miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Subtractive and divisive adaptation in visual motion computations

Models of visual motion processing that introduce priors for low speed through Bayesian computations are sometimes treated with scepticism by empirical researchers because of the convenient way in which parameters of the Bayesian priors have been chosen. Using the effects of motion adaptation on motion perception to illustrate, we show that the Bayesian prior, far from being convenient, may be estimated on-line and therefore represents a useful tool by which visual motion processes may be optimized in order to extract the motion signals commonly encountered in every day experience. The prescription for optimization, when combined with system constraints on the transmission of visual information, may lead to an exaggeration of perceptual bias through the process of adaptation. Our approach extends the Bayesian model of visual motion proposed byWeiss et al. [Weiss Y., Simoncelli, E., & Adelson, E. (2002). Motion illusions as optimal perception Nature Neuroscience, 5:598-604.], in suggesting that perceptual bias reflects a compromise taken by a rational system in the face of uncertain signals and system constraints. © 2007.

Visual signs and symptoms of Parkinson's disease

Parkinson's disease (PD) is a common disorder of middle-aged and elderly people, in which there is degeneration of the extra-pyramidal motor system. In some patients, the disease is associated with a range of visual signs and symptoms, including defects in visual acuity, colour vision, the blink reflex, pupil reactivity, saccadic and smooth pursuit movements and visual evoked potentials. In addition, there may be psychophysical changes, disturbances of complex visual functions such as visuospatial orientation and facial recognition, and chronic visual hallucinations. Some of the treatments associated with PD may have adverse ocular reactions. If visual problems are present, they can have an important effect on overall motor function, and quality of life of patients can be improved by accurate diagnosis and correction of such defects. Moreover, visual testing is useful in separating PD from other movement disorders with visual symptoms, such as dementia with Lewy bodies (DLB), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Although not central to PD, visual signs and symptoms can be an important though obscure aspect of the disease and should not be overlooked.