Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

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Quantum Models of Cognition and Decision

Metaheuristic design of feedforward neural networks

In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data.

Enhanced probabilistic neural network with local decision circles: A robust classifier

http://audition.ens.fr/dp/pdfs/pressnitzer-2006-bistable_AV_Authors_Manuscript.pdf

Temporal dynamics of auditory and visual bistability reveal common principles of perceptual organization

What are quantum fluctuations

A localization scheme for wireless sensor networks is proposed and its performances are investigated in this paper. The proposed scheme is an anchor-free scheme, in which no geometrical information of sensor nodes is required for their localization. Thus, only local interaction among sensor nodes is used to estimate their locations. This scheme employs the link quality indicator and hop count between sensor nodes for location estimation. A weighted averaging and Kalman filtering schemes are incorporated in order to reduce the effects of the measurement errors in the obtained values of the link quality indicator. We performed experiments by implementing the proposed scheme on ZigBee sensor modules. The results of our experiments indicate that estimation could be successfully performed for networks comprising four sensor nodes.

/pdf/an-anchor-free-localization-scheme-with-kalman-filtering-in-3v61zaxwd8.pdf

An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

We have proposed a novel neuron model whose states and interactions with other neurons are based on the laws of quantum physics In our previous works, neural network constricted by this neuron, ie, qubit neural network, has been shown through various benchmark simulations As an example of nonholonomic system control, in this report, the swing-up control of inverted pendulum by the qubit neural network is investigated to estimate the efficiencies for more practical applications For comparison, our qubit neural network and a conventional neural network are evaluated As a result, we find it is possible for our qubit neural network to swing up and stabilize the inverted pendulum while not for any conventional neural network

Control for swing-up of an inverted pendulum using qubit neural network

Though neural networks have attracted much interest in the last two decades for their potential to describe brain function realistically, they have failed thus far to provide models that can be simulated in a reasonable time on computers, other than toy models. Quantum computing is a likely candidate for improving the computational efficiency of neural networks, since it has been very successful in doing so for a selected set of computational problems. In this framework, the Qubit neuron model, proposed by Matsui and Nishimura, has shown its promise in improving the efficiency. The simulations we have performed have shown that the Qubit model performs learning problems with significantly improved efficiency as compared to the classical model, and a classical model in which complex numbers are allowed as model parameters and as input. In this paper, we investigate what contributes to the efficiencies through 4-bit parity check problem known as a basic benchmark test. Our simulation suggests that the improved performance is due to the use of superposition of neural states and the use of the probability interpretation in the observation of the output states of the model.

Qubit Neural Network and Its Efficiency

Several models have been proposed for describing grouping behavior such as bird flocking, terrestrial animal herding, and fish schooling. In these models, a fixed rule has been imposed on each individual a priori for its interactions in a reductive and rigid manner. We have proposed a new framework for self-organized grouping of agents by reinforcement learning. It is important to introduce a learning scheme for developing collective behavior in artificial autonomous distributed systems. This scheme can be expanded to cases in which predators are present. In this study we integrate grouping and anti-predator behaviors into our proposed scheme. The behavior of agents is demonstrated and evaluated in detail through computer simulations, and their grouping and anti-predator behaviors developed as a result of learning are shown to be diverse and robust by changing some parameters of the scheme.

Learning Grouping and Anti-predator Behaviors for Multi-agent Systems

In this paper we propose a fault tolerant baseline network as a sort of MINs (multistage interconnection networks) and discuss its performance analysis. For our MIN with N input and N output terminals, switching elements in the first and n-th stages are duplicated where n=log/sub 2/N. Four-input two-output switching elements and two-input four output ones employed in the second and (n-1)-th stages are useful in sharing loads efficiently on the first and n-th stages respectively. The comparison results show that the theoretical throughput of our MIN without faults and the performance of our MIN with faults are superior to those of previously known ELMIN though our MIN requires slightly more hardware overhead than ELMIN.

Nobuyuki Matsui

Papers

An Anchor-Free Localization Scheme with Kalman Filtering in ZigBee Sensor Network

Control for swing-up of an inverted pendulum using qubit neural network

Qubit Neural Network and Its Efficiency

Learning Grouping and Anti-predator Behaviors for Multi-agent Systems

Design of a fault tolerant multistage interconnection network with parallel duplicated switches