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

Properties and performances of associative memories, based on Complex-valued Synergetic Computer (CVSC), are explored in this paper. All the parameters of CVSC are encoded by complex values. CVSC is extended from the conventional Synergetic Computer (RVSC) in which the parameters are real values. Performances of associative memories in CVSC are investigated through a problem of image retrievals where the input images are partially occluded or noise-affected. From the experimental results concerning the retrieval performances related to various sizes of images and different levels of defectiveness of input images, we found that CVSC outperforms RVSC.

On retrieval performance of associative memory by Complex-valued Synergetic Computer

This paper proposes the re-learning for feedforward neural networks where weight faults would occur. The sequences of target outputs are encoded by means of single-parity-check codes so that a single-bit error caused by the faults can be on-line detected at the output layer. The re-learning is made every time a network produces the error, and its lost function is retrieved. The proposed scheme can easily achieve high MTTF (Mean Time To Failure).

On Retrieval of Lost Functions for Feedforward Neural Networks Using Re-Learning

Waggle dance by honeybee workers is a behavior for sharing information concerning feeding sources. Thus it is important to analyze the occurrence timing and distribution of waggle dance. This paper presents a scheme for automatically detection of workers undergoing waggle dance from the image sequence. The performance of the proposed scheme is investigated by using actual images obtained by a video camera.

http://ieeexplore.ieee.org/iel5/6309334/6318306/06318479.pdf

On detecting waggle dance behaviors from spatio-temporal sectional images

We design a mechanical apparatus that works as "quantum" computer even though it follows classical physics. To do quantum computation, we need to know what percentage of a particular state is included in a superposed quantum state. However, the superposition easily breaks unless expensive and large-scale tooling, such as cryogenic temperature is prepared. We pay attention to causal formulation of quantum mechanics. The weighting coefficients of the superposition can be determined by monitoring the trajectory of a quantum particle. Such monitoring is possible only in macroscopic physics. Efficacy of our macroscopic apparatus model is shown in one of typical quantum algorithms, Deutsch algorithm. This classical mechanical design opens the way for "quantum mechanical" artificial intelligence to be mounted on robots in usual macroscopic world.

Nobuyuki Matsui

Papers

On retrieval performance of associative memory by Complex-valued Synergetic Computer

On Retrieval of Lost Functions for Feedforward Neural Networks Using Re-Learning

On detecting waggle dance behaviors from spatio-temporal sectional images

Quantum Computation by Classical Mechanical Apparatuses

Design of Fault Tolerant Multistage Interconnection Networks with Dilated Links