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, systematic three-dimensional classification is presented for sets of interneuron slice images of silkworm moths, using self-organizing maps. Fractal dimension values are calculated for target sets to quantify denseness of their branching structures, and are employed as element values in training data for constructing a map. The other element values are calculated from the sets to which labeling and erosion are applied, and they quantifies whether the sets include thick main dendrites. The classification result is obtained as clusters with units in the map. The proposed classification employing only two elements in training data achieves as high accuracy as the manual classification made by neuroscientists.

Three-dimensional classification of insect neurons using self-organizing maps

Grouping behavior, such as bird flocking, terrestrial animal herding, and fish schooling, is one of well-known emergent phenomena. Several models have been proposed for describing grouping behaviors, and two types of models can be defined: rule-based model and learning-based model. In rule-based models, each agent in a group has fixed interaction rules with respect to other agents. On the other hand, agents in learning-based model acquire their rules by the interactions of other agents with a learning scheme such as Q-learning. In this paper, we adopt quantities obtained from trails of agents, in order to investigate the properties for grouping behaviors of agents. We also evaluate rule-based and learning-based models by using these quantities under the environments with and without predatory agents.

A Comparison of Grouping Behaviors on Rule-Based and Learning-Based Multi-agent Systems

The extended boundary node method (X-BNM) with the radial point interpolation method (RPIM) shape function is proposed and its performance is numerically investigated by comparing with the dual reciprocity boundary element method (DRM). The results of computations show that the accuracy of the X-BNM is almost equal to that of the DRM. In addition, the speed of the X-BNM with the RPIM shape function is almost equal to that of the DRM. Therefore, not only the DRM but also the X-BNM with the RPIM shape function is a powerful method for solving the Poisson problem.

Performance Improvement of Extended Boundary Node Method

PROBLEM TO BE SOLVED: To provide a novel moving image processor detecting a moving object in an moving image using a self-organizing map. SOLUTION: A composite video signal output from a camera 20 is converted into color image data by an input conversion part 50. The color image data is input to a feature extraction part 58 through an image dividing part 52 and a frame setting part 56. The feature extraction part 58 extracts n-dimensional features of the input color image data, and the extracted feature data is input to a control part 60. The control part 60 constitutes a block unit learning type self-organizing map together with a map 62, and identifies whether each pixel forms a moving object region or a background region by applying the feature data to the map 62. Based on the identified result, the control part 60 controls an output conversion part 70 to display only the moving object region on a monitor 40. COPYRIGHT: (C)2008,JPO&INPIT

Moving image processor, moving image processing method and moving image processing program

Swarm Networks are a generalization of Cellular Automata, 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

Three-dimensional classification of insect neurons using self-organizing maps

A Comparison of Grouping Behaviors on Rule-Based and Learning-Based Multi-agent Systems

Performance Improvement of Extended Boundary Node Method

Moving image processor, moving image processing method and moving image processing program

Swarm Networks in Brownian Environments