Neural codes for one’s own position and direction in a real-world “vista” environment

Humans, like animals, rely on an accurate knowledge of one’s spatial position and facing direction to keep orientated in the surrounding space. Although previous neuroimaging studies demonstrated that scene-selective regions (the parahippocampal place area or PPA, the occipital place area or OPA and the retrosplenial complex or RSC), and the hippocampus (HC) are implicated in coding position and facing direction within small-(room-sized) and large-scale navigational environments, little is known about how these regions represent these spatial quantities in a large open-field environment. Here, we used functional magnetic resonance imaging (fMRI) in humans to explore the neural codes of these navigationally-relevant information while participants viewed images which varied for position and facing direction within a familiar, real-world circular square. We observed neural adaptation for repeated directions in the HC, even if no navigational task was required. Further, we found that the amount of knowledge of the environment interacts with the PPA selectivity in encoding positions: individuals who needed more time to memorize positions in the square during a preliminary training task showed less neural attenuation in this scene-selective region. We also observed adaptation effects, which reflect the real distances between consecutive positions, in scene-selective regions but not in the HC. When examining the multi-voxel patterns of activity we observed that scene-responsive regions and the HC encoded both spatial information and that the RSC classification accuracy for positions was higher in individuals scoring higher to a self-reported questionnaire of spatial abilities. Our findings provide new insight into how the human brain represents a real, large-scale “vista” space, demonstrating the presence of neural codes for position and direction in both scene-selective and hippocampal regions, and revealing the existence, in the former regions, of a map-like spatial representation reflecting real-world distance between consecutive positions

Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments

Copyright @ Springer-Verlag 2010.In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant 60774064, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

An examination and analysis of the Boltzmann machine, its mean field theory approximation, and learning algorithm

It is currently believed that artificial neural network models may form the basis for inte1ligent computational devices. The Boltzmann Machine belongs to the class of recursive artificial neural networks and uses a supervised learning algorithm to learn the mapping between input vectors and desired outputs. This study examines the parameters that influence the performance of the Boltzmann Machine learning algorithm. Improving the performance of the algorithm through the use of a naïve mean field theory approximation is also examined. The study was initiated to examine the hypothesis that the Boltzmann Machine learning algorithm, when used with the mean field approximation, is an efficient, reliable, and flexible model of machine learning. An empirical analysis of the performance of the algorithm supports this hypothesis. The performance of the algorithm is investigated by applying it to training the Boltzmann Machine, and its mean field approximation, the exclusive-Or function. Simulation results suggest that the mean field theory approximation learns faster than the Boltzmann Machine, and shows better stability. The size of the network and the learning rate were found to have considerable impact upon the performance of the algorithm, especially in the case of the mean field theory approximation. A comparison is made with the feed forward back propagation paradigm and it is found that the back propagation network learns the exclusive-Or function eight times faster than the mean field approximation. However, the mean field approximation demonstrated better reliability and stability. Because the mean field approximation is local and asynchronous it has an advantage over back propagation with regard to a parallel implementation. The mean field approximation is domain independent and structurally flexible. These features make the network suitable for use with a structural adaption algorithm, allowing the network to modify its architecture in response to the external environment

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

The ixiQuarks: merging code and GUI in one creative space

This paper reports on ixiQuarks; an environment of instruments and effects that is built on top of the audio programming language SuperCollider. The rationale of these instruments is to explore alternative ways of designing musical interaction in screen-based software, and investigate how semiotics in interface design affects the musical output. The ixiQuarks are part of external libraries available to SuperCollider through the Quarks system. They are software instruments based on a non- realist design ideology that rejects the simulation of acoustic instruments or music hardware and focuses on experimentation at the level of musical interaction. In this environment we try to merge the graphical with the textual in the same instruments, allowing the user to reprogram and change parts of them in runtime. After a short introduction to SuperCollider and the Quark system, we will describe the ixiQuarks and the philosophical basis of their design. We conclude by looking at how they can be seen as epistemic tools that influence the musician in a complex hermeneutic circle of interpretation and signification

Improving Usability of Genetic Algorithms through Self Adaptation on Static and Dynamic Environments

We propose a self-adaptive genetic algorithm, called SAGA, for the purposes of improving the usability of genetic algorithms on both static and dynamic problems. Self-adaption can improve usability by automating some of the parameter tuning for the algorithm, a difficult and time-consuming process on canonical genetic algorithms. Reducing or simplifying the need for parameter tuning will help towards making genetic algorithms a more attractive tool for those who are not experts in the field of evolutionary algorithms, allowing more people to take advantage of the problem solving capabilities of a genetic algorithm on real-world problems. We test SAGA and analyze its the behavior on a variety of problems. First we test on static test problems, where our focus is on usability improvements as measured by the number of parameter configurations to tune and the number of fitness evaluations conducted. On the static problems, SAGA is compared to a canonical genetic algorithm. Next, we test on dynamic test problems, where the fitness landscape varies over the course of the problem\u27s execution. The dynamic problems allows us to examine whether self-adaptation can effectively react to ever-changing and unpredictable problems. On the dynamic problems, we compare to a canonical genetic algorithm as well as other genetic algorithm methods that are designed or utilized specifically for dynamic problems. Finally, we test on a real-world problem pertaining to Medicare Fee-For-Service payments in order to validate the real-world usefulness of SAGA. For this real-world problem, we compare SAGA to both a canonical genetic algorithm and logistic regression, the standard method for this problem in the field of healthcare informatics. We find that this self-adaptive genetic algorithm is successful at improving usability through a large reduction of parameter tuning while maintaining equal or superior results on a majority of the problems tested. The large reduction of parameter tuning translates to large time savings for users of SAGA. Furthermore, self-adaptation proves to be a very capable mechanisms for dealing with the difficulties of dynamic environment problems as observed by the changes to parameters in response to changes in the fitness landscape of the problem