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

In this paper we propose the on-line multiple-fault-detection of fuzzy controllers. The membership functions in each antecedent part are divided into at most four groups. The faults in each group are detected by observing calculations concerning degrees of the functions in it. Our test forms two output fuzzy sets per function, and observes the calculations concerning areas of the sets. It has the advantage of being valid for the case where the number of overlapped functions is three or more.

On-line multiple-fault-detection of fuzzy controllers

Molecular robotics is an emerging area of research aiming at building robots made of components such as sensors, computers, and actuators, which are all implemented as molecular devices. The molecular robot is supposed to react autonomously to its environment by receiving chemical/physical signals and decides behavior by molecular computation [1–3]. Firstly, I would like to introduce some basic principles of molecular computation based on hybridization reactions of DNA molecules. Various computational methods ranging from signal amplification, logic operation to reaction-diffusion-like computation have been proposed so far, will be implemented as computational components for the molecular robots. Despite extensive efforts, however, systems having all three functions (sensing, computation, and actuation) are still difficult to realize, because integrating different chemical components in the same spatio-temporal space causes a lot of undesired spurious interactions among them. How to integrate chemical devices into a consistent network is the central issue of molecular robotics indeed. I think new architectures are necessary to cope with this intrinsic difficulty. Proposed models of computation for molecular robots such as single-molecular computing, computing in liposomes and computing in gel will be introduced to show the current level of research in this field.

/pdf/cellular-automata-and-discrete-complex-systems-alqecr3pvc.pdf

Cellular Automata and Discrete Complex Systems

A neural network model for the binding problem in visual system is proposed and analyzed in this paper. It contains clusters of neurons, and each of clusters behaves as a coupled oscillator, whose activity is represented by its phase. Two networks are composed of these clusters, and they are responsible for the different properties of an input object. Connections between clusters and between networks are defined. The numerical results show that the clusters in a network could extract distinct objects by the synchronization of clusters and the clusters corresponding to the same object between networks are coherent, there by these behaviors exhibit the binding of information.

http://ieeexplore.ieee.org/iel5/9240/29303/01323817.pdf

A coupled oscillatory neural network model for binding problem

A new method has been proposed for implementing the essential boundary condition and the natural one to the Element-Free Galerkin Method (EFGM). Furthermore, the performance of the proposed method has been investigated for a nonlinear Poisson problem. The results of computations show that the accuracy degradation of the numerical solution can be suppressed by using the proposed method. In addition, the convergence performance of the proposed method is more stable than that of the standard EFGM. Therefore, it might be concluded that the proposed method is useful for solving the nonlinear Poisson problem.

Application of Extended Element-Free Galerkin Method to Nonlinear Problem

Swarm Networks are a generalization of Cellular Automata (CA), in which the neighborhoods and functionalities of cells are determined by the presence or absence of connections between cells. This paper presents a Swarm Network in which connections can be changed dynamically, and in which the cells (called "agents") are subject to Brownian motion. According to these characteristics, the model mimics behavior typically encountered in biological organisms. We show that this model is capable of universal computation by constructing a universal Brownian circuit based on it.

Nobuyuki Matsui

Papers

On-line multiple-fault-detection of fuzzy controllers

Cellular Automata and Discrete Complex Systems

A coupled oscillatory neural network model for binding problem

Application of Extended Element-Free Galerkin Method to Nonlinear Problem

On Swarm Networks in Brownian Environments