Programming in the brain: a neural network theoretical framework

Recent research shows that some brain areas perform more than one task and the switching times between them are incompatible with learning and that parts of the brain are controlled by other parts of the brain, or are “recycled”, or are used and reused for various purposes by other neural circuits in different task categories and cognitive domains. All this is conducive to the notion of “programming in the brain”. In this paper, we describe a programmable neural architecture, biologically plausible on the neural level, and we implement, test, and validate it in order to support the programming interpretation of the above-mentioned phenomenology. A programmable neural network is a fixed-weight network that is endowed with auxiliary or programming inputs and behaves as any of a specified class of neural networks when its programming inputs are fed with a code of the weight matrix of a network of the class. The construction is based on the “pulling out” of the multiplication between synaptic weights and neuron outputs and having it performed in “software” by specialised multiplicative-response fixed subnetworks. Such construction has been tested for robustness with respect to various sources of noise. Theoretical underpinnings, analysis of related research, detailed construction schemes, and extensive testing results are given.     

Connectionist learning: A comparison of neural networks and an optical thin-film multilayer model

Current work on connectionist models has been focused largely on artificial neural networks that are inspired by the networks of biological neurons in the human brain. However, there are also other connectionistarchitectures that differ significantly from this biological exemplar. We proposed a novel connectionist learning architecture inspired by the physics associated with optical coatings of multiple layers of thin-films in a previous paper (Li and Purvis 1999, Annals of Mathematics and Artificial Intelligence, 26: 1-4). The proposed model differs significantly from the widely used neuron-inspired models. With thin-film layer thicknesses serving as adjustable parameters (as compared with connection weights in a neural network) for the learning system, the optical thin-film multilayer model (OTFM) is capable of approximating virtually any kind of highly nonlinear mappings. The OTFM is not a physical implementation using optical devices. Instead, it is proposed as a new connectionist learning architecture with its distinct optical properties as compared with neural networks. In this paper we focus on a detailed comparison of neural networks and the OTFM (Li 2001, Proceedings ofINNS-IEEE International Joint Conference on Neural Networks, Washington, DC, pp. 1727-1732). We describe the architecture of the OTFM and show how it can be viewed as a connectionist learning model. We then present experimental results on solving a classification problem and a time series prediction problem that are typical of conventional connectionist architectures to demonstrate the OTFM's learning capability.     

Synaptic noise as a means of implementing weight-perturbation learning

Weight-perturbation (WP) algorithms for supervised and/or reinforcement learning offer improved biological plausibility over backpropagation because of their reduced circuitry requirements for realization in neural hardware. This paper explores the hypothesis that biological synaptic noise might serve as the substrate by which weight perturbation is implemented. We explore the basic synaptic noise hypothesis (BSNH), which embodies the weakest assumptions about the underlying neural circuitry required to implement WP algorithms. This paper identifies relevant biological constraints consistent with the BSNH, taxonomizes existing WP algorithms with regard to consistency with those constraints, and proposes a new WP algorithm that is fully consistent with the constraints. By comparing the learning effectiveness of these algorithms via simulation studies, it is found that all of the algorithms can support traditional neural network learning tasks and have similar generalization characteristics, although the results suggest a trade-off between learning efficiency and biological accuracy.     

神经网络监督控制在液压舵机伺服系统中的应用

针对液压舵机伺服系统中存在的非线性因素和工作环境的不确定干扰,提出将改进的神经网监督控制算法应用到舵机伺服控制系统设计中.该算法采用单神经元PID控制取代常规线性控制用于神经网络控制器学习,以提高控制系统的鲁棒性及神经网络模型学习初期系统的稳定性.在Simulink环境中建立液压舵机伺服控制系统模型并进行仿真,仿真结果表明:改进的神经网络监督控制,在液压舵机伺服系统中,具有良好的控制效果和较强的鲁棒性,为舵机伺服系统设计提供了一条新的思路.     

Temporal sequence detection with spiking neurons: towards recognizing robot language instructions

We present an approach for recognition and clustering of spatio temporal patterns based on networks of spiking neurons with active dendrites and dynamic synapses. We introduce a new model of an integrate-and-fire neuron with active dendrites and dynamic synapses (ADDS) and its synaptic plasticity rule. The neuron employs the dynamics of the synapses and the active properties of the dendrites as an adaptive mechanism for maximizing its response to a specific spatio-temporal distribution of incoming action potentials. The learning algorithm follows recent biological evidence on synaptic plasticity. It goes beyond the current computational approaches which are based only on the relative timing between single pre- and post-synaptic spikes and implements a functional dependence based on the state of the dendritic and somatic membrane potentials around the pre- and post-synaptic action potentials. The learning algorithm is demonstrated to effectively train the neuron towards a selective response determined by the spatio-temporal pattern of the onsets of input spike trains. The model is used in the implementation of a part of a robotic system for natural language instructions. We test the model with a robot whose goal is to recognize and execute language instructions. The research in this article demonstrates the potential of spiking neurons for processing spatio-temporal patterns and the experiments present spiking neural networks as a paradigm which can be applied for modelling sequence detectors at word level for robot instructions.     

Kinematic motor learning

This paper focuses on adaptive motor control in the kinematic domain. Several motor-learning strategies from the literature are adopted to kinematic problems: ‘feedback-error learning’, ‘distal supervised learning’, and ‘direct inverse modelling’ (DIM). One of these learning strategies, DIM, is significantly enhanced by combining it with abstract recurrent neural networks. Moreover, a newly developed learning strategy (‘learning by averaging’) is presented in detail. The performance of these learning strategies is compared with different learning tasks on two simulated robot setups (a robot-camera-head and a planar arm). The results indicate a general superiority of DIM if combined with abstract recurrent neural networks. Learning by averaging shows consistent success if the motor task is constrained by special requirements.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

Trouble diagnosis of the grinding process by using acoustic emission signals

The focus of this study is the development of a credible diagnosis system for the grinding process. The acoustic emission signals generated during machining were analyzed to determine the relationship between grinding-related troubles and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient (m), a learning rate (a), and a structure of the hidden layer in the iterative learning process. The success rates of trouble recognition were verified.     

The role of cue information in the outcome-density effect: evidence from neural network simulations and a causal learning experiment

Although normatively irrelevant to the relationship between a cue and an outcome, outcome density (i.e. its base-rate probability) affects people's estimation of causality. By what process causality is incorrectly estimated is of importance to an integrative theory of causal learning. A potential explanation may be that this happens because outcome density induces a judgement bias. An alternative explanation is explored here, following which the incorrect estimation of causality is grounded in the processing of cue–outcome information during learning. A first neural network simulation shows that, in the absence of a deep processing of cue information, cue–outcome relationships are acquired but causality is correctly estimated. The second simulation shows how an incorrect estimation of causality may emerge from the active processing of both cue and outcome information. In an experiment inspired by the simulations, the role of a deep processing of cue information was put to test. In addition to an outcome density manipulation, a shallow cue manipulation was introduced: cue information was either still displayed (concurrent) or no longer displayed (delayed) when outcome information was given. Behavioural and simulation results agree: the outcome-density effect was maximal in the concurrent condition. The results are discussed with respect to the extant explanations of the outcome-density effect within the causal learning framework.     

一种新型全液压矫直机多神经元PID伺服控制器设计与研究

针对当前普遍常用的BP神经网络算法的缺陷,即反传算法复杂,收敛速度慢,在解决一些复杂多变非线性及系统多耦合问题时,网络权值收敛局部极小点,导致网络训练失败,反传算法权值迭代出现畸形变化问题。虽然神经网络控制器在软件仿真中运行结果都能满足要求,但是在实际的现场工程应用中很难适应复杂工业现场所要求的控制快速性和及时性。针对以上缺陷,提出了一种新型的多神经元PID神经网络算法,其原理是通过简化PID神经元网络控制器,改善其权值初始化来达到新型多神经元PID控制器控制过程的快速性和响应的及时性,将其应用到全液压矫直机四缸伺服控制器。通过将传统神经元网络控制器和文中所研究的新型多神经元PID控制器位移响应曲线对比分析,该新型多神经元PID伺服控制器具有反传算法大为简化、收敛速度快、网络权值灵活性好、实时性等优点,为多神经元PID控制器的工程化应用提供了理论支持。     

Minimally Connective,Auto-associative,Neural Networks

Classic barriers to using auto-associative neural networks to model mammalian memory include the unrealistically high synaptic connectivity of fully connected networks, and the relative paucity of information that has been stored in networks with realistic numbers of synapses per neuron and learning rules amenable to physiological implementation. We describe extremely large, auto-associative networks with low synaptic density. The networks have no direct connections between neurons of the same layer. Rather, the neurons of one layer are 'linked' by connections to neurons of some other layer. Patterns of projections of one layer on to another which form projective planes, or other cognate geometries, confer considerable computational power an the network.     

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for aⁿbⁿcⁿ , a context-sensitive language. The additional difficulty with aⁿbⁿcⁿ , compared with the context-free language aⁿbⁿ , consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.     

焊接柔性加工单元中熔池的实时控制

机器人焊接过程中熔池实时控制系统是焊接柔性加工单元 (WFMC)中保证良好焊接质量的一个重要子系统。文中建立了WFMC中焊接质量实时控制子系统并实现了该子系统与WFMC中央监控计算机的实时可靠通讯。在获得了焊接熔池特征参数的基础上 ,建立了焊接过程熔池正面参数和焊缝背面参数的神经网络模型。模型根据熔池正面参数可实时预测焊缝背面宽度。并设计了神经元自学习比例求和微分(PSD)控制器 ,通过调整脉冲峰值电流 ,实现了机器人脉冲钨极气体保护焊 (GTAW )过程中通过正面熔池传感对焊接焊缝背面宽度的实时控制。控制试验证明控制器可有效地对焊接过程进行控制     

Neural network modeling and intelligent control of FCAW penetration

The neural network modeling of FCAW penetration is researched in this paper, molten pool image is acquired by CCD, and preweld gap is gotten from laser vision system, the weld penetration is estimated according to the information include welding current, welding voltage, weld width, molten pool half length and gap width. The training samples of network can be partially gotten by numerical simulation. Single neuron self-tuning PID weld penetration controller is designed, and improved Hebb learning algorithm is applied for weights adjusting. Welding current is adjusted to make the weld penetration stable. The results of experiment with various cross-section and preweld gap workpiece show that this system is suitable to molten pool control.     

模糊神经网络在感应电机矢量控制中的应用

本文提出了一种基于模糊神经网络速度控制器（FNNC）的感应电机矢量控制系统,兼具模糊逻辑处理不确定信息的能力和神经网络的自学习能力,阐明了神经网络的结构设计、样本选取及训练方法。人工神经网络（ANN）的初始权值和阈值通过离线学习得到,模糊逻辑规则通过专家经验总结。仿真结果表明采用所提出的模糊神经网络的感应电机矢量控制系统,转速响应快,跟踪性能好,稳态误差大大减小,有效提高了系统的性能。     

红景天对睡眠剥夺大鼠学习记忆的影响及其机制探索

目的: 探讨红景天对睡眠剥夺大鼠学习记忆功能的影响及其可能机制。方法: 用小平台水环境法建立睡眠剥夺模型,每组半数行Y型电迷宫测试,并检测海马和额叶组织中超氧化物歧化酶(SOD)活力及丙二醛(MDA)含量,半数利用免疫组化法检测海马区胆碱乙酰转移酶(ChAT)及胶质纤维酸性蛋白(GFAP)表达。结果: Y型电迷宫测试显示,与模型组相比,红景天组全天总反应时间(TRT)和错误反应次数(EN)均较低(P<0.05)。与模型组相比,红景天组海马组织和额叶组织SOD活力均较高(P<0.05)。与模型组相比,红景天组海马组织MDA含量较低(P<0.05),额叶组织MDA含量与模型组差别无统计学意义(P>0.05)。ChAT表达检测表明,与模型组相比,红景天组阳性细胞着色较深,轮廓较清晰,排列较整齐,各亚区阳性细胞数量较多(P<0.05)。GFAP表达检测显示,与模型组相比,红景天组阳性细胞着色较浅,细胞体积较小,突起较短较细,侧枝较少,阳性细胞数量较少(P<0.05)。结论: 红景天对睡眠剥夺后大鼠脑组织氧化损伤及神经元损伤有保护作用,可缓解睡眠剥夺造成的学习记忆功能的下降。     

How infants' reaches reveal principles of sensorimotor decision making

ABSTRACT

In Piaget's classical A-not-B-task, infants repeatedly make a sensorimotor decision to reach to one of two cued targets. Perseverative errors are induced by switching the cue from A to B, while spontaneous errors are unsolicited reaches to B when only A is cued. We argue that theoretical accounts of sensorimotor decision-making fail to address how motor decisions leave a memory trace that may impact future sensorimotor decisions. Instead, in extant neural models, perseveration is caused solely by the history of stimulation. We present a neural dynamic model of sensorimotor decision-making within the framework of Dynamic Field Theory, in which a dynamic instability amplifies fluctuations in neural activation into macroscopic, stable neural activation states that leave memory traces. The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. A conditional probabilities analysis accounts quantitatively how motor decisions depend on the history of reaching. The results provide evidence for the interdependence among subsequent reaching decisions that is explained by the model, showing that by amplifying small differences in activation and affecting learning, decisions have consequences beyond the individual behavioural act.     

Artificial intelligence in engineering

This paper reviews five artificial intelligence tools that are most applicable to engineering problems: knowledge-based systems, fuzzy logic, inductive learning, neural networks and genetic algorithms. Each of these tools will be outlined in the paper together with examples of their use in different branches of engineering. The paper concludes by describing some of the engineering applications at the Cardiff Knowledge-based Manufacturing Centre.     

Tracking control of two-wheel driven mobile robot using compound sine function neural networks

The purpose of this paper is to propose a compound sine function neural network (NN) with continuous learning algorithm for the velocity and orientation angle tracking control of a mobile robot. Herein, two NN controllers embedded in the closed-loop control system are capable of on-line continuous learning and do not require any knowledge of the dynamics model. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a sine function with a unipolar sigmoid function. In the NN algorithm, the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, that is, constant, without the weight adjustment. The developed NN controllers have simple algorithm and fast learning convergence. Therefore, the proposed NN controllers can be suitable for the real-time tracking control of the mobile robots. The simulation results show that the proposed NN controller has better control performance in the tracking control of the mobile robot. The compound sine function NN provides a new way to solve tracking control problems for a mobile robot.     

应用神经网络技术预测应力腐蚀开裂

利用已有的应力腐蚀开裂数据训练人工神经网络，对奥氏体不锈钢在含有氯离子和氧的溶液中的应力腐蚀开裂敏感性进行了预测。所用的网络结构为三层结构，氯离子和氧含量作为网络输入，腐蚀开裂敏感性作为输出。学习算法为反向传播算法，以预测精度作为标准，训练得到网络的优化结构为2-6-1。结果表明，该劂络对应力腐蚀的预测比较准确，用神经网络技术预测应力腐蚀开裂敏感性是可行的。