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 recent years, quantum inspired neural networks have been applied to various practical problems since their proposal. Here we investigate whether our qubit neural network(QNN) leads to an advantage over the conventional (real-valued) neural network(NN) in the forecasting of chaotic time series. QNN is constructed from a set of qubit neuron, of which internal state is a coherent superposition of qubit states. In this paper, we evaluate the performance of QNN through a prediction of well-known Lorentz attractor, which produces chaotic time series by three dynamical systems. The experimental results show that QNN can forecast time series more precisely, compared with the conventional NN. In addition, we found that QNN outperforms the conventional NN by reconstructing the trajectories of Lorentz attractor.

Performance of Qubit Neural Network in Chaotic Time Series Forecasting

Various kinds of analysis and mathematical models based on neuroscience are developing in the neural network study In the research, experimental data and knowledge so far obtained are essential resources to deepen their consideration of neuronal systems and functions In order to utilize the accumulation of expertise and research effectively, it is important to integrate various resources, such as bibliography and experimental data The managing and sharing of research resources are absolutely imperative for future development in both experimental and analytical studies Various scientific fields need a new method to obtain precise information, because the amount of experimental data and publications has increased rapidly due to innovations in measurement, computers and network technologies Under this situation, an effective resource-managing based on CMS (Content Management System) is introduced here for the laboratory use We are providing functional modules to manage research resources for neuroinfomatics As a practical use of these modules, a database system for managing image data of invertebrate neurons measured by Confocal Laser Scanning Microscope (CLSM) is developed Furthermore, these modules are implemented for management of contents in the Invertebrate Brain Platform

Development of Invertebrate Brain Platform: Management of Research Resources for Invertebrate Neuroscience and Neuroethology

A Perception Model of Ambiguous Figures Based on the Neural Chaos.

One of the approaches for improving quality of life of patients, undergoing hospitalization, is to improve hospital meals that are provided to them. It is a very cumbersome task to plan daily menus for hospital meals, which have many conditions for nutrition and allergies, and this task becomes difficult if patients' preferences of foods are also considered for enhancing patients' quality of life. This task can be formulated as an optimization problem with menu items as variables and specific nutritional and allergic as its constraints. We have proposed a menu creation scheme to satisfy these conditions with being incorporated patients' food preferences, and shows a preliminary result for creating menus for a patient. A genetic algorithm was utilized to better and more efficiently locate combinations of menu dishes for individual patients. This paper shows that a variety of menus can be produced for patients with several ranges of ages, three types of physical activities, and allergic conditions.

Performances in GA-based menu production for hospital meals

In this paper, methods of selecting formulas to calculate intraocular lens (IOL) power for a cataract patient are presented, using support vector machines (SVM's) and self-organizing maps (SOM's). Each of training data has measured values associated with axial length and corneal refractive power as element values. Three IOL power formulas have variables into which these values are substituted. The discrimination model constructed by SVM learning consists of three coordinate spaces having two regions corresponding to two formulas out of three. Each of the spaces provides a potential solution. The formula to be used for some patient is specified by a majority of the potential solutions. The SOM-based scheme determines the formula suitable to some patient, observing the label attached to the winner neuron for the presented data having the above element values associated with the patient. The experimental results finally establish that the proposed SVM-based scheme especially works well to select the formula.

Nobuyuki Matsui

Papers

Performance of Qubit Neural Network in Chaotic Time Series Forecasting

Development of Invertebrate Brain Platform: Management of Research Resources for Invertebrate Neuroscience and Neuroethology

A Perception Model of Ambiguous Figures Based on the Neural Chaos.

Performances in GA-based menu production for hospital meals

Formula selection for intraocular power calculation using support vector machines and self-organizing maps