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

Automatic Analyzing of a Weaving Design

This paper presents an automated method for segmenting CT images of the fractured foot. Segmentation boundary is determined by fuzzy inference with two types of knowledge acquired from orthopedic surgeons. Knowledge of joint is used to determine the boundary of adjacent normal bones. It gives higher degree to the articular cartilage according to local structure (parallelity) and intensity distribution around a joint part. Knowledge of fragment is used to find a contact place of fragments. It evaluates Euclidian distance map (EDM) of the contact place and gives higher degree to the narrow part. Each of the knowledge is represented by fuzzy if-then rules, which can provide degrees for segmentation boundary. By evaluating the degrees in region growing process, a whole foot bone is decomposed into each of anatomically meaningful bones and fragments. An experiment was done on CT images of the subjects who have depressed fractures on their calcanei. The method could effectively give higher degrees on the essential boundary, suppressing generation of useless boundary caused by the internal cavities in the bone. Each of the normal bones and fragments were correctly segmented.

Segmentation of the fractured foot CT image: a fuzzy-rule-based approach

In this paper, we explore a complex-valued neural networks (NNs) with incorporating the mechanism of stochastic resonance (SR). SR is a phenomenon where weak periodic signals in the system can be detected by the presence of the noise. It is expected that the combination of complex-valued and SR mechanism will improve the performance of NNs, rather than NNs with either of them. The performances are evaluated through the problem of affine transformations in two-dimensional space. It is shown that complex-valued NN with SR could achieve more precise transformations rather than conventional real-valued NN and complex-valued NN.

http://ieeexplore.ieee.org/iel5/5593294/5601973/05603207.pdf

Performance analysis of complex-valued neural networks with stochastic resonance

Performance Analysis of Quantum-inspired Evolutionary Algorithm

Unreliability will be a major issue for computers built from components at nanometer scales. Thus, it’s to be expected that such computers will need a high degree of defect-tolerance to overcome components’ defects which have arisen during the process of manufacturing. This paper presents a novel approach to defect-tolerance that is especially geared towards nanocomputers based on asynchronous cellular automata. According to this approach, defective cells are detected and isolated by small configurations that move around randomly in cellular space. These configurations, called random flies, will attach to configurations that are static, which is typical for configurations that contain defective cells. On the other hand, dynamic configurations, like those that conduct computations, will not be isolated from the rest of the cellular space by the random flies, and will be able to continue their operations unaffectedly.

Nobuyuki Matsui

Papers

Automatic Analyzing of a Weaving Design

Segmentation of the fractured foot CT image: a fuzzy-rule-based approach

Performance analysis of complex-valued neural networks with stochastic resonance

Performance Analysis of Quantum-inspired Evolutionary Algorithm

Online isolation of defects in cellular nanocomputers