromyographic (EMG) signals from the semitendinosus and vastus medialis muscles. The inputs consisted of flexion and extension angles measured at the hip and knee of the ipsilateral leg, their first and second derivatives, and bilateral foot contact information. The training set consisted of data from six trials, at two different speeds. The testing set consisted of data from two additional trials (one at each speed), which were not in the training set. It was possible to reconstruct the muscular activation at both speeds using both techniques. Timing of the reconstructed signals was accurate. The integrated value of the activation bursts was less accurate. The neural network gave a continuous output, whereas the rule-based inductive learning rule tree gave a quantised activation level. The advantage of rule-based inductive learning was that the rules used were both explicit and comprehensible, whilst the rules used by the neural network were implicit within its structure and not easily comprehended. The neural network was able to reconstruct the activation patterns of both muscles from one network, whereas two separate rule sets were needed for the rule-based technique. It is concluded that machine learning techniques, in comparison to explicit inverse muscular skeletal models, show good promise in modelling nearly cyclic movements such as locomotion at varying walking speeds. However, they do not provide insight into the biomechanics of the system, because they are not based on the biomechanical structure of the system.     

EEG source localization: comparative study of classical and neural network methods.

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg-Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.     

The location and characterisation of the O-linked glycans of the human insulin receptor

O-linked glycosylation is a post-translational and post-folding event involving exposed S/T residues at beta-turns or in regions with extended conformation. O-linked sites are difficult to predict from sequence analyses compared to N-linked sites. Here we compare the results of chemical analyses of isolated glycopeptides with the prediction using the neural network prediction method NetOGlyc3.1, a procedure that has been reported to correctly predict 76% of O-glycosylated residues in proteins. Using the heavily glycosylated human insulin receptor as the test protein six sites of mucin-type O-glycosylation were found at residues T744, T749, S757, S758, T759, and T763 compared to the three sites (T759 and T763- correctly, T756- incorrectly) predicted by the neural network method. These six sites occur in a 20 residue segment that begins nine residues downstream from the start of the insulin receptor beta-chain. This region which also includes N-linked glycosylation sites at N742 and N755, is predicted to lack secondary structure and is followed by residues 765-770, the known linear epitope for the monoclonal antibody 18-44.     

自组织特征映射网络在北京松山自然保护区山地草甸数量分析中的应用

采用自组织特征映射网络(SOM)对松山自然保护区山地草甸群落进行了数量分类研究, 并用Kruskal-Wallis检验和Tukey多重比较方法分析了草甸类型的环境因子之间差异的显著性。结合完全连接法和SOM, 将松山自然保护区的山地草甸群落分为7个类型, 其群落结构、物种组成等特征明显。这7个山地草甸群落主要受海拔高度、坡度、枯枝落叶层厚度和土壤深度等环境因子的影响, 其差异极显著。生态学分析表明SOM是非常有效的植物群落分类方法, 适合于山地草甸植被的 研究。     

应用神经网络和多元回归技术预测森林产量

应用传统统计技术常会因样本小和测量数据不符某种分布而受到限制。本文评价一种前馈型神经网络算法以预测落叶阔叶林产量。另外,还介绍一种由定性变为定量的数据变换方法,以用相对小的样本建立多元回归预测模型。数据变换方法有助于改善多元回归模型的预测效果。在本实验的条件下,研究结果表明神经网络技术能够产生最好的预测效果.     

Acoustical detection of coronary occlusions using neural networks

A nonlinear neural network classifier was applied to noninvasive acoustic detection of coronary artery disease; the classifier included a feature vector, derived from diastolic heart sounds, and a multi-layered network trained by the backpropagation. The feature vector is based on the linear prediction coefficients of the autoregressive method after an adaptive line enhancement method was used as the input pattern to the neural network. One hundred and twelve recordings (70 abnormal, 42 normal) were studied and the network was trained on a randomly chosen set of six abnormal and six normal patients. It was tested on a database consisting of 100 recordings to which it had not been exposed. The network correctly identified 50 of the 64 patients with coronary artery disease and 32 of the 36 patients without any coronary artery occlusions. These results showed that this neural network is capable of distinguishing normal patients from abnormal patients. In addition, the diagnostic capability of this approach is much better than any other available noninvasive approach.     

Novel method for the display of multivariate data using neural networks

A neural network has been used to reduce the dimensionality of multivariate data sets to produce two-dimensional (2D) displays of these sets. The data consisted of physicochemical properties for sets of biologically active molecules calculated by computational chemistry methods. Previous work has demonstrated that these data contain sufficient relevant information to classify the compounds according to their biological activity. The plots produced by the neural network are compared with results from two other techniques for linear and nonlinear dimension reduction, and are shown to give comparable and, in one case, superior results. Advantages of this technique are discussed.     

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.     

Optimization of a fermentation medium using neural networks and genetic algorithms

Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.     

Artificial neural networks and their use in quantitative pathology

A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.     

人工神经网络模型及其在农业和生态学研究中的应用

对于一些复杂的农业生态系统,人们对其生态过程了解较少,且这些系统的不确定性和模糊性较大,用传统的方法难以模拟这些系统的行为,神经网络模型因为能较精确地模拟这些系统的行为,而引起生态学者们的广泛兴趣。该文着重介绍了误差逆传神经网络模型的结构、算法及其在农业和生态学中的应用研究。误差逆传神经网络模型一般采用三层神经网络模型结构,三层的神经网络模型能模拟任意复杂程度的连续函数,而且因为它的结构小而不容易产生与训练数据的过度吻合。误差逆传神经网络模型算法的主要特征是:利用当前的输入误差对权值进行调整。在生态学和农业研究中,误差逆传神经网络模型通常作为非线性函数模拟器用于预测作物产量、生物生产量、生物与环境之间的关系等。已有的研究表明:误差逆传神经网络模型的模拟精度要远远高于多元线性方程,类似于非线性方程,而在样本量足够的情况下,有一定的外推能力。但是误差逆传神经网络模型需要大量的样本量来保证所求取参数的可靠性,但这在实际研究中很难做到,因而限制了误差逆传神经网络模型的应用。近年来人们提出了强制训练停止、复合模型等多种技术来提高误差逆传神经网络模型的外推能力,也提出了Garson算法、敏感性分析以及随机化检验等技术对误差逆传神经网络模型的机理进行解释。误差逆传神经网络模型的真正优势在于模拟人们了解较少或不确定性和模糊性较大系统的行为,这些是传统模型所无法实现的,因而是对传统机理模型的重要补充。     

Current developments and future directions of bio-inspired computation and implications for ecoinformatics

Evolutionary and neural computation has been used widely in solving various problems in biological ecosystems. This paper reviews some of the recent work in evolutionary computation and neural network ensembles that could be explored further in the context of ecoinformatics. Although these bio-inspired techniques were not developed specifically for ecoinformatics, their successes in solving complex problems in other fields demonstrate how these techniques could be adapted and used for tackling difficult problems in ecoinformatics. Firstly, we will review our work in modelling and model calibration, which is an important topic in ecoinformatics. Secondly one example will be given to illustrate how coevolutionary algorithms could be used in problem-solving. Thirdly, we will describe our work on neural network ensembles, which can be used for various classification and prediction problems in ecoinformatics. Finally, we will discuss ecosystem-inspired computational models and algorithms that could be explored as directions of future research.     

Vision-based force measurement using neural networks for biological cell microinjection

This paper presents a vision-based force measurement method using an artificial neural network model. The proposed model is used for measuring the applied load to a spherical biological cell during micromanipulation process. The devised vision-based method is most useful when force measurement capability is required, but it is very challenging or even infeasible to use a force sensor. Artificial neural networks in conjunction with image processing techniques have been used to estimate the applied load to a cell. A bio-micromanipulation system capable of force measurement has also been established in order to collect the training data required for the proposed neural network model. The geometric characterization of zebrafish embryos membranes has been performed during the penetration of the micropipette prior to piercing. The geometric features are extracted from images using image processing techniques. These features have been used to describe the shape and quantify the deformation of the cell at different indentation depths. The neural network is trained by taking the visual data as the input and the measured corresponding force as the output. Once the neural network is trained with sufficient number of data, it can be used as a precise sensor in bio-micromanipulation setups. However, the proposed neural network model is applicable for indentation of any other spherical elastic object. The results demonstrate the capability of the proposed method. The outcomes of this study could be useful for measuring force in biological cell micromanipulation processes such as injection of the mouse oocyte/embryo.     

A neural network model of the cerebellar cortex performing dynamic associations

The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the Golgi-granule cell system. A perceptron association is then performed on these sequences of patterns by the Purkinje cell layer. The model conforms with important biological constraints, such as the known excitatory or inhibitory nature of the various synapses. Also, as suggested by experimental evidence, the synaptic plasticity underlying the learning ability of the model, is confined to the parallel fiber — Purkinje cell synapses, and takes place under the control of the climbing fibers. The result is a neural network model, constructed according to the anatomy of the cerebellar cortex, and capable of learning and retrieval of temporal sequences of patterns. It provides a framework to represent and interpret properties of learning and control of movements by the cerebellum, and to assess the capacity of formal neural network techniques for modeling of real neural systems.     

Lessons for circuit function from large insects: towards understanding the neural basis of motor flexibility

Motor behaviors result from information processing that occurs in multiple neural networks acting at all levels from the initial selection of the behavior to its final generation. A long-standing research interest is how single neural networks can help generate different motor behaviors, that is, the origin of motor flexibility. Modern experimental techniques allow studying neural network activity during the production of multiple motor behaviors. Recent data provide strong evidence that the neural networks controlling insect legs are individually modified in task-dependent and finely tuned fashions. Understanding the mechanistic basis of these neural network modifications will be of particular interest in the upcoming years.     

A novel approach to the recognition of protein architecture from sequence using Fourier analysis and neural networks

A novel method is presented for the prediction of protein architecture from sequence using neural networks. The method involves the preprocessing of protein sequence data by numerically encoding it and then applying a Fourier transform. The encoded and transformed data are then used to train a neural network to recognize a number of different protein architectures. The method proved significantly better than comparable alternative strategies such as percentage dipeptide frequency, but is still limited by the size of the data set and the input demands of a neural network. Its main potential is as a complement to existing fold recognition techniques, with its ability to identify global symmetries within protein structures its greatest strength.     

A study of early stopping and model selection applied to the papermaking industry

This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.     

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.     

Feedforward neural network models for handling class overlap and class imbalance

This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed error function can be used for training variance-controlled neural networks (VCNNs), which are developed to handle class overlap by minimizing an error function involving the class-specific variance (CSV) computed at their outputs. Another special case of the proposed error function can be used for training class-balancing neural networks (CBNNs), which are developed to handle class imbalance by relying on class-specific correction (CSC). VCNNs and CBNNs are compared with conventional feedforward neural networks (FFNNs), quantum neural networks (QNNs), and resampling techniques. The properties of VCNNs and CBNNs are illustrated by experiments on artificial data. Various experiments involving real-world data reveal the advantages offered by VCNNs and CBNNs in the presence of class overlap and class imbalance.