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

This paper presents a 3-state asynchronous CA that requires merely two transition rules to achieve computational universality. This universality is achieved by embedding Priese’s delay-insensitive circuit elements, called the E-element and the K-element, on the cell space of a so-called Brownian CA, which is an asynchronous CA containing local configurations that conduct a random walk in the circuit topology.

On a Universal Brownian Cellular Automata with 3 States and 2 Rules

In this paper, a method of determining examination groups is presented for new outpatients visiting the department of ophthalmology, using support vector machines (SVM's). Assuming that interview sheets are divided into four classes, the proposed method copes with the examination determination as the classification of the sheets. The data are generated from handwriting sentences in the sheets, and they are arranged in the form of a matrix. Some nouns and adjectives in the sentences are chosen as elements of the matrix, and are assigned to columns of the matrix. The sentences in the sheets are assigned to rows of the matrix. Frequencies of the chosen words appearing in the sentences are basically given as element values in the matrix, and weighting is applied to element values associated with the words having comparatively high frequencies for sentences belonging to two classes at most. Normal SVM learning constructs a discrimination model, and defines four discriminant functions associated with the model. Since one-versus-all approach is employed, the class of data to be examined is determined according to output values of the four functions. It is established that the determination made by the proposed method achieves as favorable accuracy as the first determination made by an average ophthalmologist.

On Determination of Ophthalmic Examinations Using Support Vector Machines

Defects will be a major problem for manufacturing computers by nanotechnology. This paper explores this issue for nanocomputers based on cellular arrays. Known for their regular structure, such architectures promise cost-effective manufacturing based on molecular self-organization. We show a self-contained way to detect defects in a cellular array, and to configure circuits on its cells while avoiding the defects. Timing is assumed asynchronous, i.e., updating of cells is not controlled by a clock.

https://ieeexplore.ieee.org/iel5/10038/32214/01500726.pdf

On defect-tolerance in cellular computers

A dynamical neural network model is presented to show critical roles of neural synchronization and stochastic noise in perceptual alternation of ambiguous figures. In the model a coherent percept is represented by synchronized activity of neurons, and perceptual alternation is caused by exclusive activation of two possible percepts of an ambiguous figure. We show that stochastic fluctuation (noise) added to neural activity plays an essential role in obtaining perceptual alternation. We also complete a psychophysical investigation of alternation distributions, which demonstrates that perceptual alternation is caused by a certain kind of stochastic dynamics in the brain.

Synchronization and stochasticity of neural activity in perceptual alternation of ambiguous figures

Learning-based on-line testing in feedforward neural networks (NNs) is discussed. After the convergence of the ordinary learning, the re-learning employing two sigmoid activation functions per neuron in the last layer of the NN is made. It sets up the range of erroneous potentials produced from the last layer, and enables us to detect faults without extra hardware.

Nobuyuki Matsui

Papers

On a Universal Brownian Cellular Automata with 3 States and 2 Rules

On Determination of Ophthalmic Examinations Using Support Vector Machines

On defect-tolerance in cellular computers

Synchronization and stochasticity of neural activity in perceptual alternation of ambiguous figures

Learning-based on-line testing in feedforward neural networks